diff --git a/micom_2023.ipynb b/micom_2023.ipynb new file mode 100644 index 0000000..73d7e12 --- /dev/null +++ b/micom_2023.ipynb @@ -0,0 +1,8614 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C0vqP4LJ9y6K" + }, + "source": [ + "# 🧫🦠 Modeling microbiota-wide metabolism with MICOM\n", + "\n", + "This notebook will accompany the second session of the 2023 ISB Microbiome Course. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/micom).\n", + "\n", + "You can save your own local copy of this notebook by using `File > Save a copy in Drive`. You may be promted to cetify the notebook is safe. We promise that it is 🀞\n", + "\n", + "**Disclaimer:**\n", + "The linear and quadratic programming problems MICOM has to solve are very large and very complicated. There are some very good commercial solvers that are very expensive (even though they are often free for academic use). To make this tutorial as accessible as possible we will use the Open Source solver [OSQP](https://osqp.org/), which is installed along with MICOM. OSQP is amazing with quadratic programming problems (kudos!) but not as accurate for linear problems. Solvers usually only guarantee a solution within a certain numerical tolerance of the real solution. In order to make everything work with OSQP this tolerance has to be relaxed to about 10-3. This means that any result with an absolute value smaller than that might very well be zero so we should look at larger values only. Installing cost-free academic versions of commercial solvers like [IBM CPLEX](https://www.ibm.com/analytics/cplex-optimizer) or [Gurobi](https://www.gurobi.com/) would alow you to lower the tolerance to 10-6.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qgBBl4GtuTuX" + }, + "source": [ + "# πŸ“ Setup\n", + "\n", + "MICOM installation is is usually pretty straight-forward and can be as easy as typing `pip install micom` into your Terminal.\n", + "\n", + "First let's start by downloading the materials again and switching to the folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ckON4xr3_bW5" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/gibbons-lab/isb_course_2023 materials\n", + "!cd materials\n", + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "is6fmBUeorwv" + }, + "source": [ + "## Basic Installation\n", + "\n", + "Installing MICOM is straight-forward in Python. OSQP itself will be installed automatically along with it." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M_TeC5yrst3h", + "outputId": "c3cb59af-6caf-4eda-f0f4-b20f4d19ae49" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m832.5/832.5 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m31.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.4/37.4 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.3/138.3 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m71.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.9/112.9 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m49.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m526.7/526.7 kB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDone! πŸŽ‰ \n" + ] + } + ], + "source": [ + "!pip install -q micom\n", + "\n", + "print(\"Done! πŸŽ‰ \")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6oJrxxz6tV9T" + }, + "source": [ + "## Enable QIIME 2 interactions\n", + "\n", + "Before we start, we also need to install packages to read the \"biom\" file format used by QIIME 2 to save tables. This is only necessary if you want to read QIIME 2 FeatureTable artifacts (like the ones we constructed yesterday)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5rZX7SK_toLp", + "outputId": "9cc5af18-c4de-486e-cb03-8f01bacf92a5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m41.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDone! πŸŽ‰ \n" + ] + } + ], + "source": [ + "!pip install -q numpy Cython biom-format\n", + "\n", + "print(\"Done! πŸŽ‰ \")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oPy1f-WLI0lZ" + }, + "source": [ + "Okay, all done. So let's get started building some models πŸ¦ΊπŸ› d😁.\n", + "\n", + "# πŸ’» MICOM\n", + "\n", + "We will use the Python interface to MICOM since it plays nicely with Colaboratory. However, you could run the same steps within the QIIME 2 MICOM plugin ([q2-micom](https://library.qiime2.org/plugins/q2-micom/26/)).\n", + "\n", + "Here is an overview of all the steps and functions across both interfaces:\n", + "![micom overview](https://github.com/micom-dev/q2-micom/raw/706f583a060b91c12c0cec7acea2354fdd0dd320/docs/assets/overview.png)\n", + "\n", + "The process of building a metabolic model in MICOM begins with constructing a combined abundance/taxonomy table, referred to hereafter as a taxonomy table. Let's load a sample taxonomy table to see what it looks like:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "UV9SObSQkSZh", + "outputId": "2b3f71ac-8873-45fb-9352-961177f76c6a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id genus species reactions \\\n", + "0 Escherichia_coli_1 Escherichia Escherichia coli 0 95 \n", + "1 Escherichia_coli_2 Escherichia Escherichia coli 1 95 \n", + "2 Escherichia_coli_3 Escherichia Escherichia coli 2 95 \n", + "3 Escherichia_coli_4 Escherichia Escherichia coli 3 95 \n", + "0 Escherichia_coli_1 Escherichia Escherichia coli 0 95 \n", + "\n", + " metabolites sample_id abundance \n", + "0 72 sample_1 96 \n", + "1 72 sample_1 48 \n", + "2 72 sample_1 708 \n", + "3 72 sample_1 622 \n", + "0 72 sample_2 120 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idgenusspeciesreactionsmetabolitessample_idabundance
0Escherichia_coli_1EscherichiaEscherichia coli 09572sample_196
1Escherichia_coli_2EscherichiaEscherichia coli 19572sample_148
2Escherichia_coli_3EscherichiaEscherichia coli 29572sample_1708
3Escherichia_coli_4EscherichiaEscherichia coli 39572sample_1622
0Escherichia_coli_1EscherichiaEscherichia coli 09572sample_2120
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ], + "source": [ + "from micom.data import test_data\n", + "\n", + "test_data().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEk7yfd1lbYp" + }, + "source": [ + "In this taxonomy table, we see four identical strains of _E. coli_ (1 through 4), across two samples (sample_1 and sample_2). We can see that each row represents a single taxon in a single sample, and the `abundance` column identifies the abundance of that taxon in the sample.\n", + "\n", + "The `id` column specifies identifiers for the taxa and should be expressive and not include spaces or special characters. Since we are using a taxonomy database to build our models (more on that soon), we don't need a `file` column.\n", + "\n", + "You might notice that this dataframe looks very different from what we generated in yesterday's tutorial, where we ended up with separate QIIME 2 artifacts 😱\n", + "\n", + "No worries, we can deal with that.\n", + "\n", + "## Importing data from QIIME 2\n", + "\n", + "MICOM can read QIIME 2 artifacts. You don't even need to have QIIME 2 installed for that! But before we do so, let's resolve one issue. We discussed that MICOM summarizes genome-scale models into pangenome-scale models as a first step, but our data are on the ASV level...so how will we know what to summarize?\n", + "\n", + "Basically, a specific model database can be used to quickly summarize pangenome-scale models for use within MICOM. So, before we read our data we have to decide which model database to use. We will go with the [AGORA database](https://pubmed.ncbi.nlm.nih.gov/27893703/), which is a curated database of more than 800 bacterial strains that commonly live in the human gut. In particular, we will use a version of this database summarized on the genus rank which can be downloaded from the [MICOM data repository](https://doi.org/10.5281/zenodo.3755182), which contains a whole lot of prebuilt databases. This database is available from the materials folder that we previously cloned." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_57iya0D3L6-" + }, + "source": [ + "Now we're all set to start building models! The data we previously collected can be found in the `treasure_chest` folder, so we can use those files to build our taxonomy for MICOM." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "o0vBAiiqqPLC" + }, + "outputs": [], + "source": [ + "from micom.taxonomy import qiime_to_micom\n", + "\n", + "tax = qiime_to_micom(\n", + " \"dada2/table.qza\",\n", + " \"taxa.qza\",\n", + " collapse_on=\"genus\"\n", + ")" + ] + }, + { + "cell_type": "code", + "source": [ + "tax.sample_id.unique()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TkOm89mQbwXa", + "outputId": "a6093f5e-a2c8-4bf3-9e3f-bb925b81bf98" + }, + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['ERR1883195', 'ERR1883207', 'ERR1883214', 'ERR1883240',\n", + " 'ERR1883250', 'ERR1883212', 'ERR1883225', 'ERR1883294'],\n", + " dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 53 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-TQ6Zp7wouk4" + }, + "source": [ + "Notice the `collapse_on` argument. That will specify the rank on which to sumarize and can be a list of several ranks. When matching taxonomy you can either match by the particular rank of interest (for example, just comparing genus names here), or you could compare the entire taxonomy, which will require all taxonomic ranks prior to the target rank to match. For that you cloud specify `collapse_on=[\"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\"]`.\n", + "\n", + "Taxonomic names will often not match 100% between databases. For instance, the genus name \"Prevotella\" in one database may be \"Prevotella_6\" in another. The more ranks you use for matching the more likely you are to run into these issues. However, the more taxonomic ranks you use to match the more confident you can be that your observed taxon really is the same taxon as the one in the model database.\n", + "\n", + "The resulting table will contain the same abundances but it will include more ranks if `collapse_on` is a list. All ranks present in the taxonomy will be used when matching to the database. We will stick with the \"lax\" option of only matching on genus ranks.\n", + "\n", + "Let's now take a look at the taxonomy table we generated:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R5zwLVFcNTbq" + }, + "source": [ + "That looks more like the example! Again, we have a row for each taxon in each sample, so we're good to go.\n", + "\n", + "One helpful thing to do is to merge in our metadata, so we'll have it at hand for the following steps. In our case, the metadata will include the ethnic group, region, and subsistence type of each of the study participants." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + }, + "id": "X9hqoO4go0h1", + "outputId": "6d3ff6a9-7c37-4c52-ff4f-5ae39aef4cd7", + "collapsed": true + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " sample_id abundance genus id relative \\\n", + "0 ERR1883195 6.0 Akkermansia Akkermansia 0.000116 \n", + "1 ERR1883195 17971.0 Phocaeicola Phocaeicola 0.346522 \n", + "2 ERR1883195 5.0 Veillonella Veillonella 0.000096 \n", + "3 ERR1883195 4741.0 Faecalibacterium Faecalibacterium 0.091417 \n", + "4 ERR1883195 576.0 Mediterraneibacter Mediterraneibacter 0.011107 \n", + ".. ... ... ... ... ... \n", + "344 ERR1883294 5.0 Stomatobaculum Stomatobaculum 0.000926 \n", + "345 ERR1883294 3.0 Lancefieldella Lancefieldella 0.000556 \n", + "346 ERR1883294 3.0 Tumebacillus Tumebacillus 0.000556 \n", + "347 ERR1883294 3.0 Tropheryma Tropheryma 0.000556 \n", + "348 ERR1883294 2.0 Mogibacterium Mogibacterium 0.000370 \n", + "\n", + " Unnamed: 0 collection_timestamp day_relative_to_fmt description \\\n", + "0 12 2011-10-24 26 Donor 11 \n", + "1 12 2011-10-24 26 Donor 11 \n", + "2 12 2011-10-24 26 Donor 11 \n", + "3 12 2011-10-24 26 Donor 11 \n", + "4 12 2011-10-24 26 Donor 11 \n", + ".. ... ... ... ... \n", + "344 112 2011-09-29 0 Day 0 CD3 \n", + "345 112 2011-09-29 0 Day 0 CD3 \n", + "346 112 2011-09-29 0 Day 0 CD3 \n", + "347 112 2011-09-29 0 Day 0 CD3 \n", + "348 112 2011-09-29 0 Day 0 CD3 \n", + "\n", + " disease_state host_age host_age_units host_body_mass_index \\\n", + "0 healthy Restricted access years Restricted access \n", + "1 healthy Restricted access years Restricted access \n", + "2 healthy Restricted access years Restricted access \n", + "3 healthy Restricted access years Restricted access \n", + "4 healthy Restricted access years Restricted access \n", + ".. ... ... ... ... \n", + "344 Pre-FMT 61 years 32.5 \n", + "345 Pre-FMT 61 years 32.5 \n", + "346 Pre-FMT 61 years 32.5 \n", + "347 Pre-FMT 61 years 32.5 \n", + "348 Pre-FMT 61 years 32.5 \n", + "\n", + " host_height host_height_units host_subject_id host_weight \\\n", + "0 Restricted access m Donor Restricted access \n", + "1 Restricted access m Donor Restricted access \n", + "2 Restricted access m Donor Restricted access \n", + "3 Restricted access m Donor Restricted access \n", + "4 Restricted access m Donor Restricted access \n", + ".. ... ... ... ... \n", + "344 1.727 m CD3 97.3 \n", + "345 1.727 m CD3 97.3 \n", + "346 1.727 m CD3 97.3 \n", + "347 1.727 m CD3 97.3 \n", + "348 1.727 m CD3 97.3 \n", + "\n", + " host_weight_units race sex \n", + "0 kg Restricted access Restricted access \n", + "1 kg Restricted access Restricted access \n", + "2 kg Restricted access Restricted access \n", + "3 kg Restricted access Restricted access \n", + "4 kg Restricted access Restricted access \n", + ".. ... ... ... \n", + "344 kg white male \n", + "345 kg white male \n", + "346 kg white male \n", + "347 kg white male \n", + "348 kg white male \n", + "\n", + "[349 rows x 20 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sample_idabundancegenusidrelativeUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
0ERR18831956.0AkkermansiaAkkermansia0.000116122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
1ERR188319517971.0PhocaeicolaPhocaeicola0.346522122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
2ERR18831955.0VeillonellaVeillonella0.000096122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
3ERR18831954741.0FaecalibacteriumFaecalibacterium0.091417122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
4ERR1883195576.0MediterraneibacterMediterraneibacter0.011107122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
...............................................................
344ERR18832945.0StomatobaculumStomatobaculum0.0009261122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
345ERR18832943.0LancefieldellaLancefieldella0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
346ERR18832943.0TumebacillusTumebacillus0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
347ERR18832943.0TropherymaTropheryma0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
348ERR18832942.0MogibacteriumMogibacterium0.0003701122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
\n", + "

349 rows Γ— 20 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ] + }, + "metadata": {}, + "execution_count": 55 + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "metadata = pd.read_table(\"metadata.tsv\").rename(columns={\"id\": \"sample_id\"})\n", + "tax = pd.merge(tax, metadata, on=\"sample_id\")\n", + "tax" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Ok, now we want to invade our samples with C. diff. The goal is to predict susceptibility to invasion and see how disease context can influence predicted engraftment. To do this we will introduce 10% C. diff to all the samples! You can read more about this approach and its applications [here](https://www.biorxiv.org/content/10.1101/2023.04.28.538771v2) " + ], + "metadata": { + "id": "ZsFgo_CRjvsr" + } + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "vHyvSZJJ-iIi", + "outputId": "0f934995-8bd5-4c03-ddda-491fb5d5bd4f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " sample_id abundance genus id \\\n", + "1 ERR1883195 17971.000000 Phocaeicola Phocaeicola \n", + "3 ERR1883195 4741.000000 Faecalibacterium Faecalibacterium \n", + "8 ERR1883195 6805.000000 Bacteroides Bacteroides \n", + "11 ERR1883195 3076.000000 Parabacteroides Parabacteroides \n", + "5 ERR1883195 3621.444444 Clostridioides Clostridioides \n", + "71 ERR1883207 16983.000000 Phocaeicola Phocaeicola \n", + "73 ERR1883207 3717.000000 Faecalibacterium Faecalibacterium \n", + "77 ERR1883207 2956.000000 Barnesiella Barnesiella \n", + "78 ERR1883207 6984.000000 Bacteroides Bacteroides \n", + "79 ERR1883207 2901.000000 Blautia Blautia \n", + "81 ERR1883207 3413.000000 Parabacteroides Parabacteroides \n", + "7 ERR1883207 4106.000000 Clostridioides Clostridioides \n", + "222 ERR1883212 8393.000000 Phocaeicola Phocaeicola \n", + "224 ERR1883212 10841.000000 Faecalibacterium Faecalibacterium \n", + "227 ERR1883212 4038.000000 Fusicatenibacter Fusicatenibacter \n", + "229 ERR1883212 3286.000000 Bacteroides Bacteroides \n", + "230 ERR1883212 3198.000000 Blautia Blautia \n", + "234 ERR1883212 5106.000000 Streptococcus Streptococcus \n", + "7 ERR1883212 3873.555556 Clostridioides Clostridioides \n", + "152 ERR1883214 54726.000000 Akkermansia Akkermansia \n", + "161 ERR1883214 3968.000000 Clostridium Clostridium \n", + "3 ERR1883214 6521.555556 Clostridioides Clostridioides \n", + "291 ERR1883225 3734.000000 Phocaeicola Phocaeicola \n", + "295 ERR1883225 492.000000 Barnesiella Barnesiella \n", + "296 ERR1883225 1475.000000 Bacteroides Bacteroides \n", + "299 ERR1883225 911.000000 Parabacteroides Parabacteroides \n", + "5 ERR1883225 734.666667 Clostridioides Clostridioides \n", + "174 ERR1883240 35554.000000 Akkermansia Akkermansia \n", + "176 ERR1883240 15517.000000 Veillonella Veillonella \n", + "180 ERR1883240 5555.000000 Fusobacterium Fusobacterium \n", + "4 ERR1883240 6291.777778 Clostridioides Clostridioides \n", + "188 ERR1883250 11467.000000 Akkermansia Akkermansia \n", + "190 ERR1883250 9955.000000 Mediterraneibacter Mediterraneibacter \n", + "191 ERR1883250 11923.000000 Erysipelatoclostridium Erysipelatoclostridium \n", + "193 ERR1883250 13975.000000 Blautia Blautia \n", + "196 ERR1883250 3282.000000 Ruthenibacterium Ruthenibacterium \n", + "6 ERR1883250 5622.444444 Clostridioides Clostridioides \n", + "321 ERR1883294 619.000000 Veillonella Veillonella \n", + "325 ERR1883294 591.000000 Fusobacterium Fusobacterium \n", + "329 ERR1883294 1866.000000 Anaerosinus Anaerosinus \n", + "330 ERR1883294 580.000000 Megasphaera Megasphaera \n", + "332 ERR1883294 734.000000 Phytobacter Phytobacter \n", + "333 ERR1883294 294.000000 Morganella Morganella \n", + "7 ERR1883294 520.444444 Clostridioides Clostridioides \n", + "\n", + " relative Unnamed: 0 collection_timestamp day_relative_to_fmt \\\n", + "1 0.496238 12.0 2011-10-24 26 \n", + "3 0.130915 12.0 2011-10-24 26 \n", + "8 0.187908 12.0 2011-10-24 26 \n", + "11 0.084938 12.0 2011-10-24 26 \n", + "5 0.100000 12.0 2011-10-24 26 \n", + "71 0.413614 24.0 2012-01-12 44 \n", + "73 0.090526 24.0 2012-01-12 44 \n", + "77 0.071992 24.0 2012-01-12 44 \n", + "78 0.170093 24.0 2012-01-12 44 \n", + "79 0.070653 24.0 2012-01-12 44 \n", + "81 0.083122 24.0 2012-01-12 44 \n", + "7 0.100000 24.0 2012-01-12 44 \n", + "222 0.216674 30.0 2012-10-10 135 \n", + "224 0.279872 30.0 2012-10-10 135 \n", + "227 0.104245 30.0 2012-10-10 135 \n", + "229 0.084832 30.0 2012-10-10 135 \n", + "230 0.082560 30.0 2012-10-10 135 \n", + "234 0.131817 30.0 2012-10-10 135 \n", + "7 0.100000 30.0 2012-10-10 135 \n", + "152 0.839156 32.0 2011-07-26 0 \n", + "161 0.060844 32.0 2011-07-26 0 \n", + "3 0.100000 32.0 2011-07-26 0 \n", + "291 0.508258 43.0 2011-07-26 54 \n", + "295 0.066969 43.0 2011-07-26 54 \n", + "296 0.200771 43.0 2011-07-26 54 \n", + "299 0.124002 43.0 2011-07-26 54 \n", + "5 0.100000 43.0 2011-07-26 54 \n", + "174 0.565087 58.0 2012-02-14 pre-FMT \n", + "176 0.246623 58.0 2012-02-14 pre-FMT \n", + "180 0.088290 58.0 2012-02-14 pre-FMT \n", + "4 0.100000 58.0 2012-02-14 pre-FMT \n", + "188 0.203950 68.0 2011-12-23 pre-FMT \n", + "190 0.177058 68.0 2011-12-23 pre-FMT \n", + "191 0.212061 68.0 2011-12-23 pre-FMT \n", + "193 0.248557 68.0 2011-12-23 pre-FMT \n", + "196 0.058373 68.0 2011-12-23 pre-FMT \n", + "6 0.100000 68.0 2011-12-23 pre-FMT \n", + "321 0.118937 112.0 2011-09-29 0 \n", + "325 0.113557 112.0 2011-09-29 0 \n", + "329 0.358540 112.0 2011-09-29 0 \n", + "330 0.111443 112.0 2011-09-29 0 \n", + "332 0.141033 112.0 2011-09-29 0 \n", + "333 0.056490 112.0 2011-09-29 0 \n", + "7 0.100000 112.0 2011-09-29 0 \n", + "\n", + " description disease_state host_age host_age_units \\\n", + "1 Donor 11 healthy Restricted access years \n", + "3 Donor 11 healthy Restricted access years \n", + "8 Donor 11 healthy Restricted access years \n", + "11 Donor 11 healthy Restricted access years \n", + "5 Donor 11 healthy Restricted access years \n", + "71 Donor 12 healthy Restricted access years \n", + "73 Donor 12 healthy Restricted access years \n", + "77 Donor 12 healthy Restricted access years \n", + "78 Donor 12 healthy Restricted access years \n", + "79 Donor 12 healthy Restricted access years \n", + "81 Donor 12 healthy Restricted access years \n", + "7 Donor 12 healthy Restricted access years \n", + "222 Donor 14 healthy Restricted access years \n", + "224 Donor 14 healthy Restricted access years \n", + "227 Donor 14 healthy Restricted access years \n", + "229 Donor 14 healthy Restricted access years \n", + "230 Donor 14 healthy Restricted access years \n", + "234 Donor 14 healthy Restricted access years \n", + "7 Donor 14 healthy Restricted access years \n", + "152 Day 0 CD1 Pre-FMT 39 years \n", + "161 Day 0 CD1 Pre-FMT 39 years \n", + "3 Day 0 CD1 Pre-FMT 39 years \n", + "291 Donor CD1 healthy Restricted access years \n", + "295 Donor CD1 healthy Restricted access years \n", + "296 Donor CD1 healthy Restricted access years \n", + "299 Donor CD1 healthy Restricted access years \n", + "5 Donor CD1 healthy Restricted access years \n", + "174 CD9 pre-FMT Pre-FMT 47 years \n", + "176 CD9 pre-FMT Pre-FMT 47 years \n", + "180 CD9 pre-FMT Pre-FMT 47 years \n", + "4 CD9 pre-FMT Pre-FMT 47 years \n", + "188 CD13 pre-FMT Pre-FMT 53 years \n", + "190 CD13 pre-FMT Pre-FMT 53 years \n", + "191 CD13 pre-FMT Pre-FMT 53 years \n", + "193 CD13 pre-FMT Pre-FMT 53 years \n", + "196 CD13 pre-FMT Pre-FMT 53 years \n", + "6 CD13 pre-FMT Pre-FMT 53 years \n", + "321 Day 0 CD3 Pre-FMT 61 years \n", + "325 Day 0 CD3 Pre-FMT 61 years \n", + "329 Day 0 CD3 Pre-FMT 61 years \n", + "330 Day 0 CD3 Pre-FMT 61 years \n", + "332 Day 0 CD3 Pre-FMT 61 years \n", + "333 Day 0 CD3 Pre-FMT 61 years \n", + "7 Day 0 CD3 Pre-FMT 61 years \n", + "\n", + " host_body_mass_index host_height host_height_units host_subject_id \\\n", + "1 Restricted access Restricted access m Donor \n", + "3 Restricted access Restricted access m Donor \n", + "8 Restricted access Restricted access m Donor \n", + "11 Restricted access Restricted access m Donor \n", + "5 Restricted access Restricted access m Donor \n", + "71 Restricted access Restricted access m Donor \n", + "73 Restricted access Restricted access m Donor \n", + "77 Restricted access Restricted access m Donor \n", + "78 Restricted access Restricted access m Donor \n", + "79 Restricted access Restricted access m Donor \n", + "81 Restricted access Restricted access m Donor \n", + "7 Restricted access Restricted access m Donor \n", + "222 Restricted access Restricted access m Donor \n", + "224 Restricted access Restricted access m Donor \n", + "227 Restricted access Restricted access m Donor \n", + "229 Restricted access Restricted access m Donor \n", + "230 Restricted access Restricted access m Donor \n", + "234 Restricted access Restricted access m Donor \n", + "7 Restricted access Restricted access m Donor \n", + "152 29.3 165.1 m CD1 \n", + "161 29.3 165.1 m CD1 \n", + "3 29.3 165.1 m CD1 \n", + "291 Restricted access Restricted access m Donor \n", + "295 Restricted access Restricted access m Donor \n", + "296 Restricted access Restricted access m Donor \n", + "299 Restricted access Restricted access m Donor \n", + "5 Restricted access Restricted access m Donor \n", + "174 35.5 1.55 m CD9 \n", + "176 35.5 1.55 m CD9 \n", + "180 35.5 1.55 m CD9 \n", + "4 35.5 1.55 m CD9 \n", + "188 34.4 1.56 m CD13 \n", + "190 34.4 1.56 m CD13 \n", + "191 34.4 1.56 m CD13 \n", + "193 34.4 1.56 m CD13 \n", + "196 34.4 1.56 m CD13 \n", + "6 34.4 1.56 m CD13 \n", + "321 32.5 1.727 m CD3 \n", + "325 32.5 1.727 m CD3 \n", + "329 32.5 1.727 m CD3 \n", + "330 32.5 1.727 m CD3 \n", + "332 32.5 1.727 m CD3 \n", + "333 32.5 1.727 m CD3 \n", + "7 32.5 1.727 m CD3 \n", + "\n", + " host_weight host_weight_units race sex \n", + "1 Restricted access kg Restricted access Restricted access \n", + "3 Restricted access kg Restricted access Restricted access \n", + "8 Restricted access kg Restricted access Restricted access \n", + "11 Restricted access kg Restricted access Restricted access \n", + "5 Restricted access kg Restricted access Restricted access \n", + "71 Restricted access kg Restricted access Restricted access \n", + "73 Restricted access kg Restricted access Restricted access \n", + "77 Restricted access kg Restricted access Restricted access \n", + "78 Restricted access kg Restricted access Restricted access \n", + "79 Restricted access kg Restricted access Restricted access \n", + "81 Restricted access kg Restricted access Restricted access \n", + "7 Restricted access kg Restricted access Restricted access \n", + "222 Restricted access kg Restricted access Restricted access \n", + "224 Restricted access kg Restricted access Restricted access \n", + "227 Restricted access kg Restricted access Restricted access \n", + "229 Restricted access kg Restricted access Restricted access \n", + "230 Restricted access kg Restricted access Restricted access \n", + "234 Restricted access kg Restricted access Restricted access \n", + "7 Restricted access kg Restricted access Restricted access \n", + "152 80.1 kg white female \n", + "161 80.1 kg white female \n", + "3 80.1 kg white female \n", + "291 Restricted access kg Restricted access Restricted access \n", + "295 Restricted access kg Restricted access Restricted access \n", + "296 Restricted access kg Restricted access Restricted access \n", + "299 Restricted access kg Restricted access Restricted access \n", + "5 Restricted access kg Restricted access Restricted access \n", + "174 85.1 kg white female \n", + "176 85.1 kg white female \n", + "180 85.1 kg white female \n", + "4 85.1 kg white female \n", + "188 83.9 kg white female \n", + "190 83.9 kg white female \n", + "191 83.9 kg white female \n", + "193 83.9 kg white female \n", + "196 83.9 kg white female \n", + "6 83.9 kg white female \n", + "321 97.3 kg white male \n", + "325 97.3 kg white male \n", + "329 97.3 kg white male \n", + "330 97.3 kg white male \n", + "332 97.3 kg white male \n", + "333 97.3 kg white male \n", + "7 97.3 kg white male " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sample_idabundancegenusidrelativeUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
1ERR188319517971.000000PhocaeicolaPhocaeicola0.49623812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
3ERR18831954741.000000FaecalibacteriumFaecalibacterium0.13091512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
8ERR18831956805.000000BacteroidesBacteroides0.18790812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
11ERR18831953076.000000ParabacteroidesParabacteroides0.08493812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
5ERR18831953621.444444ClostridioidesClostridioides0.10000012.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
71ERR188320716983.000000PhocaeicolaPhocaeicola0.41361424.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
73ERR18832073717.000000FaecalibacteriumFaecalibacterium0.09052624.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
77ERR18832072956.000000BarnesiellaBarnesiella0.07199224.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
78ERR18832076984.000000BacteroidesBacteroides0.17009324.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
79ERR18832072901.000000BlautiaBlautia0.07065324.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
81ERR18832073413.000000ParabacteroidesParabacteroides0.08312224.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
7ERR18832074106.000000ClostridioidesClostridioides0.10000024.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
222ERR18832128393.000000PhocaeicolaPhocaeicola0.21667430.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
224ERR188321210841.000000FaecalibacteriumFaecalibacterium0.27987230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
227ERR18832124038.000000FusicatenibacterFusicatenibacter0.10424530.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
229ERR18832123286.000000BacteroidesBacteroides0.08483230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
230ERR18832123198.000000BlautiaBlautia0.08256030.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
234ERR18832125106.000000StreptococcusStreptococcus0.13181730.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
7ERR18832123873.555556ClostridioidesClostridioides0.10000030.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
152ERR188321454726.000000AkkermansiaAkkermansia0.83915632.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
161ERR18832143968.000000ClostridiumClostridium0.06084432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
3ERR18832146521.555556ClostridioidesClostridioides0.10000032.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
291ERR18832253734.000000PhocaeicolaPhocaeicola0.50825843.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
295ERR1883225492.000000BarnesiellaBarnesiella0.06696943.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
296ERR18832251475.000000BacteroidesBacteroides0.20077143.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
299ERR1883225911.000000ParabacteroidesParabacteroides0.12400243.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
5ERR1883225734.666667ClostridioidesClostridioides0.10000043.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
174ERR188324035554.000000AkkermansiaAkkermansia0.56508758.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
176ERR188324015517.000000VeillonellaVeillonella0.24662358.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
180ERR18832405555.000000FusobacteriumFusobacterium0.08829058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
4ERR18832406291.777778ClostridioidesClostridioides0.10000058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
188ERR188325011467.000000AkkermansiaAkkermansia0.20395068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
190ERR18832509955.000000MediterraneibacterMediterraneibacter0.17705868.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
191ERR188325011923.000000ErysipelatoclostridiumErysipelatoclostridium0.21206168.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
193ERR188325013975.000000BlautiaBlautia0.24855768.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
196ERR18832503282.000000RuthenibacteriumRuthenibacterium0.05837368.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
6ERR18832505622.444444ClostridioidesClostridioides0.10000068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
321ERR1883294619.000000VeillonellaVeillonella0.118937112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
325ERR1883294591.000000FusobacteriumFusobacterium0.113557112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
329ERR18832941866.000000AnaerosinusAnaerosinus0.358540112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
330ERR1883294580.000000MegasphaeraMegasphaera0.111443112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
332ERR1883294734.000000PhytobacterPhytobacter0.141033112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
333ERR1883294294.000000MorganellaMorganella0.056490112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
7ERR1883294520.444444ClostridioidesClostridioides0.100000112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ] + }, + "metadata": {}, + "execution_count": 106 + } + ], + "source": [ + "invader='Clostridioides' # choose your favorite invader, here we have chosen C. diff\n", + "invader_rel=0.1 # set the fraction of total relative abundance you want it to invade with\n", + "invaded=pd.DataFrame() # set up results dataframe\n", + "\n", + "for smp,df in tax.groupby(by='sample_id'): # loop through data, one sample at a time\n", + " df=df[df.relative>0.05].copy() # filter out genera below 5% (this is a high threshold, which will make simulation run faster)\n", + " df=df[df.genus!='Clostridioides'] # some samples already have C. diff, so lets remove it and then re-introduce\n", + " abund=df.abundance.sum()*invader_rel/(1-invader_rel) # calculate the abundance needed to achieve desired relative abundance\n", + "\n", + " info=df.iloc[0,:].copy() # get necessary sample info\n", + " info.genus=invader # add invader name\n", + " info.id=invader\n", + " info.abundance=abund # add invader abundance\n", + " df.loc[df.shape[0]+1,info.index]=info.values #append invader info\n", + " df.relative=df.abundance.apply(lambda x:x/df.abundance.sum()) # re-calculate relative abundance\n", + " invaded=pd.concat([invaded,df]) # append results to output dataframe\n", + "invaded\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RTbYBR8cJfup" + }, + "source": [ + "With our taxonomy table ready to go, and our metadata merged, its finally time to get to the model building! πŸŽ‰\n", + "\n", + "## Building community models\n", + "\n", + "With the data we have now, building our models is pretty easy. We just pass our taxonomy table and model database to MICOM. We will remove all taxa that make up less than 5% of the community to keep the models small and speed up this tutorial. We will also have to specify where to write the models. For simplicity, we'll run this process in parallel over two threads. It should take around 10 minutes to finish." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51, + "referenced_widgets": [ + "37b0134de4bd4fe38c1afe7bc2bbdc59", + "bc2776a5195f4a8c8dda0e22e2fd9ea3" + ] + }, + "id": "kDbSN71SmCZr", + "outputId": "e51c2208-4536-4f90-804c-442d884549bf" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Output()" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "37b0134de4bd4fe38c1afe7bc2bbdc59" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:micom.logger:Less than 50% of the abundance could be matched to the model database. Model `ERR1883294` may not be representative of the sample\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [], + "text/html": [ + "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from micom.workflows import build\n",
+        "from micom import Community\n",
+        "import pandas as pd\n",
+        "\n",
+        "manifest = build(invaded, \"agora103_genus.qza\", \"models\", solver=\"osqp\",\n",
+        "                 cutoff=0.05, threads=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kwya6vbZZSUo"
+      },
+      "source": [
+        "You'll see a warning pop up indicating that less than 50% of the abundances can be matched to the database for one of the samples. This can happen with some data, and may indicate the models may not be completely representative of the samples.Typically a fraction of 80% or more is considered great. We'll continue, but remember to keep an eye out for this in future projects!\n",
+        "\n",
+        "In lower-biomass 16S amplicon sequencing samples from stool, many reads can match to food components or to host mitochondria and these hits probably do not contribute much to bacterial community metabolism. These hits will be excluded from MICOM.\n",
+        "\n",
+        "Let's take a look what we got back from the `build` process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "r9qwglr88Ise",
+        "outputId": "168ee50d-23c5-4dfe-e3fb-bc733ec82188"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0  ERR1883195        12.0           2011-10-24                  26   \n",
+              "1  ERR1883207        24.0           2012-01-12                  44   \n",
+              "2  ERR1883212        30.0           2012-10-10                 135   \n",
+              "3  ERR1883214        32.0           2011-07-26                   0   \n",
+              "4  ERR1883225        43.0           2011-07-26                  54   \n",
+              "5  ERR1883240        58.0           2012-02-14             pre-FMT   \n",
+              "6  ERR1883250        68.0           2011-12-23             pre-FMT   \n",
+              "7  ERR1883294       112.0           2011-09-29                   0   \n",
+              "\n",
+              "    description disease_state           host_age host_age_units  \\\n",
+              "0      Donor 11       healthy  Restricted access          years   \n",
+              "1      Donor 12       healthy  Restricted access          years   \n",
+              "2      Donor 14       healthy  Restricted access          years   \n",
+              "3     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "4     Donor CD1       healthy  Restricted access          years   \n",
+              "5   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "6  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "7     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "\n",
+              "  host_body_mass_index        host_height  ... host_subject_id  \\\n",
+              "0    Restricted access  Restricted access  ...           Donor   \n",
+              "1    Restricted access  Restricted access  ...           Donor   \n",
+              "2    Restricted access  Restricted access  ...           Donor   \n",
+              "3                 29.3              165.1  ...             CD1   \n",
+              "4    Restricted access  Restricted access  ...           Donor   \n",
+              "5                 35.5               1.55  ...             CD9   \n",
+              "6                 34.4               1.56  ...            CD13   \n",
+              "7                 32.5              1.727  ...             CD3   \n",
+              "\n",
+              "         host_weight host_weight_units               race                sex  \\\n",
+              "0  Restricted access                kg  Restricted access  Restricted access   \n",
+              "1  Restricted access                kg  Restricted access  Restricted access   \n",
+              "2  Restricted access                kg  Restricted access  Restricted access   \n",
+              "3               80.1                kg              white             female   \n",
+              "4  Restricted access                kg  Restricted access  Restricted access   \n",
+              "5               85.1                kg              white             female   \n",
+              "6               83.9                kg              white             female   \n",
+              "7               97.3                kg              white               male   \n",
+              "\n",
+              "                file found_taxa  total_taxa  found_fraction  \\\n",
+              "0  ERR1883195.pickle        5.0         5.0        1.000000   \n",
+              "1  ERR1883207.pickle        7.0         7.0        1.000000   \n",
+              "2  ERR1883212.pickle        6.0         7.0        0.857143   \n",
+              "3  ERR1883214.pickle        6.0         6.0        1.000000   \n",
+              "4  ERR1883225.pickle        5.0         5.0        1.000000   \n",
+              "5  ERR1883240.pickle        4.0         4.0        1.000000   \n",
+              "6  ERR1883250.pickle        5.0         6.0        0.833333   \n",
+              "7  ERR1883294.pickle        5.0         7.0        0.714286   \n",
+              "\n",
+              "   found_abundance_fraction  \n",
+              "0                  1.000000  \n",
+              "1                  1.000000  \n",
+              "2                  0.895755  \n",
+              "3                  1.000000  \n",
+              "4                  1.000000  \n",
+              "5                  1.000000  \n",
+              "6                  0.941627  \n",
+              "7                  0.500427  \n",
+              "\n",
+              "[8 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_height...host_subject_idhost_weighthost_weight_unitsracesexfilefound_taxatotal_taxafound_fractionfound_abundance_fraction
0ERR188319512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883195.pickle5.05.01.0000001.000000
1ERR188320724.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883207.pickle7.07.01.0000001.000000
2ERR188321230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883212.pickle6.07.00.8571430.895755
3ERR188321432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1...CD180.1kgwhitefemaleERR1883214.pickle6.06.01.0000001.000000
4ERR188322543.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883225.pickle5.05.01.0000001.000000
5ERR188324058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55...CD985.1kgwhitefemaleERR1883240.pickle4.04.01.0000001.000000
6ERR188325068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56...CD1383.9kgwhitefemaleERR1883250.pickle5.06.00.8333330.941627
7ERR1883294112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727...CD397.3kgwhitemaleERR1883294.pickle5.07.00.7142860.500427
\n",
+              "
8 rows Γ— 21 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ],
+      "source": [
+        "manifest"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y4KAJkhIdspQ"
+      },
+      "source": [
+        "This will tell you how many taxa were found in the database and what fraction of the total abundance was represented by the database. For most samples, this looks okay (i.e., >70% of abundance represented).\n",
+        "\n",
+        "So we now have our community models and can leverage MICOM fully by simulating community growth - let's discuss what we want to look at.\n",
+        "\n",
+        "### Microbiome Context\n",
+        "\n",
+        "Now that our models are ready to go, let's think about some of the insights we might gain from these samples. First and foremost, we want to investigate the invasion potential of C. diff. How does its ability to invade vary in samples from healthy donors versus individuals with dysbiotic gut microbiomes (pre-FMT)?\n",
+        "\n",
+        "Additionally, we can use MICOM to take a mechanistic look at what metabolic strategies are leveraged by C. diff (e.g., what niche(s) does it occupy) in these different contexts.\n",
+        "\n",
+        "All that and more, coming up. Stay tuned!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "etrvjwBLkKdR"
+      },
+      "source": [
+        "First we need to import our dietary context. For simplicity we will be using a formulation that represents an average western diet, but if information about host diet is known other formulations can be used (e.g., vegetarian or vegan diet). Additional dietary formulations can be found [here]( https://github.com/micom-dev/media/tree/main/media)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "aJwXAR4PPAkA",
+        "outputId": "4397913a-b2b6-4059-e355-4226d29a0a90"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  reaction metabolite      global_id  flux\n",
+              "reaction                                                  \n",
+              "EX_4abz_m        EX_4abz_m     4abz_m     EX_4abz(e)   0.1\n",
+              "EX_4hbz_m        EX_4hbz_m     4hbz_m     EX_4hbz(e)   0.1\n",
+              "EX_ac_m            EX_ac_m       ac_m       EX_ac(e)   0.1\n",
+              "EX_acgam_m      EX_acgam_m    acgam_m    EX_acgam(e)   0.1\n",
+              "EX_ala_L_m      EX_ala_L_m    ala_L_m    EX_ala_L(e)   0.1\n",
+              "...                    ...        ...            ...   ...\n",
+              "EX_octa_m        EX_octa_m     octa_m     EX_octa(e)   0.0\n",
+              "EX_adpcbl_m    EX_adpcbl_m   adpcbl_m   EX_adpcbl(e)   0.0\n",
+              "EX_fe3dcit_m  EX_fe3dcit_m  fe3dcit_m  EX_fe3dcit(e)   0.0\n",
+              "EX_pydx5p_m    EX_pydx5p_m   pydx5p_m   EX_pydx5p(e)   0.0\n",
+              "EX_glu_D_m      EX_glu_D_m    glu_D_m    EX_glu_D(e)   0.0\n",
+              "\n",
+              "[171 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
reactionmetaboliteglobal_idflux
reaction
EX_4abz_mEX_4abz_m4abz_mEX_4abz(e)0.1
EX_4hbz_mEX_4hbz_m4hbz_mEX_4hbz(e)0.1
EX_ac_mEX_ac_mac_mEX_ac(e)0.1
EX_acgam_mEX_acgam_macgam_mEX_acgam(e)0.1
EX_ala_L_mEX_ala_L_mala_L_mEX_ala_L(e)0.1
...............
EX_octa_mEX_octa_mocta_mEX_octa(e)0.0
EX_adpcbl_mEX_adpcbl_madpcbl_mEX_adpcbl(e)0.0
EX_fe3dcit_mEX_fe3dcit_mfe3dcit_mEX_fe3dcit(e)0.0
EX_pydx5p_mEX_pydx5p_mpydx5p_mEX_pydx5p(e)0.0
EX_glu_D_mEX_glu_D_mglu_D_mEX_glu_D(e)0.0
\n",
+              "
171 rows Γ— 4 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ],
+      "source": [
+        "from micom.qiime_formats import load_qiime_medium\n",
+        "medium = load_qiime_medium(\"western_diet_gut_agora.qza\")\n",
+        "medium"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4s8R4WYUez4g"
+      },
+      "source": [
+        "### Growing the models\n",
+        "Great, now we have our media & our models, it's time to get growing. This will take some time, so we'll use that time as an opportunity to discuss more in depth what these processes do, and what to look for in the results. First, let's run the `grow()` command. This will take the models we've built, and find an optimal solution to the fluxes based upon the medium that's been applied."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3WH8VVrVS4mv"
+      },
+      "source": [
+        "If that takes too long or was aborted, we can read it in from the treasure chest."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 100,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34,
+          "referenced_widgets": [
+            "59428de9a88b4204885d03de441cbde3",
+            "901fb7e8d8c844a8b2e86735ca2bead3"
+          ]
+        },
+        "id": "IjDguZEcWGjG",
+        "outputId": "03508c5b-c7fc-46dd-c53b-bbd88dc5c236"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "59428de9a88b4204885d03de441cbde3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from micom.workflows import grow, save_results\n",
+        "\n",
+        "growth = grow(manifest, \"models\",medium, tradeoff=0.8, threads=2)\n",
+        "\n",
+        "# We'll save the results to a file\n",
+        "save_results(growth, \"growth.zip\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rHedHJxHWkjy"
+      },
+      "source": [
+        "Again, if that takes too long or was aborted, we can read it in from the treasure chest."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rcPNBkDpWGrQ"
+      },
+      "outputs": [],
+      "source": [
+        "from micom.workflows import load_results\n",
+        "\n",
+        "try:               # Will only run if the previous step failed\n",
+        "  growth\n",
+        "except NameError:\n",
+        "  growth = load_results(\"treasure_chest/growth.zip\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "69gq9QfAzqxq"
+      },
+      "source": [
+        "What kind of results did we get? Well, `grow` returns a tuple of 3 data sets:\n",
+        "\n",
+        "1. The predicted growth rate for all taxa in all samples\n",
+        "2. The import and export fluxes for each taxon and the external environment\n",
+        "3. Annotations for the fluxes mapping to other databases\n",
+        "\n",
+        "### πŸ“ˆ Growth Rates\n",
+        "\n",
+        "The growth rates are pretty straightforward."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 112,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "2r_XUm7U-HSm",
+        "outputId": "2ef89987-13a9-48a6-85b0-26dcdcf7ff92"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    abundance  growth_rate  reactions  metabolites                   taxon  \\\n",
+              "0    0.187908     0.147278       3293         1883             Bacteroides   \n",
+              "1    0.100000     0.078512       1844         1401          Clostridioides   \n",
+              "2    0.130915     0.102472       1986         1472        Faecalibacterium   \n",
+              "3    0.084938     0.066644       2879         1747         Parabacteroides   \n",
+              "4    0.496238     0.389606       2712         1656             Phocaeicola   \n",
+              "5    0.170093     0.248553       3293         1883             Bacteroides   \n",
+              "6    0.071992     0.108048       2235         1371             Barnesiella   \n",
+              "7    0.070653     0.101473       2808         1732                 Blautia   \n",
+              "8    0.100000     0.143653       1844         1401          Clostridioides   \n",
+              "9    0.090526     0.129872       1986         1472        Faecalibacterium   \n",
+              "10   0.083122     0.122119       2879         1747         Parabacteroides   \n",
+              "11   0.413614     0.607244       2712         1656             Phocaeicola   \n",
+              "12   0.759954     0.300619       2274         1386             Akkermansia   \n",
+              "13   0.100000     0.144461       1844         1401          Clostridioides   \n",
+              "14   0.055102     0.041183       3532         2001             Clostridium   \n",
+              "15   0.013039     0.009244       2826         1639  Erysipelatoclostridium   \n",
+              "16   0.035175     0.025067       1274         1138        Paeniclostridium   \n",
+              "17   0.036730     0.031724       1652         1308             Veillonella   \n",
+              "18   0.094704     0.133863       3293         1883             Bacteroides   \n",
+              "19   0.092168     0.130290       2808         1732                 Blautia   \n",
+              "20   0.111638     0.157821       1844         1401          Clostridioides   \n",
+              "21   0.312443     0.441683       1986         1472        Faecalibacterium   \n",
+              "22   0.241890     0.341913       2712         1656             Phocaeicola   \n",
+              "23   0.147157     0.208032       2860         1805           Streptococcus   \n",
+              "24   0.565087     0.128835       2274         1386             Akkermansia   \n",
+              "25   0.100000     0.377698       1844         1401          Clostridioides   \n",
+              "26   0.088290     0.323804       2197         1602           Fusobacterium   \n",
+              "27   0.246623     0.928642       1652         1308             Veillonella   \n",
+              "28   0.202085     0.267660       2274         1386             Akkermansia   \n",
+              "29   0.246283     0.412257       2808         1732                 Blautia   \n",
+              "30   0.105784     0.176446       1844         1401          Clostridioides   \n",
+              "31   0.020161     0.033840       1966         1392           Coprobacillus   \n",
+              "32   0.012706     0.023217       1187         1056               Dialister   \n",
+              "33   0.210121     0.353430       2826         1639  Erysipelatoclostridium   \n",
+              "34   0.014204     0.022333       1207         1105          Flavonifractor   \n",
+              "35   0.013217     0.021691       1369         1170             Lachnospira   \n",
+              "36   0.175438     0.295404       2681         1578      Mediterraneibacter   \n",
+              "37   0.045896     0.069084       2650         1613               Alistipes   \n",
+              "38   0.012559     0.017830       2034         1491            Anaerostipes   \n",
+              "39   0.168400     0.243903       3293         1883             Bacteroides   \n",
+              "40   0.056172     0.083090       2235         1371             Barnesiella   \n",
+              "41   0.027401     0.039184       2808         1732                 Blautia   \n",
+              "42   0.100000     0.141583       1844         1401          Clostridioides   \n",
+              "43   0.021236     0.029938       2073         1577             Eubacterium   \n",
+              "44   0.027287     0.038572       1986         1472        Faecalibacterium   \n",
+              "45   0.104009     0.151023       2879         1747         Parabacteroides   \n",
+              "46   0.010732     0.012497       1080         1019          Parasutterella   \n",
+              "47   0.426310     0.619569       2712         1656             Phocaeicola   \n",
+              "48   0.192591     0.360678       1844         1401          Clostridioides   \n",
+              "49   0.197835     0.370503       2197         1602           Fusobacterium   \n",
+              "50   0.194153     0.363601       1206         1068             Megasphaera   \n",
+              "51   0.098416     0.184309       1577         1286              Morganella   \n",
+              "52   0.025775     0.042924       1080         1019          Parasutterella   \n",
+              "53   0.084021     0.156784       2860         1805           Streptococcus   \n",
+              "54   0.207208     0.388049       1652         1308             Veillonella   \n",
+              "\n",
+              "    tradeoff   sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "1        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "2        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "3        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "4        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "5        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "6        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "7        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "8        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "9        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "10       0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "11       0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "12       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "13       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "14       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "15       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "16       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "17       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "18       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "19       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "20       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "21       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "22       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "23       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "24       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "25       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "26       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "27       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "28       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "29       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "30       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "31       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "32       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "33       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "34       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "35       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "36       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "37       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "38       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "39       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "40       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "41       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "42       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "43       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "44       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "45       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "46       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "47       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "48       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "49       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "50       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "51       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "52       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "53       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "54       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "\n",
+              "    ...           host_age host_age_units host_body_mass_index  \\\n",
+              "0   ...  Restricted access          years    Restricted access   \n",
+              "1   ...  Restricted access          years    Restricted access   \n",
+              "2   ...  Restricted access          years    Restricted access   \n",
+              "3   ...  Restricted access          years    Restricted access   \n",
+              "4   ...  Restricted access          years    Restricted access   \n",
+              "5   ...  Restricted access          years    Restricted access   \n",
+              "6   ...  Restricted access          years    Restricted access   \n",
+              "7   ...  Restricted access          years    Restricted access   \n",
+              "8   ...  Restricted access          years    Restricted access   \n",
+              "9   ...  Restricted access          years    Restricted access   \n",
+              "10  ...  Restricted access          years    Restricted access   \n",
+              "11  ...  Restricted access          years    Restricted access   \n",
+              "12  ...                 39          years                 29.3   \n",
+              "13  ...                 39          years                 29.3   \n",
+              "14  ...                 39          years                 29.3   \n",
+              "15  ...                 39          years                 29.3   \n",
+              "16  ...                 39          years                 29.3   \n",
+              "17  ...                 39          years                 29.3   \n",
+              "18  ...  Restricted access          years    Restricted access   \n",
+              "19  ...  Restricted access          years    Restricted access   \n",
+              "20  ...  Restricted access          years    Restricted access   \n",
+              "21  ...  Restricted access          years    Restricted access   \n",
+              "22  ...  Restricted access          years    Restricted access   \n",
+              "23  ...  Restricted access          years    Restricted access   \n",
+              "24  ...                 47          years                 35.5   \n",
+              "25  ...                 47          years                 35.5   \n",
+              "26  ...                 47          years                 35.5   \n",
+              "27  ...                 47          years                 35.5   \n",
+              "28  ...                 53          years                 34.4   \n",
+              "29  ...                 53          years                 34.4   \n",
+              "30  ...                 53          years                 34.4   \n",
+              "31  ...                 53          years                 34.4   \n",
+              "32  ...                 53          years                 34.4   \n",
+              "33  ...                 53          years                 34.4   \n",
+              "34  ...                 53          years                 34.4   \n",
+              "35  ...                 53          years                 34.4   \n",
+              "36  ...                 53          years                 34.4   \n",
+              "37  ...  Restricted access          years    Restricted access   \n",
+              "38  ...  Restricted access          years    Restricted access   \n",
+              "39  ...  Restricted access          years    Restricted access   \n",
+              "40  ...  Restricted access          years    Restricted access   \n",
+              "41  ...  Restricted access          years    Restricted access   \n",
+              "42  ...  Restricted access          years    Restricted access   \n",
+              "43  ...  Restricted access          years    Restricted access   \n",
+              "44  ...  Restricted access          years    Restricted access   \n",
+              "45  ...  Restricted access          years    Restricted access   \n",
+              "46  ...  Restricted access          years    Restricted access   \n",
+              "47  ...  Restricted access          years    Restricted access   \n",
+              "48  ...                 61          years                 32.5   \n",
+              "49  ...                 61          years                 32.5   \n",
+              "50  ...                 61          years                 32.5   \n",
+              "51  ...                 61          years                 32.5   \n",
+              "52  ...                 61          years                 32.5   \n",
+              "53  ...                 61          years                 32.5   \n",
+              "54  ...                 61          years                 32.5   \n",
+              "\n",
+              "          host_height host_height_units host_subject_id        host_weight  \\\n",
+              "0   Restricted access                 m           Donor  Restricted access   \n",
+              "1   Restricted access                 m           Donor  Restricted access   \n",
+              "2   Restricted access                 m           Donor  Restricted access   \n",
+              "3   Restricted access                 m           Donor  Restricted access   \n",
+              "4   Restricted access                 m           Donor  Restricted access   \n",
+              "5   Restricted access                 m           Donor  Restricted access   \n",
+              "6   Restricted access                 m           Donor  Restricted access   \n",
+              "7   Restricted access                 m           Donor  Restricted access   \n",
+              "8   Restricted access                 m           Donor  Restricted access   \n",
+              "9   Restricted access                 m           Donor  Restricted access   \n",
+              "10  Restricted access                 m           Donor  Restricted access   \n",
+              "11  Restricted access                 m           Donor  Restricted access   \n",
+              "12              165.1                 m             CD1               80.1   \n",
+              "13              165.1                 m             CD1               80.1   \n",
+              "14              165.1                 m             CD1               80.1   \n",
+              "15              165.1                 m             CD1               80.1   \n",
+              "16              165.1                 m             CD1               80.1   \n",
+              "17              165.1                 m             CD1               80.1   \n",
+              "18  Restricted access                 m           Donor  Restricted access   \n",
+              "19  Restricted access                 m           Donor  Restricted access   \n",
+              "20  Restricted access                 m           Donor  Restricted access   \n",
+              "21  Restricted access                 m           Donor  Restricted access   \n",
+              "22  Restricted access                 m           Donor  Restricted access   \n",
+              "23  Restricted access                 m           Donor  Restricted access   \n",
+              "24               1.55                 m             CD9               85.1   \n",
+              "25               1.55                 m             CD9               85.1   \n",
+              "26               1.55                 m             CD9               85.1   \n",
+              "27               1.55                 m             CD9               85.1   \n",
+              "28               1.56                 m            CD13               83.9   \n",
+              "29               1.56                 m            CD13               83.9   \n",
+              "30               1.56                 m            CD13               83.9   \n",
+              "31               1.56                 m            CD13               83.9   \n",
+              "32               1.56                 m            CD13               83.9   \n",
+              "33               1.56                 m            CD13               83.9   \n",
+              "34               1.56                 m            CD13               83.9   \n",
+              "35               1.56                 m            CD13               83.9   \n",
+              "36               1.56                 m            CD13               83.9   \n",
+              "37  Restricted access                 m           Donor  Restricted access   \n",
+              "38  Restricted access                 m           Donor  Restricted access   \n",
+              "39  Restricted access                 m           Donor  Restricted access   \n",
+              "40  Restricted access                 m           Donor  Restricted access   \n",
+              "41  Restricted access                 m           Donor  Restricted access   \n",
+              "42  Restricted access                 m           Donor  Restricted access   \n",
+              "43  Restricted access                 m           Donor  Restricted access   \n",
+              "44  Restricted access                 m           Donor  Restricted access   \n",
+              "45  Restricted access                 m           Donor  Restricted access   \n",
+              "46  Restricted access                 m           Donor  Restricted access   \n",
+              "47  Restricted access                 m           Donor  Restricted access   \n",
+              "48              1.727                 m             CD3               97.3   \n",
+              "49              1.727                 m             CD3               97.3   \n",
+              "50              1.727                 m             CD3               97.3   \n",
+              "51              1.727                 m             CD3               97.3   \n",
+              "52              1.727                 m             CD3               97.3   \n",
+              "53              1.727                 m             CD3               97.3   \n",
+              "54              1.727                 m             CD3               97.3   \n",
+              "\n",
+              "   host_weight_units               race                sex  \n",
+              "0                 kg  Restricted access  Restricted access  \n",
+              "1                 kg  Restricted access  Restricted access  \n",
+              "2                 kg  Restricted access  Restricted access  \n",
+              "3                 kg  Restricted access  Restricted access  \n",
+              "4                 kg  Restricted access  Restricted access  \n",
+              "5                 kg  Restricted access  Restricted access  \n",
+              "6                 kg  Restricted access  Restricted access  \n",
+              "7                 kg  Restricted access  Restricted access  \n",
+              "8                 kg  Restricted access  Restricted access  \n",
+              "9                 kg  Restricted access  Restricted access  \n",
+              "10                kg  Restricted access  Restricted access  \n",
+              "11                kg  Restricted access  Restricted access  \n",
+              "12                kg              white             female  \n",
+              "13                kg              white             female  \n",
+              "14                kg              white             female  \n",
+              "15                kg              white             female  \n",
+              "16                kg              white             female  \n",
+              "17                kg              white             female  \n",
+              "18                kg  Restricted access  Restricted access  \n",
+              "19                kg  Restricted access  Restricted access  \n",
+              "20                kg  Restricted access  Restricted access  \n",
+              "21                kg  Restricted access  Restricted access  \n",
+              "22                kg  Restricted access  Restricted access  \n",
+              "23                kg  Restricted access  Restricted access  \n",
+              "24                kg              white             female  \n",
+              "25                kg              white             female  \n",
+              "26                kg              white             female  \n",
+              "27                kg              white             female  \n",
+              "28                kg              white             female  \n",
+              "29                kg              white             female  \n",
+              "30                kg              white             female  \n",
+              "31                kg              white             female  \n",
+              "32                kg              white             female  \n",
+              "33                kg              white             female  \n",
+              "34                kg              white             female  \n",
+              "35                kg              white             female  \n",
+              "36                kg              white             female  \n",
+              "37                kg  Restricted access  Restricted access  \n",
+              "38                kg  Restricted access  Restricted access  \n",
+              "39                kg  Restricted access  Restricted access  \n",
+              "40                kg  Restricted access  Restricted access  \n",
+              "41                kg  Restricted access  Restricted access  \n",
+              "42                kg  Restricted access  Restricted access  \n",
+              "43                kg  Restricted access  Restricted access  \n",
+              "44                kg  Restricted access  Restricted access  \n",
+              "45                kg  Restricted access  Restricted access  \n",
+              "46                kg  Restricted access  Restricted access  \n",
+              "47                kg  Restricted access  Restricted access  \n",
+              "48                kg              white               male  \n",
+              "49                kg              white               male  \n",
+              "50                kg              white               male  \n",
+              "51                kg              white               male  \n",
+              "52                kg              white               male  \n",
+              "53                kg              white               male  \n",
+              "54                kg              white               male  \n",
+              "\n",
+              "[55 rows x 22 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
abundancegrowth_ratereactionsmetabolitestaxontradeoffsample_idUnnamed: 0collection_timestampday_relative_to_fmt...host_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
00.1879080.14727832931883Bacteroides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
10.1000000.07851218441401Clostridioides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
20.1309150.10247219861472Faecalibacterium0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
30.0849380.06664428791747Parabacteroides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
40.4962380.38960627121656Phocaeicola0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
50.1700930.24855332931883Bacteroides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
60.0719920.10804822351371Barnesiella0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
70.0706530.10147328081732Blautia0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
80.1000000.14365318441401Clostridioides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
90.0905260.12987219861472Faecalibacterium0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
100.0831220.12211928791747Parabacteroides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
110.4136140.60724427121656Phocaeicola0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
120.7599540.30061922741386Akkermansia0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
130.1000000.14446118441401Clostridioides0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
140.0551020.04118335322001Clostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
150.0130390.00924428261639Erysipelatoclostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
160.0351750.02506712741138Paeniclostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
170.0367300.03172416521308Veillonella0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
180.0947040.13386332931883Bacteroides0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
190.0921680.13029028081732Blautia0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
200.1116380.15782118441401Clostridioides0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
210.3124430.44168319861472Faecalibacterium0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
220.2418900.34191327121656Phocaeicola0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
230.1471570.20803228601805Streptococcus0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
240.5650870.12883522741386Akkermansia0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
250.1000000.37769818441401Clostridioides0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
260.0882900.32380421971602Fusobacterium0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
270.2466230.92864216521308Veillonella0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
280.2020850.26766022741386Akkermansia0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
290.2462830.41225728081732Blautia0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
300.1057840.17644618441401Clostridioides0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
310.0201610.03384019661392Coprobacillus0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
320.0127060.02321711871056Dialister0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
330.2101210.35343028261639Erysipelatoclostridium0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
340.0142040.02233312071105Flavonifractor0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
350.0132170.02169113691170Lachnospira0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
360.1754380.29540426811578Mediterraneibacter0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
370.0458960.06908426501613Alistipes0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
380.0125590.01783020341491Anaerostipes0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
390.1684000.24390332931883Bacteroides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
400.0561720.08309022351371Barnesiella0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
410.0274010.03918428081732Blautia0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
420.1000000.14158318441401Clostridioides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
430.0212360.02993820731577Eubacterium0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
440.0272870.03857219861472Faecalibacterium0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
450.1040090.15102328791747Parabacteroides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
460.0107320.01249710801019Parasutterella0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
470.4263100.61956927121656Phocaeicola0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
480.1925910.36067818441401Clostridioides0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
490.1978350.37050321971602Fusobacterium0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
500.1941530.36360112061068Megasphaera0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
510.0984160.18430915771286Morganella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
520.0257750.04292410801019Parasutterella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
530.0840210.15678428601805Streptococcus0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
540.2072080.38804916521308Veillonella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
\n",
+              "
55 rows Γ— 22 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 112
+        }
+      ],
+      "source": [
+        "growth_rates=pd.merge(growth.growth_rates,metadata,on='sample_id')\n",
+        "growth_rates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F5BK7DDv0UfA"
+      },
+      "source": [
+        "### ↔️ Exchange Fluxes\n",
+        "\n",
+        "More interesting are the exchange fluxes. These reactions represent the import and export of metabolites from the system Let's look at those now:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 102,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "lQW2BBS10jdN",
+        "outputId": "d0ebb86e-549a-4727-8f9c-4461c25eeeca"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             taxon   sample_id  tolerance          reaction      flux  \\\n",
+              "0      Bacteroides  ERR1883195     0.0001    EX_MGlcn150(e) -0.001865   \n",
+              "1      Bacteroides  ERR1883195     0.0001    EX_MGlcn180(e) -0.001879   \n",
+              "4      Bacteroides  ERR1883195     0.0001  EX_MGlcn81_rl(e)  0.002123   \n",
+              "5      Bacteroides  ERR1883195     0.0001     EX_MGlcn24(e) -0.002457   \n",
+              "6      Bacteroides  ERR1883195     0.0001    EX_MGlcn165(e)  0.004506   \n",
+              "...            ...         ...        ...               ...       ...   \n",
+              "22046       medium  ERR1883294     0.0001  EX_MGlcn170_rl_m -0.000192   \n",
+              "22050       medium  ERR1883294     0.0001          EX_nmn_m -0.100040   \n",
+              "22056       medium  ERR1883294     0.0001          EX_akg_m  0.000115   \n",
+              "22070       medium  ERR1883294     0.0001          EX_oaa_m  0.000154   \n",
+              "22078       medium  ERR1883294     0.0001         EX_xtsn_m  0.000457   \n",
+              "\n",
+              "       abundance     metabolite direction  \n",
+              "0       0.208787    MGlcn150[e]    import  \n",
+              "1       0.208787    MGlcn180[e]    import  \n",
+              "4       0.208787  MGlcn81_rl[e]    export  \n",
+              "5       0.208787     MGlcn24[e]    import  \n",
+              "6       0.208787    MGlcn165[e]    export  \n",
+              "...          ...            ...       ...  \n",
+              "22046        NaN  MGlcn170_rl_m    import  \n",
+              "22050        NaN          nmn_m    import  \n",
+              "22056        NaN          akg_m    export  \n",
+              "22070        NaN          oaa_m    export  \n",
+              "22078        NaN         xtsn_m    export  \n",
+              "\n",
+              "[10597 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
taxonsample_idtolerancereactionfluxabundancemetabolitedirection
0BacteroidesERR18831950.0001EX_MGlcn150(e)-0.0018650.208787MGlcn150[e]import
1BacteroidesERR18831950.0001EX_MGlcn180(e)-0.0018790.208787MGlcn180[e]import
4BacteroidesERR18831950.0001EX_MGlcn81_rl(e)0.0021230.208787MGlcn81_rl[e]export
5BacteroidesERR18831950.0001EX_MGlcn24(e)-0.0024570.208787MGlcn24[e]import
6BacteroidesERR18831950.0001EX_MGlcn165(e)0.0045060.208787MGlcn165[e]export
...........................
22046mediumERR18832940.0001EX_MGlcn170_rl_m-0.000192NaNMGlcn170_rl_mimport
22050mediumERR18832940.0001EX_nmn_m-0.100040NaNnmn_mimport
22056mediumERR18832940.0001EX_akg_m0.000115NaNakg_mexport
22070mediumERR18832940.0001EX_oaa_m0.000154NaNoaa_mexport
22078mediumERR18832940.0001EX_xtsn_m0.000457NaNxtsn_mexport
\n",
+              "
10597 rows Γ— 8 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 102
+        }
+      ],
+      "source": [
+        "growth.exchanges"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pu5XtkUl1YG1"
+      },
+      "source": [
+        "So we see how much of each metabolite is either consumed or produced by each taxon in each sample. `tolerance` denotes the accuracy of the solver and tells you the smallest absolute flux that is likely different form zero (i.e., substantial flux). *All of the fluxes are normalized to 1g dry weight of bacteria*. So, you can directly compare fluxes between taxa, even if they are present at very different abundances.\n",
+        "\n",
+        "If you're curious what the abbreviation for each of these metabolites represents, that can be found in the annotations dataframe. For instance, let's find out what `\"tre[e]\"` represents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "DphXa9hw1yxM",
+        "outputId": "4bf58c2c-2cc4-47b0-e4af-e7e7b827e54d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          metabolite       name  molecular_weight  C_number  N_number  \\\n",
+              "reaction                                                                \n",
+              "EX_tre(e)     tre[e]  Trehalose         342.29648        12         0   \n",
+              "\n",
+              "                hmdb inchi kegg.compound pubchem.compound chebi   reaction  \n",
+              "reaction                                                                    \n",
+              "EX_tre(e)  HMDB00975   NaN        C01083             7427   NaN  EX_tre(e)  "
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
metabolitenamemolecular_weightC_numberN_numberhmdbinchikegg.compoundpubchem.compoundchebireaction
reaction
EX_tre(e)tre[e]Trehalose342.29648120HMDB00975NaNC010837427NaNEX_tre(e)
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ],
+      "source": [
+        "anns = growth.annotations\n",
+        "anns[anns.metabolite == \"tre[e]\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GVHLD2dm4a6B"
+      },
+      "source": [
+        "Trehalose! Interesting, [that's an important metabolite](https://pubmed.ncbi.nlm.nih.gov/34277467/) in the context of CDI! All of these annotations and more information at are also available at https://vmh.life, maintained by Dr. Ines Thiele's lab."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CImtzqRJbbGj"
+      },
+      "source": [
+        "\n",
+        "# πŸ“Š Visualizations\n",
+        "\n",
+        "Let's visualize our results. Because of the rich output of these models, it can be overwhelming to represent it all, but don't worry! There are tools in place for this already.\n",
+        "\n",
+        "We will use the standard visualizations included in MICOM. These tools take in the growth results we obtained before and create visualizations in standalone HTML files that bundle the plots and raw data and can be viewed directly in your browser.\n",
+        "\n",
+        "First, let's look at the growth rates of each taxon across samples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "EaplMHFLcMT7"
+      },
+      "outputs": [],
+      "source": [
+        "from micom.viz import *\n",
+        "\n",
+        "viz = plot_growth(growth, filename=\"growthrates.html\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Normally, we could call `viz.view()` afterwards and it would open it in our web browser. However, this will not work in Colab. However, the plot function creates the file `growth_rates_[DATE].html` in your `materials` folder. To open it, simply download that file and view it in your web browser. We can see that there are many things going on, but it's not super clear. Let's continue."
+      ],
+      "metadata": {
+        "id": "_sjNIuCSXkyb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We're interested in understanding the invasion potential of C. diff so lets extract the predicted C. diff growth rates. In addition to C. diff growth rate we can also look at what fraction of the community growth rate this represents.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qPTCyw-7RqFm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 121,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 436
+        },
+        "id": "9YlWytSp-yKI",
+        "outputId": "c77dda37-1dd1-4ccf-a99c-cdb1519c5e8a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    abundance  growth_rate  reactions  metabolites           taxon  tradeoff  \\\n",
+              "1    0.100000     0.078512       1844         1401  Clostridioides       0.8   \n",
+              "8    0.100000     0.143653       1844         1401  Clostridioides       0.8   \n",
+              "13   0.100000     0.144461       1844         1401  Clostridioides       0.8   \n",
+              "20   0.111638     0.157821       1844         1401  Clostridioides       0.8   \n",
+              "25   0.100000     0.377698       1844         1401  Clostridioides       0.8   \n",
+              "30   0.105784     0.176446       1844         1401  Clostridioides       0.8   \n",
+              "42   0.100000     0.141583       1844         1401  Clostridioides       0.8   \n",
+              "48   0.192591     0.360678       1844         1401  Clostridioides       0.8   \n",
+              "\n",
+              "     sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  ...  \\\n",
+              "1   ERR1883195          12           2011-10-24                  26  ...   \n",
+              "8   ERR1883207          24           2012-01-12                  44  ...   \n",
+              "13  ERR1883214          32           2011-07-26                   0  ...   \n",
+              "20  ERR1883212          30           2012-10-10                 135  ...   \n",
+              "25  ERR1883240          58           2012-02-14             pre-FMT  ...   \n",
+              "30  ERR1883250          68           2011-12-23             pre-FMT  ...   \n",
+              "42  ERR1883225          43           2011-07-26                  54  ...   \n",
+              "48  ERR1883294         112           2011-09-29                   0  ...   \n",
+              "\n",
+              "   host_age_units host_body_mass_index        host_height host_height_units  \\\n",
+              "1           years    Restricted access  Restricted access                 m   \n",
+              "8           years    Restricted access  Restricted access                 m   \n",
+              "13          years                 29.3              165.1                 m   \n",
+              "20          years    Restricted access  Restricted access                 m   \n",
+              "25          years                 35.5               1.55                 m   \n",
+              "30          years                 34.4               1.56                 m   \n",
+              "42          years    Restricted access  Restricted access                 m   \n",
+              "48          years                 32.5              1.727                 m   \n",
+              "\n",
+              "   host_subject_id        host_weight host_weight_units               race  \\\n",
+              "1            Donor  Restricted access                kg  Restricted access   \n",
+              "8            Donor  Restricted access                kg  Restricted access   \n",
+              "13             CD1               80.1                kg              white   \n",
+              "20           Donor  Restricted access                kg  Restricted access   \n",
+              "25             CD9               85.1                kg              white   \n",
+              "30            CD13               83.9                kg              white   \n",
+              "42           Donor  Restricted access                kg  Restricted access   \n",
+              "48             CD3               97.3                kg              white   \n",
+              "\n",
+              "                  sex abundance_weighted_growth_rate  \n",
+              "1   Restricted access                       0.007851  \n",
+              "8   Restricted access                       0.014365  \n",
+              "13             female                       0.014446  \n",
+              "20  Restricted access                       0.017619  \n",
+              "25             female                       0.037770  \n",
+              "30             female                       0.018665  \n",
+              "42  Restricted access                       0.014158  \n",
+              "48               male                       0.069463  \n",
+              "\n",
+              "[8 rows x 23 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
abundancegrowth_ratereactionsmetabolitestaxontradeoffsample_idUnnamed: 0collection_timestampday_relative_to_fmt...host_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesexabundance_weighted_growth_rate
10.1000000.07851218441401Clostridioides0.8ERR1883195122011-10-2426...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.007851
80.1000000.14365318441401Clostridioides0.8ERR1883207242012-01-1244...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.014365
130.1000000.14446118441401Clostridioides0.8ERR1883214322011-07-260...years29.3165.1mCD180.1kgwhitefemale0.014446
200.1116380.15782118441401Clostridioides0.8ERR1883212302012-10-10135...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.017619
250.1000000.37769818441401Clostridioides0.8ERR1883240582012-02-14pre-FMT...years35.51.55mCD985.1kgwhitefemale0.037770
300.1057840.17644618441401Clostridioides0.8ERR1883250682011-12-23pre-FMT...years34.41.56mCD1383.9kgwhitefemale0.018665
420.1000000.14158318441401Clostridioides0.8ERR1883225432011-07-2654...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.014158
480.1925910.36067818441401Clostridioides0.8ERR18832941122011-09-290...years32.51.727mCD397.3kgwhitemale0.069463
\n",
+              "
8 rows Γ— 23 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 121
+        }
+      ],
+      "source": [
+        "cdiff = growth_rates[growth_rates.taxon=='Clostridioides'].copy()\n",
+        "cdiff"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we've extracted the C. diff specific growth rates lets take a look at how they compare between patients with healthy and disbiotic gut microbiomes"
+      ],
+      "metadata": {
+        "id": "bQKYxSZTSPk1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 122,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "AWG3U094-_OI",
+        "outputId": "ef7e9ff0-9348-48ba-af7f-d05c65ad91d6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 122
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "sns.boxplot(x='disease_state',y='growth_rate',data=cdiff)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Looks like C. diff is predicted to grow in all samples but its predicted growth rate is ~2x higher in the Pre-FMT samples. You can see there is also a decent amount of variation in the Pre-FMT results."
+      ],
+      "metadata": {
+        "id": "MEuym_PVui-k"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G1JbbKrLcVye"
+      },
+      "source": [
+        "## Growth niches\n",
+        "\n",
+        "Another thing we can look at is whether individual taxa inhabit different growth niches across different disease contexts. Here we can use the `plot_exchanges_per_taxon` function to see how exchanges differ within and between taxa, within and across human populations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "id": "NlZrfv38esj8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6df836fd-71ac-4cfd-fd1c-883e9d31f311"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ],
+      "source": [
+        "plot_exchanges_per_taxon(growth, perplexity=4, direction=\"import\", filename=\"niche.html\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qXnbUCCs2yVG"
+      },
+      "source": [
+        "\n",
+        "This function projects the full set of import or export fluxes onto a two dimensional plane, and arranges taxa so that more similar flux patterns lie nearer together. Taxa closer to one another compete for a more similar set of resources (and/or produce a more similar set of metabolites). The center of the plot signifies a more competitive nutrient space, whereas clusters on the outskirts denote more isolated niches.\n",
+        "\n",
+        "You can tune [TSNE parameters](https://distill.pub/2016/misread-tsne/), such as perplexity, to get a more meaningful grouping. We will lower the perplexity here since we don't have a lot of data points.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "One small take away from this analysis is the speration between C. diff pre- and post- FMT samples, suggesting that C. diff may leverage different metabolic strategies in these contexts. Lets take a closer look at this..."
+      ],
+      "metadata": {
+        "id": "weoSaLDPZYAK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4Y_XfHkB4sO8"
+      },
+      "source": [
+        "## Comparative Metabolomics\n",
+        "\n",
+        "Now let's compare the metabolomic imports between the two disease contexts. We're interested to see how the metabolomic profile of the microbiome changes when the disease state changes, as changes in microbiome context can lead to changes in host succeptibility to infection.  To look into this deeper, we'll transform the microbiome import data and then plot the metabolite exports on a heatmap."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pAtec3I78DJJ"
+      },
+      "source": [
+        "We can use the `consumption_rates` function in MICOM to calculate consumption rates from the growth results. This will tell us what the patient microbiomes are consuming and provide additional insight into available niches. To visualize the results we'll  run a centered log ratio transformation on the data, to account for the compositional nature of these data and compare all the fluxes against each other. Importantly, here we consider the consumption rates for samples with no C. diff present to understand how the initial state of the patient microbiomes may influence invasion potential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 103,
+      "metadata": {
+        "id": "DJAsxjnFdajN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 486
+        },
+        "outputId": "59b69294-cdef-4fdf-a5fa-5f1d8cab1cde"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "disease_state    Pre-FMT                                     healthy  \\\n",
+              "sample_id     ERR1883214 ERR1883240 ERR1883250 ERR1883294 ERR1883195   \n",
+              "name                                                                   \n",
+              "D-glucose      -3.253101  -3.564477  -2.064038  -3.972573   4.450353   \n",
+              "acetate         2.572101   3.594739   4.306373   4.539524  -2.642172   \n",
+              "Water           2.966225  -3.584166   3.043172  -3.972573   4.497614   \n",
+              "D-Fructose      3.219760  -3.584166  -0.695070  -3.972573   3.268810   \n",
+              "pyruvate       -3.404889   3.505223  -3.497278   3.022735  -2.642172   \n",
+              "...                  ...        ...        ...        ...        ...   \n",
+              "Thiamin        -3.404889  -2.773089  -2.982110  -3.403379  -2.388275   \n",
+              "Spermidine     -3.404889  -2.445104  -2.992604  -3.250518  -2.374584   \n",
+              "Fe2+           -2.838896  -2.091063  -2.203376  -2.370595  -1.778976   \n",
+              "Putrescine     -3.404889  -2.848543  -3.087577  -3.295817  -2.353273   \n",
+              "Menaquinone 8  -2.873638  -3.295815  -2.979719  -3.351557  -2.276149   \n",
+              "\n",
+              "disease_state                                   \n",
+              "sample_id     ERR1883207 ERR1883212 ERR1883225  \n",
+              "name                                            \n",
+              "D-glucose       4.493531   3.416515   4.569671  \n",
+              "acetate        -2.772490  -3.668369  -2.625105  \n",
+              "Water           4.280483   3.763363   4.354428  \n",
+              "D-Fructose      3.253561   2.612354   3.276231  \n",
+              "pyruvate       -2.772490  -3.668369  -2.625105  \n",
+              "...                  ...        ...        ...  \n",
+              "Thiamin        -2.444364  -2.939826  -2.378889  \n",
+              "Spermidine     -2.445049  -2.936479  -2.376260  \n",
+              "Fe2+           -1.820472  -2.611296  -1.668659  \n",
+              "Putrescine     -2.438953  -2.912851  -2.400161  \n",
+              "Menaquinone 8  -2.471199  -3.185775  -2.625105  \n",
+              "\n",
+              "[647 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
disease_statePre-FMThealthy
sample_idERR1883214ERR1883240ERR1883250ERR1883294ERR1883195ERR1883207ERR1883212ERR1883225
name
D-glucose-3.253101-3.564477-2.064038-3.9725734.4503534.4935313.4165154.569671
acetate2.5721013.5947394.3063734.539524-2.642172-2.772490-3.668369-2.625105
Water2.966225-3.5841663.043172-3.9725734.4976144.2804833.7633634.354428
D-Fructose3.219760-3.584166-0.695070-3.9725733.2688103.2535612.6123543.276231
pyruvate-3.4048893.505223-3.4972783.022735-2.642172-2.772490-3.668369-2.625105
...........................
Thiamin-3.404889-2.773089-2.982110-3.403379-2.388275-2.444364-2.939826-2.378889
Spermidine-3.404889-2.445104-2.992604-3.250518-2.374584-2.445049-2.936479-2.376260
Fe2+-2.838896-2.091063-2.203376-2.370595-1.778976-1.820472-2.611296-1.668659
Putrescine-3.404889-2.848543-3.087577-3.295817-2.353273-2.438953-2.912851-2.400161
Menaquinone 8-2.873638-3.295815-2.979719-3.351557-2.276149-2.471199-3.185775-2.625105
\n",
+              "
647 rows Γ— 8 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 103
+        }
+      ],
+      "source": [
+        "from micom.measures import consumption_rates\n",
+        "import numpy as np\n",
+        "no_cdiff_growth = load_results(\"treasure_chest/no-cdiff-growth.zip\")  # Load growth results with no C. diff invasion\n",
+        "exchanges = consumption_rates(no_cdiff_growth)         # extract consumption rates\n",
+        "exchanges=pd.merge(exchanges,metadata,on='sample_id')  # add metadata\n",
+        "exchanges = pd.pivot_table(                            # convert to a matrix of samples vs. metabolites\n",
+        "    exchanges,                                         # that contains the production rates\n",
+        "    index = ['disease_state','sample_id'],\n",
+        "    columns = 'name',\n",
+        "    values = 'flux'\n",
+        ")\n",
+        "exchanges = exchanges.T.fillna(0.0)                    # if a metabolite is not produced its flux is zero\n",
+        "exchanges = exchanges.apply(                           # ...and a CLR transform again, normalizes the fluxes\n",
+        "    lambda xs: np.log(xs + 0.001) - np.log(xs.mean() + 0.001),\n",
+        "    axis=0)\n",
+        "exchanges = exchanges.reindex(                         # sort by variance, highest variance fluxes first\n",
+        "    exchanges.var(axis = 1).sort_values(ascending=False).index\n",
+        ")\n",
+        "\n",
+        "exchanges"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nYuc7Wu38nYd"
+      },
+      "source": [
+        "We can use seaborn to plot our heatmap:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 104,
+      "metadata": {
+        "id": "YHnKeFuF3qAt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "06c5b995-06e9-48ca-fd7e-25e5998af6e0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 104
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "import numpy as np\n",
+        "\n",
+        "sns.clustermap(\n",
+        "    exchanges.head(50),  # take 50 highest fluxes\n",
+        "    cmap = 'viridis',\n",
+        "    yticklabels = True,  # show all metabolite names\n",
+        "    figsize = (8, 12)    # size of the heatmap\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1i2LStZiUst"
+      },
+      "source": [
+        "We can see here that the disease context is important - there are significant differences in consumption rates between the healthy and pre-fmt microbiomes. These differences may why C. diff can exploit the pre-FMT microbiomes and achieve higher predicted growth rates."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l8maOr3w2bOo"
+      },
+      "source": [
+        "# 🏫 Exercises"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Time for you to try your hand at some analysis, lets take a closer looks at the metabolic strategies used by C. diff"
+      ],
+      "metadata": {
+        "id": "XZt3ojssnCzz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VjUustQG2_bX"
+      },
+      "source": [
+        "## Metabolic strategies used by C. diff\n",
+        "We've alread looked at the community wide consumption fluxes in the absencence of C. diff and found that they differ between disease contexts. What about the import fluxes of C. diff specifically? Can you develop a visualization to look at those?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your code here"
+      ],
+      "metadata": {
+        "id": "zrUZSSp62uUt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CsqIRTbC7doD"
+      },
+      "source": [
+        "# πŸ”΅ Addendum\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hycoXNTi5xsH"
+      },
+      "source": [
+        "## Choosing a tradeoff value\n",
+        "\n",
+        "Even if you don't have growth rates available you can still use your data to choose a decent tradeoff value. This can be done by choosing the largest tradeoff value that still allows growth for the majority of the taxa that you observed in the sample (if they are present at an appreciable abundance, they should be able to grow). This can be done with the `tradeoff` workflow in MICOM that will run cooperative tradeoff with varying tradeoff values, which can be visualized with the `plot_tradeoff` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "manifest"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "I8NnkbnymxoE",
+        "outputId": "41feea98-0cfb-4fe5-c0d6-3fa11631782e"
+      },
+      "execution_count": 123,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0  ERR1883195        12.0           2011-10-24                  26   \n",
+              "1  ERR1883207        24.0           2012-01-12                  44   \n",
+              "2  ERR1883212        30.0           2012-10-10                 135   \n",
+              "3  ERR1883214        32.0           2011-07-26                   0   \n",
+              "4  ERR1883225        43.0           2011-07-26                  54   \n",
+              "5  ERR1883240        58.0           2012-02-14             pre-FMT   \n",
+              "6  ERR1883250        68.0           2011-12-23             pre-FMT   \n",
+              "7  ERR1883294       112.0           2011-09-29                   0   \n",
+              "\n",
+              "    description disease_state           host_age host_age_units  \\\n",
+              "0      Donor 11       healthy  Restricted access          years   \n",
+              "1      Donor 12       healthy  Restricted access          years   \n",
+              "2      Donor 14       healthy  Restricted access          years   \n",
+              "3     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "4     Donor CD1       healthy  Restricted access          years   \n",
+              "5   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "6  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "7     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "\n",
+              "  host_body_mass_index        host_height  ... host_subject_id  \\\n",
+              "0    Restricted access  Restricted access  ...           Donor   \n",
+              "1    Restricted access  Restricted access  ...           Donor   \n",
+              "2    Restricted access  Restricted access  ...           Donor   \n",
+              "3                 29.3              165.1  ...             CD1   \n",
+              "4    Restricted access  Restricted access  ...           Donor   \n",
+              "5                 35.5               1.55  ...             CD9   \n",
+              "6                 34.4               1.56  ...            CD13   \n",
+              "7                 32.5              1.727  ...             CD3   \n",
+              "\n",
+              "         host_weight host_weight_units               race                sex  \\\n",
+              "0  Restricted access                kg  Restricted access  Restricted access   \n",
+              "1  Restricted access                kg  Restricted access  Restricted access   \n",
+              "2  Restricted access                kg  Restricted access  Restricted access   \n",
+              "3               80.1                kg              white             female   \n",
+              "4  Restricted access                kg  Restricted access  Restricted access   \n",
+              "5               85.1                kg              white             female   \n",
+              "6               83.9                kg              white             female   \n",
+              "7               97.3                kg              white               male   \n",
+              "\n",
+              "                file found_taxa  total_taxa  found_fraction  \\\n",
+              "0  ERR1883195.pickle        4.0         4.0        1.000000   \n",
+              "1  ERR1883207.pickle        6.0         6.0        1.000000   \n",
+              "2  ERR1883212.pickle        5.0         6.0        0.833333   \n",
+              "3  ERR1883214.pickle        2.0         2.0        1.000000   \n",
+              "4  ERR1883225.pickle        4.0         4.0        1.000000   \n",
+              "5  ERR1883240.pickle        3.0         3.0        1.000000   \n",
+              "6  ERR1883250.pickle        4.0         5.0        0.800000   \n",
+              "7  ERR1883294.pickle        4.0         6.0        0.666667   \n",
+              "\n",
+              "   found_abundance_fraction  \n",
+              "0                  1.000000  \n",
+              "1                  1.000000  \n",
+              "2                  0.884172  \n",
+              "3                  1.000000  \n",
+              "4                  1.000000  \n",
+              "5                  1.000000  \n",
+              "6                  0.935141  \n",
+              "7                  0.444919  \n",
+              "\n",
+              "[8 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_height...host_subject_idhost_weighthost_weight_unitsracesexfilefound_taxatotal_taxafound_fractionfound_abundance_fraction
0ERR188319512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883195.pickle4.04.01.0000001.000000
1ERR188320724.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883207.pickle6.06.01.0000001.000000
2ERR188321230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883212.pickle5.06.00.8333330.884172
3ERR188321432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1...CD180.1kgwhitefemaleERR1883214.pickle2.02.01.0000001.000000
4ERR188322543.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883225.pickle4.04.01.0000001.000000
5ERR188324058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55...CD985.1kgwhitefemaleERR1883240.pickle3.03.01.0000001.000000
6ERR188325068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56...CD1383.9kgwhitefemaleERR1883250.pickle4.05.00.8000000.935141
7ERR1883294112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727...CD397.3kgwhitemaleERR1883294.pickle4.06.00.6666670.444919
\n",
+              "
8 rows Γ— 21 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 123
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 149,
+      "metadata": {
+        "id": "8_1jesZTHYra",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51,
+          "referenced_widgets": [
+            "d450a2f0b9de425990f12bc7a5631782",
+            "af7b0c394a064469a6f7e8216b197336"
+          ]
+        },
+        "outputId": "b86ed405-c9b6-499c-b124-0915e21af8fa"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d450a2f0b9de425990f12bc7a5631782"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 149
+        }
+      ],
+      "source": [
+        "from micom.workflows import tradeoff\n",
+        "import micom\n",
+        "\n",
+        "tradeoff_results = tradeoff(manifest, \"models\", medium, threads=2)\n",
+        "tradeoff_results.to_csv(\"tradeoff.csv\", index=False)\n",
+        "\n",
+        "plot_tradeoff(tradeoff_results, tolerance=1e-4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y9703vhK6d6c"
+      },
+      "source": [
+        "After opeing `tradeoff_[DATE].html` you will see that, for our example here, all tradeoff values work great. This is because we modeled very few taxa, which keeps the compettion down. If you would allow for fewer abundant taxa in the models, this would change drastically. For instance, here is an example from a colorectal cancer data set:\n",
+        "\n",
+        "[![tradeoff example](https://micom-dev.github.io/micom/_images/tradeoff.png)](https://micom-dev.github.io/micom/_static/tradeoff.html)\n",
+        "\n",
+        "You can see how not using the cooperative tradeoff would give you nonsense results where only 10% of all observed taxa grew. A tradeoff value of 0.6-0.8 would probably be a good choice for this particular data set."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "c991a7ed881363492957ff225bb30af9d5174cd8515a21cbef71fcaa303e4050"
+      }
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "37b0134de4bd4fe38c1afe7bc2bbdc59": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_bc2776a5195f4a8c8dda0e22e2fd9ea3",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:05:08\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:05:08\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "bc2776a5195f4a8c8dda0e22e2fd9ea3": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "59428de9a88b4204885d03de441cbde3": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_901fb7e8d8c844a8b2e86735ca2bead3",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:09:32\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:09:32\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "901fb7e8d8c844a8b2e86735ca2bead3": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d450a2f0b9de425990f12bc7a5631782": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_af7b0c394a064469a6f7e8216b197336",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:32:42\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:32:42\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "af7b0c394a064469a6f7e8216b197336": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/micom_2023_solutions.ipynb b/micom_2023_solutions.ipynb
new file mode 100644
index 0000000..ec7efaa
--- /dev/null
+++ b/micom_2023_solutions.ipynb
@@ -0,0 +1,9112 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C0vqP4LJ9y6K"
+      },
+      "source": [
+        "# 🧫🦠 Modeling microbiota-wide metabolism with MICOM\n",
+        "\n",
+        "This notebook will accompany the second session of the 2023 ISB Microbiome Course. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/micom).\n",
+        "\n",
+        "You can save your own local copy of this notebook by using `File > Save a copy in Drive`. You may be promted to cetify the notebook is safe. We promise that it is 🀞\n",
+        "\n",
+        "**Disclaimer:**\n",
+        "The linear and quadratic programming problems MICOM has to solve are very large and very complicated. There are some very good commercial solvers that are very expensive (even though they are often free for academic use). To make this tutorial as accessible as possible we will use the Open Source solver [OSQP](https://osqp.org/), which is installed along with MICOM. OSQP is amazing with quadratic programming problems (kudos!) but not as accurate for linear problems. Solvers usually only guarantee a solution within a certain numerical tolerance of the real solution. In order to make everything work with OSQP this tolerance has to be relaxed to about 10-3. This means that any result with an absolute value smaller than that might very well be zero so we should look at larger values only. Installing cost-free academic versions of commercial solvers like [IBM CPLEX](https://www.ibm.com/analytics/cplex-optimizer) or [Gurobi](https://www.gurobi.com/) would alow you to lower the tolerance to 10-6.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qgBBl4GtuTuX"
+      },
+      "source": [
+        "# πŸ“ Setup\n",
+        "\n",
+        "MICOM installation is is usually pretty straight-forward and can be as easy as typing `pip install micom` into your Terminal.\n",
+        "\n",
+        "First let's start by downloading the materials again and switching to the folder."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ckON4xr3_bW5"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/gibbons-lab/isb_course_2023 materials\n",
+        "!cd materials\n",
+        "!ls"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "is6fmBUeorwv"
+      },
+      "source": [
+        "## Basic Installation\n",
+        "\n",
+        "Installing MICOM is straight-forward in Python. OSQP itself will be installed automatically along with it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M_TeC5yrst3h",
+        "outputId": "c3cb59af-6caf-4eda-f0f4-b20f4d19ae49"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m832.5/832.5 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m31.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.4/37.4 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.3/138.3 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m71.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.9/112.9 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m49.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m526.7/526.7 kB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDone! πŸŽ‰ \n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q micom\n",
+        "\n",
+        "print(\"Done! πŸŽ‰ \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6oJrxxz6tV9T"
+      },
+      "source": [
+        "## Enable QIIME 2 interactions\n",
+        "\n",
+        "Before we start, we also need to install packages to read the \"biom\" file format used by QIIME 2 to save tables. This is only necessary if you want to read QIIME 2 FeatureTable artifacts (like the ones we constructed yesterday)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5rZX7SK_toLp",
+        "outputId": "9cc5af18-c4de-486e-cb03-8f01bacf92a5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m41.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDone! πŸŽ‰ \n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q numpy Cython biom-format\n",
+        "\n",
+        "print(\"Done! πŸŽ‰ \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oPy1f-WLI0lZ"
+      },
+      "source": [
+        "Okay, all done. So let's get started building some models πŸ¦ΊπŸ› d😁.\n",
+        "\n",
+        "# πŸ’» MICOM\n",
+        "\n",
+        "We will use the Python interface to MICOM since it plays nicely with Colaboratory. However, you could run the same steps within the QIIME 2 MICOM plugin ([q2-micom](https://library.qiime2.org/plugins/q2-micom/26/)).\n",
+        "\n",
+        "Here is an overview of all the steps and functions across both interfaces:\n",
+        "![micom overview](https://github.com/micom-dev/q2-micom/raw/706f583a060b91c12c0cec7acea2354fdd0dd320/docs/assets/overview.png)\n",
+        "\n",
+        "The process of building a metabolic model in MICOM begins with constructing a combined abundance/taxonomy table, referred to hereafter as a taxonomy table. Let's load a sample taxonomy table to see what it looks like:\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "UV9SObSQkSZh",
+        "outputId": "2b3f71ac-8873-45fb-9352-961177f76c6a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   id        genus             species  reactions  \\\n",
+              "0  Escherichia_coli_1  Escherichia  Escherichia coli 0         95   \n",
+              "1  Escherichia_coli_2  Escherichia  Escherichia coli 1         95   \n",
+              "2  Escherichia_coli_3  Escherichia  Escherichia coli 2         95   \n",
+              "3  Escherichia_coli_4  Escherichia  Escherichia coli 3         95   \n",
+              "0  Escherichia_coli_1  Escherichia  Escherichia coli 0         95   \n",
+              "\n",
+              "   metabolites sample_id  abundance  \n",
+              "0           72  sample_1         96  \n",
+              "1           72  sample_1         48  \n",
+              "2           72  sample_1        708  \n",
+              "3           72  sample_1        622  \n",
+              "0           72  sample_2        120  "
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
idgenusspeciesreactionsmetabolitessample_idabundance
0Escherichia_coli_1EscherichiaEscherichia coli 09572sample_196
1Escherichia_coli_2EscherichiaEscherichia coli 19572sample_148
2Escherichia_coli_3EscherichiaEscherichia coli 29572sample_1708
3Escherichia_coli_4EscherichiaEscherichia coli 39572sample_1622
0Escherichia_coli_1EscherichiaEscherichia coli 09572sample_2120
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ],
+      "source": [
+        "from micom.data import test_data\n",
+        "\n",
+        "test_data().head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AEk7yfd1lbYp"
+      },
+      "source": [
+        "In this taxonomy table, we see four identical strains of _E. coli_ (1 through 4), across two samples (sample_1 and sample_2). We can see that each row represents a single taxon in a single sample, and the `abundance` column identifies the abundance of that taxon in the sample.\n",
+        "\n",
+        "The `id` column specifies identifiers for the taxa and should be expressive and not include spaces or special characters. Since we are using a taxonomy database to build our models (more on that soon), we don't need a `file` column.\n",
+        "\n",
+        "You might notice that this dataframe looks very different from what we generated in yesterday's tutorial, where we ended up with separate QIIME 2 artifacts 😱\n",
+        "\n",
+        "No worries, we can deal with that.\n",
+        "\n",
+        "## Importing data from QIIME 2\n",
+        "\n",
+        "MICOM can read QIIME 2 artifacts. You don't even need to have QIIME 2 installed for that! But before we do so, let's resolve one issue. We discussed that MICOM summarizes genome-scale models into pangenome-scale models as a first step, but our data are on the ASV level...so how will we know what to summarize?\n",
+        "\n",
+        "Basically, a specific model database can be used to quickly summarize pangenome-scale models for use within MICOM. So, before we read our data we have to decide which model database to use. We will go with the [AGORA database](https://pubmed.ncbi.nlm.nih.gov/27893703/), which is a curated database of more than 800 bacterial strains that commonly live in the human gut. In particular, we will use a version of this database summarized on the genus rank which can be downloaded from the [MICOM data repository](https://doi.org/10.5281/zenodo.3755182), which contains a whole lot of prebuilt databases. This database is available from the materials folder that we previously cloned."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_57iya0D3L6-"
+      },
+      "source": [
+        "Now we're all set to start building models! The data we previously collected can be found in the `treasure_chest` folder, so we can use those files to build our taxonomy for MICOM."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "o0vBAiiqqPLC"
+      },
+      "outputs": [],
+      "source": [
+        "from micom.taxonomy import qiime_to_micom\n",
+        "\n",
+        "tax = qiime_to_micom(\n",
+        "    \"dada2/table.qza\",\n",
+        "    \"taxa.qza\",\n",
+        "    collapse_on=\"genus\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tax.sample_id.unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TkOm89mQbwXa",
+        "outputId": "a6093f5e-a2c8-4bf3-9e3f-bb925b81bf98"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['ERR1883195', 'ERR1883207', 'ERR1883214', 'ERR1883240',\n",
+              "       'ERR1883250', 'ERR1883212', 'ERR1883225', 'ERR1883294'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-TQ6Zp7wouk4"
+      },
+      "source": [
+        "Notice the `collapse_on` argument. That will specify the rank on which to sumarize and can be a list of several ranks. When matching taxonomy you can either match by the particular rank of interest (for example, just comparing genus names here), or you could compare the entire taxonomy, which will require all taxonomic ranks prior to the target rank to match. For that you cloud specify `collapse_on=[\"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\"]`.\n",
+        "\n",
+        "Taxonomic names will often not match 100% between databases. For instance, the genus name \"Prevotella\" in one database may be \"Prevotella_6\" in another. The more ranks you use for matching the more likely you are to run into these issues. However, the more taxonomic ranks you use to match the more confident you can be that your observed taxon really is the same taxon as the one in the model database.\n",
+        "\n",
+        "The resulting table will contain the same abundances but it will include more ranks if `collapse_on` is a list. All ranks present in the taxonomy will be used when matching to the database. We will stick with the \"lax\" option of only matching on genus ranks.\n",
+        "\n",
+        "Let's now take a look at the taxonomy table we generated:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5zwLVFcNTbq"
+      },
+      "source": [
+        "That looks more like the example! Again, we have a row for each taxon in each sample, so we're good to go.\n",
+        "\n",
+        "One helpful thing to do is to merge in our metadata, so we'll have it at hand for the following steps. In our case, the metadata will include the ethnic group, region, and subsistence type of each of the study participants."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 548
+        },
+        "id": "X9hqoO4go0h1",
+        "outputId": "6d3ff6a9-7c37-4c52-ff4f-5ae39aef4cd7",
+        "collapsed": true
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      sample_id  abundance               genus                  id  relative  \\\n",
+              "0    ERR1883195        6.0         Akkermansia         Akkermansia  0.000116   \n",
+              "1    ERR1883195    17971.0         Phocaeicola         Phocaeicola  0.346522   \n",
+              "2    ERR1883195        5.0         Veillonella         Veillonella  0.000096   \n",
+              "3    ERR1883195     4741.0    Faecalibacterium    Faecalibacterium  0.091417   \n",
+              "4    ERR1883195      576.0  Mediterraneibacter  Mediterraneibacter  0.011107   \n",
+              "..          ...        ...                 ...                 ...       ...   \n",
+              "344  ERR1883294        5.0      Stomatobaculum      Stomatobaculum  0.000926   \n",
+              "345  ERR1883294        3.0      Lancefieldella      Lancefieldella  0.000556   \n",
+              "346  ERR1883294        3.0        Tumebacillus        Tumebacillus  0.000556   \n",
+              "347  ERR1883294        3.0          Tropheryma          Tropheryma  0.000556   \n",
+              "348  ERR1883294        2.0       Mogibacterium       Mogibacterium  0.000370   \n",
+              "\n",
+              "     Unnamed: 0 collection_timestamp day_relative_to_fmt description  \\\n",
+              "0            12           2011-10-24                  26    Donor 11   \n",
+              "1            12           2011-10-24                  26    Donor 11   \n",
+              "2            12           2011-10-24                  26    Donor 11   \n",
+              "3            12           2011-10-24                  26    Donor 11   \n",
+              "4            12           2011-10-24                  26    Donor 11   \n",
+              "..          ...                  ...                 ...         ...   \n",
+              "344         112           2011-09-29                   0   Day 0 CD3   \n",
+              "345         112           2011-09-29                   0   Day 0 CD3   \n",
+              "346         112           2011-09-29                   0   Day 0 CD3   \n",
+              "347         112           2011-09-29                   0   Day 0 CD3   \n",
+              "348         112           2011-09-29                   0   Day 0 CD3   \n",
+              "\n",
+              "    disease_state           host_age host_age_units host_body_mass_index  \\\n",
+              "0         healthy  Restricted access          years    Restricted access   \n",
+              "1         healthy  Restricted access          years    Restricted access   \n",
+              "2         healthy  Restricted access          years    Restricted access   \n",
+              "3         healthy  Restricted access          years    Restricted access   \n",
+              "4         healthy  Restricted access          years    Restricted access   \n",
+              "..            ...                ...            ...                  ...   \n",
+              "344       Pre-FMT                 61          years                 32.5   \n",
+              "345       Pre-FMT                 61          years                 32.5   \n",
+              "346       Pre-FMT                 61          years                 32.5   \n",
+              "347       Pre-FMT                 61          years                 32.5   \n",
+              "348       Pre-FMT                 61          years                 32.5   \n",
+              "\n",
+              "           host_height host_height_units host_subject_id        host_weight  \\\n",
+              "0    Restricted access                 m           Donor  Restricted access   \n",
+              "1    Restricted access                 m           Donor  Restricted access   \n",
+              "2    Restricted access                 m           Donor  Restricted access   \n",
+              "3    Restricted access                 m           Donor  Restricted access   \n",
+              "4    Restricted access                 m           Donor  Restricted access   \n",
+              "..                 ...               ...             ...                ...   \n",
+              "344              1.727                 m             CD3               97.3   \n",
+              "345              1.727                 m             CD3               97.3   \n",
+              "346              1.727                 m             CD3               97.3   \n",
+              "347              1.727                 m             CD3               97.3   \n",
+              "348              1.727                 m             CD3               97.3   \n",
+              "\n",
+              "    host_weight_units               race                sex  \n",
+              "0                  kg  Restricted access  Restricted access  \n",
+              "1                  kg  Restricted access  Restricted access  \n",
+              "2                  kg  Restricted access  Restricted access  \n",
+              "3                  kg  Restricted access  Restricted access  \n",
+              "4                  kg  Restricted access  Restricted access  \n",
+              "..                ...                ...                ...  \n",
+              "344                kg              white               male  \n",
+              "345                kg              white               male  \n",
+              "346                kg              white               male  \n",
+              "347                kg              white               male  \n",
+              "348                kg              white               male  \n",
+              "\n",
+              "[349 rows x 20 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idabundancegenusidrelativeUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
0ERR18831956.0AkkermansiaAkkermansia0.000116122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
1ERR188319517971.0PhocaeicolaPhocaeicola0.346522122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
2ERR18831955.0VeillonellaVeillonella0.000096122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
3ERR18831954741.0FaecalibacteriumFaecalibacterium0.091417122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
4ERR1883195576.0MediterraneibacterMediterraneibacter0.011107122011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
...............................................................
344ERR18832945.0StomatobaculumStomatobaculum0.0009261122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
345ERR18832943.0LancefieldellaLancefieldella0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
346ERR18832943.0TumebacillusTumebacillus0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
347ERR18832943.0TropherymaTropheryma0.0005561122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
348ERR18832942.0MogibacteriumMogibacterium0.0003701122011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
\n",
+              "
349 rows Γ— 20 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "metadata = pd.read_table(\"metadata.tsv\").rename(columns={\"id\": \"sample_id\"})\n",
+        "tax = pd.merge(tax, metadata, on=\"sample_id\")\n",
+        "tax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ok, now we want to invade our samples with C. diff. The goal is to predict susceptibility to invasion and see how disease context can influence predicted engraftment. To do this we will introduce 10% C. diff to all the samples! You can read more about this approach and its applications [here](https://www.biorxiv.org/content/10.1101/2023.04.28.538771v2)  "
+      ],
+      "metadata": {
+        "id": "ZsFgo_CRjvsr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 106,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "vHyvSZJJ-iIi",
+        "outputId": "0f934995-8bd5-4c03-ddda-491fb5d5bd4f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      sample_id     abundance                   genus                      id  \\\n",
+              "1    ERR1883195  17971.000000             Phocaeicola             Phocaeicola   \n",
+              "3    ERR1883195   4741.000000        Faecalibacterium        Faecalibacterium   \n",
+              "8    ERR1883195   6805.000000             Bacteroides             Bacteroides   \n",
+              "11   ERR1883195   3076.000000         Parabacteroides         Parabacteroides   \n",
+              "5    ERR1883195   3621.444444          Clostridioides          Clostridioides   \n",
+              "71   ERR1883207  16983.000000             Phocaeicola             Phocaeicola   \n",
+              "73   ERR1883207   3717.000000        Faecalibacterium        Faecalibacterium   \n",
+              "77   ERR1883207   2956.000000             Barnesiella             Barnesiella   \n",
+              "78   ERR1883207   6984.000000             Bacteroides             Bacteroides   \n",
+              "79   ERR1883207   2901.000000                 Blautia                 Blautia   \n",
+              "81   ERR1883207   3413.000000         Parabacteroides         Parabacteroides   \n",
+              "7    ERR1883207   4106.000000          Clostridioides          Clostridioides   \n",
+              "222  ERR1883212   8393.000000             Phocaeicola             Phocaeicola   \n",
+              "224  ERR1883212  10841.000000        Faecalibacterium        Faecalibacterium   \n",
+              "227  ERR1883212   4038.000000        Fusicatenibacter        Fusicatenibacter   \n",
+              "229  ERR1883212   3286.000000             Bacteroides             Bacteroides   \n",
+              "230  ERR1883212   3198.000000                 Blautia                 Blautia   \n",
+              "234  ERR1883212   5106.000000           Streptococcus           Streptococcus   \n",
+              "7    ERR1883212   3873.555556          Clostridioides          Clostridioides   \n",
+              "152  ERR1883214  54726.000000             Akkermansia             Akkermansia   \n",
+              "161  ERR1883214   3968.000000             Clostridium             Clostridium   \n",
+              "3    ERR1883214   6521.555556          Clostridioides          Clostridioides   \n",
+              "291  ERR1883225   3734.000000             Phocaeicola             Phocaeicola   \n",
+              "295  ERR1883225    492.000000             Barnesiella             Barnesiella   \n",
+              "296  ERR1883225   1475.000000             Bacteroides             Bacteroides   \n",
+              "299  ERR1883225    911.000000         Parabacteroides         Parabacteroides   \n",
+              "5    ERR1883225    734.666667          Clostridioides          Clostridioides   \n",
+              "174  ERR1883240  35554.000000             Akkermansia             Akkermansia   \n",
+              "176  ERR1883240  15517.000000             Veillonella             Veillonella   \n",
+              "180  ERR1883240   5555.000000           Fusobacterium           Fusobacterium   \n",
+              "4    ERR1883240   6291.777778          Clostridioides          Clostridioides   \n",
+              "188  ERR1883250  11467.000000             Akkermansia             Akkermansia   \n",
+              "190  ERR1883250   9955.000000      Mediterraneibacter      Mediterraneibacter   \n",
+              "191  ERR1883250  11923.000000  Erysipelatoclostridium  Erysipelatoclostridium   \n",
+              "193  ERR1883250  13975.000000                 Blautia                 Blautia   \n",
+              "196  ERR1883250   3282.000000        Ruthenibacterium        Ruthenibacterium   \n",
+              "6    ERR1883250   5622.444444          Clostridioides          Clostridioides   \n",
+              "321  ERR1883294    619.000000             Veillonella             Veillonella   \n",
+              "325  ERR1883294    591.000000           Fusobacterium           Fusobacterium   \n",
+              "329  ERR1883294   1866.000000             Anaerosinus             Anaerosinus   \n",
+              "330  ERR1883294    580.000000             Megasphaera             Megasphaera   \n",
+              "332  ERR1883294    734.000000             Phytobacter             Phytobacter   \n",
+              "333  ERR1883294    294.000000              Morganella              Morganella   \n",
+              "7    ERR1883294    520.444444          Clostridioides          Clostridioides   \n",
+              "\n",
+              "     relative  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "1    0.496238        12.0           2011-10-24                  26   \n",
+              "3    0.130915        12.0           2011-10-24                  26   \n",
+              "8    0.187908        12.0           2011-10-24                  26   \n",
+              "11   0.084938        12.0           2011-10-24                  26   \n",
+              "5    0.100000        12.0           2011-10-24                  26   \n",
+              "71   0.413614        24.0           2012-01-12                  44   \n",
+              "73   0.090526        24.0           2012-01-12                  44   \n",
+              "77   0.071992        24.0           2012-01-12                  44   \n",
+              "78   0.170093        24.0           2012-01-12                  44   \n",
+              "79   0.070653        24.0           2012-01-12                  44   \n",
+              "81   0.083122        24.0           2012-01-12                  44   \n",
+              "7    0.100000        24.0           2012-01-12                  44   \n",
+              "222  0.216674        30.0           2012-10-10                 135   \n",
+              "224  0.279872        30.0           2012-10-10                 135   \n",
+              "227  0.104245        30.0           2012-10-10                 135   \n",
+              "229  0.084832        30.0           2012-10-10                 135   \n",
+              "230  0.082560        30.0           2012-10-10                 135   \n",
+              "234  0.131817        30.0           2012-10-10                 135   \n",
+              "7    0.100000        30.0           2012-10-10                 135   \n",
+              "152  0.839156        32.0           2011-07-26                   0   \n",
+              "161  0.060844        32.0           2011-07-26                   0   \n",
+              "3    0.100000        32.0           2011-07-26                   0   \n",
+              "291  0.508258        43.0           2011-07-26                  54   \n",
+              "295  0.066969        43.0           2011-07-26                  54   \n",
+              "296  0.200771        43.0           2011-07-26                  54   \n",
+              "299  0.124002        43.0           2011-07-26                  54   \n",
+              "5    0.100000        43.0           2011-07-26                  54   \n",
+              "174  0.565087        58.0           2012-02-14             pre-FMT   \n",
+              "176  0.246623        58.0           2012-02-14             pre-FMT   \n",
+              "180  0.088290        58.0           2012-02-14             pre-FMT   \n",
+              "4    0.100000        58.0           2012-02-14             pre-FMT   \n",
+              "188  0.203950        68.0           2011-12-23             pre-FMT   \n",
+              "190  0.177058        68.0           2011-12-23             pre-FMT   \n",
+              "191  0.212061        68.0           2011-12-23             pre-FMT   \n",
+              "193  0.248557        68.0           2011-12-23             pre-FMT   \n",
+              "196  0.058373        68.0           2011-12-23             pre-FMT   \n",
+              "6    0.100000        68.0           2011-12-23             pre-FMT   \n",
+              "321  0.118937       112.0           2011-09-29                   0   \n",
+              "325  0.113557       112.0           2011-09-29                   0   \n",
+              "329  0.358540       112.0           2011-09-29                   0   \n",
+              "330  0.111443       112.0           2011-09-29                   0   \n",
+              "332  0.141033       112.0           2011-09-29                   0   \n",
+              "333  0.056490       112.0           2011-09-29                   0   \n",
+              "7    0.100000       112.0           2011-09-29                   0   \n",
+              "\n",
+              "      description disease_state           host_age host_age_units  \\\n",
+              "1        Donor 11       healthy  Restricted access          years   \n",
+              "3        Donor 11       healthy  Restricted access          years   \n",
+              "8        Donor 11       healthy  Restricted access          years   \n",
+              "11       Donor 11       healthy  Restricted access          years   \n",
+              "5        Donor 11       healthy  Restricted access          years   \n",
+              "71       Donor 12       healthy  Restricted access          years   \n",
+              "73       Donor 12       healthy  Restricted access          years   \n",
+              "77       Donor 12       healthy  Restricted access          years   \n",
+              "78       Donor 12       healthy  Restricted access          years   \n",
+              "79       Donor 12       healthy  Restricted access          years   \n",
+              "81       Donor 12       healthy  Restricted access          years   \n",
+              "7        Donor 12       healthy  Restricted access          years   \n",
+              "222      Donor 14       healthy  Restricted access          years   \n",
+              "224      Donor 14       healthy  Restricted access          years   \n",
+              "227      Donor 14       healthy  Restricted access          years   \n",
+              "229      Donor 14       healthy  Restricted access          years   \n",
+              "230      Donor 14       healthy  Restricted access          years   \n",
+              "234      Donor 14       healthy  Restricted access          years   \n",
+              "7        Donor 14       healthy  Restricted access          years   \n",
+              "152     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "161     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "3       Day 0 CD1       Pre-FMT                 39          years   \n",
+              "291     Donor CD1       healthy  Restricted access          years   \n",
+              "295     Donor CD1       healthy  Restricted access          years   \n",
+              "296     Donor CD1       healthy  Restricted access          years   \n",
+              "299     Donor CD1       healthy  Restricted access          years   \n",
+              "5       Donor CD1       healthy  Restricted access          years   \n",
+              "174   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "176   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "180   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "4     CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "188  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "190  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "191  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "193  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "196  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "6    CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "321     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "325     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "329     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "330     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "332     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "333     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "7       Day 0 CD3       Pre-FMT                 61          years   \n",
+              "\n",
+              "    host_body_mass_index        host_height host_height_units host_subject_id  \\\n",
+              "1      Restricted access  Restricted access                 m           Donor   \n",
+              "3      Restricted access  Restricted access                 m           Donor   \n",
+              "8      Restricted access  Restricted access                 m           Donor   \n",
+              "11     Restricted access  Restricted access                 m           Donor   \n",
+              "5      Restricted access  Restricted access                 m           Donor   \n",
+              "71     Restricted access  Restricted access                 m           Donor   \n",
+              "73     Restricted access  Restricted access                 m           Donor   \n",
+              "77     Restricted access  Restricted access                 m           Donor   \n",
+              "78     Restricted access  Restricted access                 m           Donor   \n",
+              "79     Restricted access  Restricted access                 m           Donor   \n",
+              "81     Restricted access  Restricted access                 m           Donor   \n",
+              "7      Restricted access  Restricted access                 m           Donor   \n",
+              "222    Restricted access  Restricted access                 m           Donor   \n",
+              "224    Restricted access  Restricted access                 m           Donor   \n",
+              "227    Restricted access  Restricted access                 m           Donor   \n",
+              "229    Restricted access  Restricted access                 m           Donor   \n",
+              "230    Restricted access  Restricted access                 m           Donor   \n",
+              "234    Restricted access  Restricted access                 m           Donor   \n",
+              "7      Restricted access  Restricted access                 m           Donor   \n",
+              "152                 29.3              165.1                 m             CD1   \n",
+              "161                 29.3              165.1                 m             CD1   \n",
+              "3                   29.3              165.1                 m             CD1   \n",
+              "291    Restricted access  Restricted access                 m           Donor   \n",
+              "295    Restricted access  Restricted access                 m           Donor   \n",
+              "296    Restricted access  Restricted access                 m           Donor   \n",
+              "299    Restricted access  Restricted access                 m           Donor   \n",
+              "5      Restricted access  Restricted access                 m           Donor   \n",
+              "174                 35.5               1.55                 m             CD9   \n",
+              "176                 35.5               1.55                 m             CD9   \n",
+              "180                 35.5               1.55                 m             CD9   \n",
+              "4                   35.5               1.55                 m             CD9   \n",
+              "188                 34.4               1.56                 m            CD13   \n",
+              "190                 34.4               1.56                 m            CD13   \n",
+              "191                 34.4               1.56                 m            CD13   \n",
+              "193                 34.4               1.56                 m            CD13   \n",
+              "196                 34.4               1.56                 m            CD13   \n",
+              "6                   34.4               1.56                 m            CD13   \n",
+              "321                 32.5              1.727                 m             CD3   \n",
+              "325                 32.5              1.727                 m             CD3   \n",
+              "329                 32.5              1.727                 m             CD3   \n",
+              "330                 32.5              1.727                 m             CD3   \n",
+              "332                 32.5              1.727                 m             CD3   \n",
+              "333                 32.5              1.727                 m             CD3   \n",
+              "7                   32.5              1.727                 m             CD3   \n",
+              "\n",
+              "           host_weight host_weight_units               race                sex  \n",
+              "1    Restricted access                kg  Restricted access  Restricted access  \n",
+              "3    Restricted access                kg  Restricted access  Restricted access  \n",
+              "8    Restricted access                kg  Restricted access  Restricted access  \n",
+              "11   Restricted access                kg  Restricted access  Restricted access  \n",
+              "5    Restricted access                kg  Restricted access  Restricted access  \n",
+              "71   Restricted access                kg  Restricted access  Restricted access  \n",
+              "73   Restricted access                kg  Restricted access  Restricted access  \n",
+              "77   Restricted access                kg  Restricted access  Restricted access  \n",
+              "78   Restricted access                kg  Restricted access  Restricted access  \n",
+              "79   Restricted access                kg  Restricted access  Restricted access  \n",
+              "81   Restricted access                kg  Restricted access  Restricted access  \n",
+              "7    Restricted access                kg  Restricted access  Restricted access  \n",
+              "222  Restricted access                kg  Restricted access  Restricted access  \n",
+              "224  Restricted access                kg  Restricted access  Restricted access  \n",
+              "227  Restricted access                kg  Restricted access  Restricted access  \n",
+              "229  Restricted access                kg  Restricted access  Restricted access  \n",
+              "230  Restricted access                kg  Restricted access  Restricted access  \n",
+              "234  Restricted access                kg  Restricted access  Restricted access  \n",
+              "7    Restricted access                kg  Restricted access  Restricted access  \n",
+              "152               80.1                kg              white             female  \n",
+              "161               80.1                kg              white             female  \n",
+              "3                 80.1                kg              white             female  \n",
+              "291  Restricted access                kg  Restricted access  Restricted access  \n",
+              "295  Restricted access                kg  Restricted access  Restricted access  \n",
+              "296  Restricted access                kg  Restricted access  Restricted access  \n",
+              "299  Restricted access                kg  Restricted access  Restricted access  \n",
+              "5    Restricted access                kg  Restricted access  Restricted access  \n",
+              "174               85.1                kg              white             female  \n",
+              "176               85.1                kg              white             female  \n",
+              "180               85.1                kg              white             female  \n",
+              "4                 85.1                kg              white             female  \n",
+              "188               83.9                kg              white             female  \n",
+              "190               83.9                kg              white             female  \n",
+              "191               83.9                kg              white             female  \n",
+              "193               83.9                kg              white             female  \n",
+              "196               83.9                kg              white             female  \n",
+              "6                 83.9                kg              white             female  \n",
+              "321               97.3                kg              white               male  \n",
+              "325               97.3                kg              white               male  \n",
+              "329               97.3                kg              white               male  \n",
+              "330               97.3                kg              white               male  \n",
+              "332               97.3                kg              white               male  \n",
+              "333               97.3                kg              white               male  \n",
+              "7                 97.3                kg              white               male  "
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idabundancegenusidrelativeUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
1ERR188319517971.000000PhocaeicolaPhocaeicola0.49623812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
3ERR18831954741.000000FaecalibacteriumFaecalibacterium0.13091512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
8ERR18831956805.000000BacteroidesBacteroides0.18790812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
11ERR18831953076.000000ParabacteroidesParabacteroides0.08493812.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
5ERR18831953621.444444ClostridioidesClostridioides0.10000012.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
71ERR188320716983.000000PhocaeicolaPhocaeicola0.41361424.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
73ERR18832073717.000000FaecalibacteriumFaecalibacterium0.09052624.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
77ERR18832072956.000000BarnesiellaBarnesiella0.07199224.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
78ERR18832076984.000000BacteroidesBacteroides0.17009324.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
79ERR18832072901.000000BlautiaBlautia0.07065324.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
81ERR18832073413.000000ParabacteroidesParabacteroides0.08312224.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
7ERR18832074106.000000ClostridioidesClostridioides0.10000024.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
222ERR18832128393.000000PhocaeicolaPhocaeicola0.21667430.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
224ERR188321210841.000000FaecalibacteriumFaecalibacterium0.27987230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
227ERR18832124038.000000FusicatenibacterFusicatenibacter0.10424530.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
229ERR18832123286.000000BacteroidesBacteroides0.08483230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
230ERR18832123198.000000BlautiaBlautia0.08256030.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
234ERR18832125106.000000StreptococcusStreptococcus0.13181730.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
7ERR18832123873.555556ClostridioidesClostridioides0.10000030.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
152ERR188321454726.000000AkkermansiaAkkermansia0.83915632.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
161ERR18832143968.000000ClostridiumClostridium0.06084432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
3ERR18832146521.555556ClostridioidesClostridioides0.10000032.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1mCD180.1kgwhitefemale
291ERR18832253734.000000PhocaeicolaPhocaeicola0.50825843.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
295ERR1883225492.000000BarnesiellaBarnesiella0.06696943.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
296ERR18832251475.000000BacteroidesBacteroides0.20077143.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
299ERR1883225911.000000ParabacteroidesParabacteroides0.12400243.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
5ERR1883225734.666667ClostridioidesClostridioides0.10000043.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
174ERR188324035554.000000AkkermansiaAkkermansia0.56508758.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
176ERR188324015517.000000VeillonellaVeillonella0.24662358.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
180ERR18832405555.000000FusobacteriumFusobacterium0.08829058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
4ERR18832406291.777778ClostridioidesClostridioides0.10000058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55mCD985.1kgwhitefemale
188ERR188325011467.000000AkkermansiaAkkermansia0.20395068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
190ERR18832509955.000000MediterraneibacterMediterraneibacter0.17705868.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
191ERR188325011923.000000ErysipelatoclostridiumErysipelatoclostridium0.21206168.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
193ERR188325013975.000000BlautiaBlautia0.24855768.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
196ERR18832503282.000000RuthenibacteriumRuthenibacterium0.05837368.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
6ERR18832505622.444444ClostridioidesClostridioides0.10000068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56mCD1383.9kgwhitefemale
321ERR1883294619.000000VeillonellaVeillonella0.118937112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
325ERR1883294591.000000FusobacteriumFusobacterium0.113557112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
329ERR18832941866.000000AnaerosinusAnaerosinus0.358540112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
330ERR1883294580.000000MegasphaeraMegasphaera0.111443112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
332ERR1883294734.000000PhytobacterPhytobacter0.141033112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
333ERR1883294294.000000MorganellaMorganella0.056490112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
7ERR1883294520.444444ClostridioidesClostridioides0.100000112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727mCD397.3kgwhitemale
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 106
+        }
+      ],
+      "source": [
+        "invader='Clostridioides' # choose your favorite invader, here we have chosen C. diff\n",
+        "invader_rel=0.1          # set the fraction of total relative abundance you want it to invade with\n",
+        "invaded=pd.DataFrame()   # set up results dataframe\n",
+        "\n",
+        "for smp,df in tax.groupby(by='sample_id'):  # loop through data, one sample at a time\n",
+        "  df=df[df.relative>0.05].copy()            # filter out genera below 5% (this is a high threshold, which will make simulation run faster)\n",
+        "  df=df[df.genus!='Clostridioides']         # some samples already have C. diff, so lets remove it and then re-introduce\n",
+        "  abund=df.abundance.sum()*invader_rel/(1-invader_rel)  # calculate the abundance needed to achieve desired relative abundance\n",
+        "\n",
+        "  info=df.iloc[0,:].copy()  # get necessary sample info\n",
+        "  info.genus=invader        # add invader name\n",
+        "  info.id=invader\n",
+        "  info.abundance=abund      # add invader abundance\n",
+        "  df.loc[df.shape[0]+1,info.index]=info.values  #append invader info\n",
+        "  df.relative=df.abundance.apply(lambda x:x/df.abundance.sum()) # re-calculate relative abundance\n",
+        "  invaded=pd.concat([invaded,df]) # append results to output dataframe\n",
+        "invaded\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RTbYBR8cJfup"
+      },
+      "source": [
+        "With our taxonomy table ready to go, and our metadata merged, its finally time to get to the model building! πŸŽ‰\n",
+        "\n",
+        "## Building community models\n",
+        "\n",
+        "With the data we have now, building our models is pretty easy. We just pass our taxonomy table and model database to MICOM. We will remove all taxa that make up less than 5% of the community to keep the models small and speed up this tutorial. We will also have to specify where to write the models. For simplicity, we'll run this process in parallel over two threads. It should take around 10 minutes to finish."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 99,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51,
+          "referenced_widgets": [
+            "37b0134de4bd4fe38c1afe7bc2bbdc59",
+            "bc2776a5195f4a8c8dda0e22e2fd9ea3"
+          ]
+        },
+        "id": "kDbSN71SmCZr",
+        "outputId": "e51c2208-4536-4f90-804c-442d884549bf"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "37b0134de4bd4fe38c1afe7bc2bbdc59"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:micom.logger:Less than 50% of the abundance could be matched to the model database. Model `ERR1883294` may not be representative of the sample\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from micom.workflows import build\n",
+        "from micom import Community\n",
+        "import pandas as pd\n",
+        "\n",
+        "manifest = build(invaded, \"agora103_genus.qza\", \"models\", solver=\"osqp\",\n",
+        "                 cutoff=0.05, threads=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kwya6vbZZSUo"
+      },
+      "source": [
+        "You'll see a warning pop up indicating that less than 50% of the abundances can be matched to the database for one of the samples. This can happen with some data, and may indicate the models may not be completely representative of the samples.Typically a fraction of 80% or more is considered great. We'll continue, but remember to keep an eye out for this in future projects!\n",
+        "\n",
+        "In lower-biomass 16S amplicon sequencing samples from stool, many reads can match to food components or to host mitochondria and these hits probably do not contribute much to bacterial community metabolism. These hits will be excluded from MICOM.\n",
+        "\n",
+        "Let's take a look what we got back from the `build` process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "r9qwglr88Ise",
+        "outputId": "168ee50d-23c5-4dfe-e3fb-bc733ec82188"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0  ERR1883195        12.0           2011-10-24                  26   \n",
+              "1  ERR1883207        24.0           2012-01-12                  44   \n",
+              "2  ERR1883212        30.0           2012-10-10                 135   \n",
+              "3  ERR1883214        32.0           2011-07-26                   0   \n",
+              "4  ERR1883225        43.0           2011-07-26                  54   \n",
+              "5  ERR1883240        58.0           2012-02-14             pre-FMT   \n",
+              "6  ERR1883250        68.0           2011-12-23             pre-FMT   \n",
+              "7  ERR1883294       112.0           2011-09-29                   0   \n",
+              "\n",
+              "    description disease_state           host_age host_age_units  \\\n",
+              "0      Donor 11       healthy  Restricted access          years   \n",
+              "1      Donor 12       healthy  Restricted access          years   \n",
+              "2      Donor 14       healthy  Restricted access          years   \n",
+              "3     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "4     Donor CD1       healthy  Restricted access          years   \n",
+              "5   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "6  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "7     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "\n",
+              "  host_body_mass_index        host_height  ... host_subject_id  \\\n",
+              "0    Restricted access  Restricted access  ...           Donor   \n",
+              "1    Restricted access  Restricted access  ...           Donor   \n",
+              "2    Restricted access  Restricted access  ...           Donor   \n",
+              "3                 29.3              165.1  ...             CD1   \n",
+              "4    Restricted access  Restricted access  ...           Donor   \n",
+              "5                 35.5               1.55  ...             CD9   \n",
+              "6                 34.4               1.56  ...            CD13   \n",
+              "7                 32.5              1.727  ...             CD3   \n",
+              "\n",
+              "         host_weight host_weight_units               race                sex  \\\n",
+              "0  Restricted access                kg  Restricted access  Restricted access   \n",
+              "1  Restricted access                kg  Restricted access  Restricted access   \n",
+              "2  Restricted access                kg  Restricted access  Restricted access   \n",
+              "3               80.1                kg              white             female   \n",
+              "4  Restricted access                kg  Restricted access  Restricted access   \n",
+              "5               85.1                kg              white             female   \n",
+              "6               83.9                kg              white             female   \n",
+              "7               97.3                kg              white               male   \n",
+              "\n",
+              "                file found_taxa  total_taxa  found_fraction  \\\n",
+              "0  ERR1883195.pickle        5.0         5.0        1.000000   \n",
+              "1  ERR1883207.pickle        7.0         7.0        1.000000   \n",
+              "2  ERR1883212.pickle        6.0         7.0        0.857143   \n",
+              "3  ERR1883214.pickle        6.0         6.0        1.000000   \n",
+              "4  ERR1883225.pickle        5.0         5.0        1.000000   \n",
+              "5  ERR1883240.pickle        4.0         4.0        1.000000   \n",
+              "6  ERR1883250.pickle        5.0         6.0        0.833333   \n",
+              "7  ERR1883294.pickle        5.0         7.0        0.714286   \n",
+              "\n",
+              "   found_abundance_fraction  \n",
+              "0                  1.000000  \n",
+              "1                  1.000000  \n",
+              "2                  0.895755  \n",
+              "3                  1.000000  \n",
+              "4                  1.000000  \n",
+              "5                  1.000000  \n",
+              "6                  0.941627  \n",
+              "7                  0.500427  \n",
+              "\n",
+              "[8 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_height...host_subject_idhost_weighthost_weight_unitsracesexfilefound_taxatotal_taxafound_fractionfound_abundance_fraction
0ERR188319512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883195.pickle5.05.01.0000001.000000
1ERR188320724.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883207.pickle7.07.01.0000001.000000
2ERR188321230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883212.pickle6.07.00.8571430.895755
3ERR188321432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1...CD180.1kgwhitefemaleERR1883214.pickle6.06.01.0000001.000000
4ERR188322543.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883225.pickle5.05.01.0000001.000000
5ERR188324058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55...CD985.1kgwhitefemaleERR1883240.pickle4.04.01.0000001.000000
6ERR188325068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56...CD1383.9kgwhitefemaleERR1883250.pickle5.06.00.8333330.941627
7ERR1883294112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727...CD397.3kgwhitemaleERR1883294.pickle5.07.00.7142860.500427
\n",
+              "
8 rows Γ— 21 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ],
+      "source": [
+        "manifest"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y4KAJkhIdspQ"
+      },
+      "source": [
+        "This will tell you how many taxa were found in the database and what fraction of the total abundance was represented by the database. For most samples, this looks okay (i.e., >70% of abundance represented).\n",
+        "\n",
+        "So we now have our community models and can leverage MICOM fully by simulating community growth - let's discuss what we want to look at.\n",
+        "\n",
+        "### Microbiome Context\n",
+        "\n",
+        "Now that our models are ready to go, let's think about some of the insights we might gain from these samples. First and foremost, we want to investigate the invasion potential of C. diff. How does its ability to invade vary in samples from healthy donors versus individuals with dysbiotic gut microbiomes (pre-FMT)?\n",
+        "\n",
+        "Additionally, we can use MICOM to take a mechanistic look at what metabolic strategies are leveraged by C. diff (e.g., what niche(s) does it occupy) in these different contexts.\n",
+        "\n",
+        "All that and more, coming up. Stay tuned!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "etrvjwBLkKdR"
+      },
+      "source": [
+        "First we need to import our dietary context. For simplicity we will be using a formulation that represents an average western diet, but if information about host diet is known other formulations can be used (e.g., vegetarian or vegan diet). Additional dietary formulations can be found [here]( https://github.com/micom-dev/media/tree/main/media)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "aJwXAR4PPAkA",
+        "outputId": "4397913a-b2b6-4059-e355-4226d29a0a90"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  reaction metabolite      global_id  flux\n",
+              "reaction                                                  \n",
+              "EX_4abz_m        EX_4abz_m     4abz_m     EX_4abz(e)   0.1\n",
+              "EX_4hbz_m        EX_4hbz_m     4hbz_m     EX_4hbz(e)   0.1\n",
+              "EX_ac_m            EX_ac_m       ac_m       EX_ac(e)   0.1\n",
+              "EX_acgam_m      EX_acgam_m    acgam_m    EX_acgam(e)   0.1\n",
+              "EX_ala_L_m      EX_ala_L_m    ala_L_m    EX_ala_L(e)   0.1\n",
+              "...                    ...        ...            ...   ...\n",
+              "EX_octa_m        EX_octa_m     octa_m     EX_octa(e)   0.0\n",
+              "EX_adpcbl_m    EX_adpcbl_m   adpcbl_m   EX_adpcbl(e)   0.0\n",
+              "EX_fe3dcit_m  EX_fe3dcit_m  fe3dcit_m  EX_fe3dcit(e)   0.0\n",
+              "EX_pydx5p_m    EX_pydx5p_m   pydx5p_m   EX_pydx5p(e)   0.0\n",
+              "EX_glu_D_m      EX_glu_D_m    glu_D_m    EX_glu_D(e)   0.0\n",
+              "\n",
+              "[171 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
reactionmetaboliteglobal_idflux
reaction
EX_4abz_mEX_4abz_m4abz_mEX_4abz(e)0.1
EX_4hbz_mEX_4hbz_m4hbz_mEX_4hbz(e)0.1
EX_ac_mEX_ac_mac_mEX_ac(e)0.1
EX_acgam_mEX_acgam_macgam_mEX_acgam(e)0.1
EX_ala_L_mEX_ala_L_mala_L_mEX_ala_L(e)0.1
...............
EX_octa_mEX_octa_mocta_mEX_octa(e)0.0
EX_adpcbl_mEX_adpcbl_madpcbl_mEX_adpcbl(e)0.0
EX_fe3dcit_mEX_fe3dcit_mfe3dcit_mEX_fe3dcit(e)0.0
EX_pydx5p_mEX_pydx5p_mpydx5p_mEX_pydx5p(e)0.0
EX_glu_D_mEX_glu_D_mglu_D_mEX_glu_D(e)0.0
\n",
+              "
171 rows Γ— 4 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ],
+      "source": [
+        "from micom.qiime_formats import load_qiime_medium\n",
+        "medium = load_qiime_medium(\"western_diet_gut_agora.qza\")\n",
+        "medium"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4s8R4WYUez4g"
+      },
+      "source": [
+        "### Growing the models\n",
+        "Great, now we have our media & our models, it's time to get growing. This will take some time, so we'll use that time as an opportunity to discuss more in depth what these processes do, and what to look for in the results. First, let's run the `grow()` command. This will take the models we've built, and find an optimal solution to the fluxes based upon the medium that's been applied."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3WH8VVrVS4mv"
+      },
+      "source": [
+        "If that takes too long or was aborted, we can read it in from the treasure chest."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 100,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34,
+          "referenced_widgets": [
+            "59428de9a88b4204885d03de441cbde3",
+            "901fb7e8d8c844a8b2e86735ca2bead3"
+          ]
+        },
+        "id": "IjDguZEcWGjG",
+        "outputId": "03508c5b-c7fc-46dd-c53b-bbd88dc5c236"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "59428de9a88b4204885d03de441cbde3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from micom.workflows import grow, save_results\n",
+        "\n",
+        "growth = grow(manifest, \"models\",medium, tradeoff=0.8, threads=2)\n",
+        "\n",
+        "# We'll save the results to a file\n",
+        "save_results(growth, \"growth.zip\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rHedHJxHWkjy"
+      },
+      "source": [
+        "Again, if that takes too long or was aborted, we can read it in from the treasure chest."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rcPNBkDpWGrQ"
+      },
+      "outputs": [],
+      "source": [
+        "from micom.workflows import load_results\n",
+        "\n",
+        "try:               # Will only run if the previous step failed\n",
+        "  growth\n",
+        "except NameError:\n",
+        "  growth = load_results(\"treasure_chest/growth.zip\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "69gq9QfAzqxq"
+      },
+      "source": [
+        "What kind of results did we get? Well, `grow` returns a tuple of 3 data sets:\n",
+        "\n",
+        "1. The predicted growth rate for all taxa in all samples\n",
+        "2. The import and export fluxes for each taxon and the external environment\n",
+        "3. Annotations for the fluxes mapping to other databases\n",
+        "\n",
+        "### πŸ“ˆ Growth Rates\n",
+        "\n",
+        "The growth rates are pretty straightforward."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 112,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "2r_XUm7U-HSm",
+        "outputId": "2ef89987-13a9-48a6-85b0-26dcdcf7ff92"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    abundance  growth_rate  reactions  metabolites                   taxon  \\\n",
+              "0    0.187908     0.147278       3293         1883             Bacteroides   \n",
+              "1    0.100000     0.078512       1844         1401          Clostridioides   \n",
+              "2    0.130915     0.102472       1986         1472        Faecalibacterium   \n",
+              "3    0.084938     0.066644       2879         1747         Parabacteroides   \n",
+              "4    0.496238     0.389606       2712         1656             Phocaeicola   \n",
+              "5    0.170093     0.248553       3293         1883             Bacteroides   \n",
+              "6    0.071992     0.108048       2235         1371             Barnesiella   \n",
+              "7    0.070653     0.101473       2808         1732                 Blautia   \n",
+              "8    0.100000     0.143653       1844         1401          Clostridioides   \n",
+              "9    0.090526     0.129872       1986         1472        Faecalibacterium   \n",
+              "10   0.083122     0.122119       2879         1747         Parabacteroides   \n",
+              "11   0.413614     0.607244       2712         1656             Phocaeicola   \n",
+              "12   0.759954     0.300619       2274         1386             Akkermansia   \n",
+              "13   0.100000     0.144461       1844         1401          Clostridioides   \n",
+              "14   0.055102     0.041183       3532         2001             Clostridium   \n",
+              "15   0.013039     0.009244       2826         1639  Erysipelatoclostridium   \n",
+              "16   0.035175     0.025067       1274         1138        Paeniclostridium   \n",
+              "17   0.036730     0.031724       1652         1308             Veillonella   \n",
+              "18   0.094704     0.133863       3293         1883             Bacteroides   \n",
+              "19   0.092168     0.130290       2808         1732                 Blautia   \n",
+              "20   0.111638     0.157821       1844         1401          Clostridioides   \n",
+              "21   0.312443     0.441683       1986         1472        Faecalibacterium   \n",
+              "22   0.241890     0.341913       2712         1656             Phocaeicola   \n",
+              "23   0.147157     0.208032       2860         1805           Streptococcus   \n",
+              "24   0.565087     0.128835       2274         1386             Akkermansia   \n",
+              "25   0.100000     0.377698       1844         1401          Clostridioides   \n",
+              "26   0.088290     0.323804       2197         1602           Fusobacterium   \n",
+              "27   0.246623     0.928642       1652         1308             Veillonella   \n",
+              "28   0.202085     0.267660       2274         1386             Akkermansia   \n",
+              "29   0.246283     0.412257       2808         1732                 Blautia   \n",
+              "30   0.105784     0.176446       1844         1401          Clostridioides   \n",
+              "31   0.020161     0.033840       1966         1392           Coprobacillus   \n",
+              "32   0.012706     0.023217       1187         1056               Dialister   \n",
+              "33   0.210121     0.353430       2826         1639  Erysipelatoclostridium   \n",
+              "34   0.014204     0.022333       1207         1105          Flavonifractor   \n",
+              "35   0.013217     0.021691       1369         1170             Lachnospira   \n",
+              "36   0.175438     0.295404       2681         1578      Mediterraneibacter   \n",
+              "37   0.045896     0.069084       2650         1613               Alistipes   \n",
+              "38   0.012559     0.017830       2034         1491            Anaerostipes   \n",
+              "39   0.168400     0.243903       3293         1883             Bacteroides   \n",
+              "40   0.056172     0.083090       2235         1371             Barnesiella   \n",
+              "41   0.027401     0.039184       2808         1732                 Blautia   \n",
+              "42   0.100000     0.141583       1844         1401          Clostridioides   \n",
+              "43   0.021236     0.029938       2073         1577             Eubacterium   \n",
+              "44   0.027287     0.038572       1986         1472        Faecalibacterium   \n",
+              "45   0.104009     0.151023       2879         1747         Parabacteroides   \n",
+              "46   0.010732     0.012497       1080         1019          Parasutterella   \n",
+              "47   0.426310     0.619569       2712         1656             Phocaeicola   \n",
+              "48   0.192591     0.360678       1844         1401          Clostridioides   \n",
+              "49   0.197835     0.370503       2197         1602           Fusobacterium   \n",
+              "50   0.194153     0.363601       1206         1068             Megasphaera   \n",
+              "51   0.098416     0.184309       1577         1286              Morganella   \n",
+              "52   0.025775     0.042924       1080         1019          Parasutterella   \n",
+              "53   0.084021     0.156784       2860         1805           Streptococcus   \n",
+              "54   0.207208     0.388049       1652         1308             Veillonella   \n",
+              "\n",
+              "    tradeoff   sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "1        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "2        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "3        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "4        0.8  ERR1883195          12           2011-10-24                  26   \n",
+              "5        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "6        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "7        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "8        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "9        0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "10       0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "11       0.8  ERR1883207          24           2012-01-12                  44   \n",
+              "12       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "13       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "14       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "15       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "16       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "17       0.8  ERR1883214          32           2011-07-26                   0   \n",
+              "18       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "19       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "20       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "21       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "22       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "23       0.8  ERR1883212          30           2012-10-10                 135   \n",
+              "24       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "25       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "26       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "27       0.8  ERR1883240          58           2012-02-14             pre-FMT   \n",
+              "28       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "29       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "30       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "31       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "32       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "33       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "34       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "35       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "36       0.8  ERR1883250          68           2011-12-23             pre-FMT   \n",
+              "37       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "38       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "39       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "40       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "41       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "42       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "43       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "44       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "45       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "46       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "47       0.8  ERR1883225          43           2011-07-26                  54   \n",
+              "48       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "49       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "50       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "51       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "52       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "53       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "54       0.8  ERR1883294         112           2011-09-29                   0   \n",
+              "\n",
+              "    ...           host_age host_age_units host_body_mass_index  \\\n",
+              "0   ...  Restricted access          years    Restricted access   \n",
+              "1   ...  Restricted access          years    Restricted access   \n",
+              "2   ...  Restricted access          years    Restricted access   \n",
+              "3   ...  Restricted access          years    Restricted access   \n",
+              "4   ...  Restricted access          years    Restricted access   \n",
+              "5   ...  Restricted access          years    Restricted access   \n",
+              "6   ...  Restricted access          years    Restricted access   \n",
+              "7   ...  Restricted access          years    Restricted access   \n",
+              "8   ...  Restricted access          years    Restricted access   \n",
+              "9   ...  Restricted access          years    Restricted access   \n",
+              "10  ...  Restricted access          years    Restricted access   \n",
+              "11  ...  Restricted access          years    Restricted access   \n",
+              "12  ...                 39          years                 29.3   \n",
+              "13  ...                 39          years                 29.3   \n",
+              "14  ...                 39          years                 29.3   \n",
+              "15  ...                 39          years                 29.3   \n",
+              "16  ...                 39          years                 29.3   \n",
+              "17  ...                 39          years                 29.3   \n",
+              "18  ...  Restricted access          years    Restricted access   \n",
+              "19  ...  Restricted access          years    Restricted access   \n",
+              "20  ...  Restricted access          years    Restricted access   \n",
+              "21  ...  Restricted access          years    Restricted access   \n",
+              "22  ...  Restricted access          years    Restricted access   \n",
+              "23  ...  Restricted access          years    Restricted access   \n",
+              "24  ...                 47          years                 35.5   \n",
+              "25  ...                 47          years                 35.5   \n",
+              "26  ...                 47          years                 35.5   \n",
+              "27  ...                 47          years                 35.5   \n",
+              "28  ...                 53          years                 34.4   \n",
+              "29  ...                 53          years                 34.4   \n",
+              "30  ...                 53          years                 34.4   \n",
+              "31  ...                 53          years                 34.4   \n",
+              "32  ...                 53          years                 34.4   \n",
+              "33  ...                 53          years                 34.4   \n",
+              "34  ...                 53          years                 34.4   \n",
+              "35  ...                 53          years                 34.4   \n",
+              "36  ...                 53          years                 34.4   \n",
+              "37  ...  Restricted access          years    Restricted access   \n",
+              "38  ...  Restricted access          years    Restricted access   \n",
+              "39  ...  Restricted access          years    Restricted access   \n",
+              "40  ...  Restricted access          years    Restricted access   \n",
+              "41  ...  Restricted access          years    Restricted access   \n",
+              "42  ...  Restricted access          years    Restricted access   \n",
+              "43  ...  Restricted access          years    Restricted access   \n",
+              "44  ...  Restricted access          years    Restricted access   \n",
+              "45  ...  Restricted access          years    Restricted access   \n",
+              "46  ...  Restricted access          years    Restricted access   \n",
+              "47  ...  Restricted access          years    Restricted access   \n",
+              "48  ...                 61          years                 32.5   \n",
+              "49  ...                 61          years                 32.5   \n",
+              "50  ...                 61          years                 32.5   \n",
+              "51  ...                 61          years                 32.5   \n",
+              "52  ...                 61          years                 32.5   \n",
+              "53  ...                 61          years                 32.5   \n",
+              "54  ...                 61          years                 32.5   \n",
+              "\n",
+              "          host_height host_height_units host_subject_id        host_weight  \\\n",
+              "0   Restricted access                 m           Donor  Restricted access   \n",
+              "1   Restricted access                 m           Donor  Restricted access   \n",
+              "2   Restricted access                 m           Donor  Restricted access   \n",
+              "3   Restricted access                 m           Donor  Restricted access   \n",
+              "4   Restricted access                 m           Donor  Restricted access   \n",
+              "5   Restricted access                 m           Donor  Restricted access   \n",
+              "6   Restricted access                 m           Donor  Restricted access   \n",
+              "7   Restricted access                 m           Donor  Restricted access   \n",
+              "8   Restricted access                 m           Donor  Restricted access   \n",
+              "9   Restricted access                 m           Donor  Restricted access   \n",
+              "10  Restricted access                 m           Donor  Restricted access   \n",
+              "11  Restricted access                 m           Donor  Restricted access   \n",
+              "12              165.1                 m             CD1               80.1   \n",
+              "13              165.1                 m             CD1               80.1   \n",
+              "14              165.1                 m             CD1               80.1   \n",
+              "15              165.1                 m             CD1               80.1   \n",
+              "16              165.1                 m             CD1               80.1   \n",
+              "17              165.1                 m             CD1               80.1   \n",
+              "18  Restricted access                 m           Donor  Restricted access   \n",
+              "19  Restricted access                 m           Donor  Restricted access   \n",
+              "20  Restricted access                 m           Donor  Restricted access   \n",
+              "21  Restricted access                 m           Donor  Restricted access   \n",
+              "22  Restricted access                 m           Donor  Restricted access   \n",
+              "23  Restricted access                 m           Donor  Restricted access   \n",
+              "24               1.55                 m             CD9               85.1   \n",
+              "25               1.55                 m             CD9               85.1   \n",
+              "26               1.55                 m             CD9               85.1   \n",
+              "27               1.55                 m             CD9               85.1   \n",
+              "28               1.56                 m            CD13               83.9   \n",
+              "29               1.56                 m            CD13               83.9   \n",
+              "30               1.56                 m            CD13               83.9   \n",
+              "31               1.56                 m            CD13               83.9   \n",
+              "32               1.56                 m            CD13               83.9   \n",
+              "33               1.56                 m            CD13               83.9   \n",
+              "34               1.56                 m            CD13               83.9   \n",
+              "35               1.56                 m            CD13               83.9   \n",
+              "36               1.56                 m            CD13               83.9   \n",
+              "37  Restricted access                 m           Donor  Restricted access   \n",
+              "38  Restricted access                 m           Donor  Restricted access   \n",
+              "39  Restricted access                 m           Donor  Restricted access   \n",
+              "40  Restricted access                 m           Donor  Restricted access   \n",
+              "41  Restricted access                 m           Donor  Restricted access   \n",
+              "42  Restricted access                 m           Donor  Restricted access   \n",
+              "43  Restricted access                 m           Donor  Restricted access   \n",
+              "44  Restricted access                 m           Donor  Restricted access   \n",
+              "45  Restricted access                 m           Donor  Restricted access   \n",
+              "46  Restricted access                 m           Donor  Restricted access   \n",
+              "47  Restricted access                 m           Donor  Restricted access   \n",
+              "48              1.727                 m             CD3               97.3   \n",
+              "49              1.727                 m             CD3               97.3   \n",
+              "50              1.727                 m             CD3               97.3   \n",
+              "51              1.727                 m             CD3               97.3   \n",
+              "52              1.727                 m             CD3               97.3   \n",
+              "53              1.727                 m             CD3               97.3   \n",
+              "54              1.727                 m             CD3               97.3   \n",
+              "\n",
+              "   host_weight_units               race                sex  \n",
+              "0                 kg  Restricted access  Restricted access  \n",
+              "1                 kg  Restricted access  Restricted access  \n",
+              "2                 kg  Restricted access  Restricted access  \n",
+              "3                 kg  Restricted access  Restricted access  \n",
+              "4                 kg  Restricted access  Restricted access  \n",
+              "5                 kg  Restricted access  Restricted access  \n",
+              "6                 kg  Restricted access  Restricted access  \n",
+              "7                 kg  Restricted access  Restricted access  \n",
+              "8                 kg  Restricted access  Restricted access  \n",
+              "9                 kg  Restricted access  Restricted access  \n",
+              "10                kg  Restricted access  Restricted access  \n",
+              "11                kg  Restricted access  Restricted access  \n",
+              "12                kg              white             female  \n",
+              "13                kg              white             female  \n",
+              "14                kg              white             female  \n",
+              "15                kg              white             female  \n",
+              "16                kg              white             female  \n",
+              "17                kg              white             female  \n",
+              "18                kg  Restricted access  Restricted access  \n",
+              "19                kg  Restricted access  Restricted access  \n",
+              "20                kg  Restricted access  Restricted access  \n",
+              "21                kg  Restricted access  Restricted access  \n",
+              "22                kg  Restricted access  Restricted access  \n",
+              "23                kg  Restricted access  Restricted access  \n",
+              "24                kg              white             female  \n",
+              "25                kg              white             female  \n",
+              "26                kg              white             female  \n",
+              "27                kg              white             female  \n",
+              "28                kg              white             female  \n",
+              "29                kg              white             female  \n",
+              "30                kg              white             female  \n",
+              "31                kg              white             female  \n",
+              "32                kg              white             female  \n",
+              "33                kg              white             female  \n",
+              "34                kg              white             female  \n",
+              "35                kg              white             female  \n",
+              "36                kg              white             female  \n",
+              "37                kg  Restricted access  Restricted access  \n",
+              "38                kg  Restricted access  Restricted access  \n",
+              "39                kg  Restricted access  Restricted access  \n",
+              "40                kg  Restricted access  Restricted access  \n",
+              "41                kg  Restricted access  Restricted access  \n",
+              "42                kg  Restricted access  Restricted access  \n",
+              "43                kg  Restricted access  Restricted access  \n",
+              "44                kg  Restricted access  Restricted access  \n",
+              "45                kg  Restricted access  Restricted access  \n",
+              "46                kg  Restricted access  Restricted access  \n",
+              "47                kg  Restricted access  Restricted access  \n",
+              "48                kg              white               male  \n",
+              "49                kg              white               male  \n",
+              "50                kg              white               male  \n",
+              "51                kg              white               male  \n",
+              "52                kg              white               male  \n",
+              "53                kg              white               male  \n",
+              "54                kg              white               male  \n",
+              "\n",
+              "[55 rows x 22 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
abundancegrowth_ratereactionsmetabolitestaxontradeoffsample_idUnnamed: 0collection_timestampday_relative_to_fmt...host_agehost_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesex
00.1879080.14727832931883Bacteroides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
10.1000000.07851218441401Clostridioides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
20.1309150.10247219861472Faecalibacterium0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
30.0849380.06664428791747Parabacteroides0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
40.4962380.38960627121656Phocaeicola0.8ERR1883195122011-10-2426...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
50.1700930.24855332931883Bacteroides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
60.0719920.10804822351371Barnesiella0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
70.0706530.10147328081732Blautia0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
80.1000000.14365318441401Clostridioides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
90.0905260.12987219861472Faecalibacterium0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
100.0831220.12211928791747Parabacteroides0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
110.4136140.60724427121656Phocaeicola0.8ERR1883207242012-01-1244...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
120.7599540.30061922741386Akkermansia0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
130.1000000.14446118441401Clostridioides0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
140.0551020.04118335322001Clostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
150.0130390.00924428261639Erysipelatoclostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
160.0351750.02506712741138Paeniclostridium0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
170.0367300.03172416521308Veillonella0.8ERR1883214322011-07-260...39years29.3165.1mCD180.1kgwhitefemale
180.0947040.13386332931883Bacteroides0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
190.0921680.13029028081732Blautia0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
200.1116380.15782118441401Clostridioides0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
210.3124430.44168319861472Faecalibacterium0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
220.2418900.34191327121656Phocaeicola0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
230.1471570.20803228601805Streptococcus0.8ERR1883212302012-10-10135...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
240.5650870.12883522741386Akkermansia0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
250.1000000.37769818441401Clostridioides0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
260.0882900.32380421971602Fusobacterium0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
270.2466230.92864216521308Veillonella0.8ERR1883240582012-02-14pre-FMT...47years35.51.55mCD985.1kgwhitefemale
280.2020850.26766022741386Akkermansia0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
290.2462830.41225728081732Blautia0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
300.1057840.17644618441401Clostridioides0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
310.0201610.03384019661392Coprobacillus0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
320.0127060.02321711871056Dialister0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
330.2101210.35343028261639Erysipelatoclostridium0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
340.0142040.02233312071105Flavonifractor0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
350.0132170.02169113691170Lachnospira0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
360.1754380.29540426811578Mediterraneibacter0.8ERR1883250682011-12-23pre-FMT...53years34.41.56mCD1383.9kgwhitefemale
370.0458960.06908426501613Alistipes0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
380.0125590.01783020341491Anaerostipes0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
390.1684000.24390332931883Bacteroides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
400.0561720.08309022351371Barnesiella0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
410.0274010.03918428081732Blautia0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
420.1000000.14158318441401Clostridioides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
430.0212360.02993820731577Eubacterium0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
440.0272870.03857219861472Faecalibacterium0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
450.1040090.15102328791747Parabacteroides0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
460.0107320.01249710801019Parasutterella0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
470.4263100.61956927121656Phocaeicola0.8ERR1883225432011-07-2654...Restricted accessyearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access
480.1925910.36067818441401Clostridioides0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
490.1978350.37050321971602Fusobacterium0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
500.1941530.36360112061068Megasphaera0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
510.0984160.18430915771286Morganella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
520.0257750.04292410801019Parasutterella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
530.0840210.15678428601805Streptococcus0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
540.2072080.38804916521308Veillonella0.8ERR18832941122011-09-290...61years32.51.727mCD397.3kgwhitemale
\n",
+              "
55 rows Γ— 22 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 112
+        }
+      ],
+      "source": [
+        "growth_rates=pd.merge(growth.growth_rates,metadata,on='sample_id')\n",
+        "growth_rates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F5BK7DDv0UfA"
+      },
+      "source": [
+        "### ↔️ Exchange Fluxes\n",
+        "\n",
+        "More interesting are the exchange fluxes. These reactions represent the import and export of metabolites from the system Let's look at those now:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 102,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "lQW2BBS10jdN",
+        "outputId": "d0ebb86e-549a-4727-8f9c-4461c25eeeca"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             taxon   sample_id  tolerance          reaction      flux  \\\n",
+              "0      Bacteroides  ERR1883195     0.0001    EX_MGlcn150(e) -0.001865   \n",
+              "1      Bacteroides  ERR1883195     0.0001    EX_MGlcn180(e) -0.001879   \n",
+              "4      Bacteroides  ERR1883195     0.0001  EX_MGlcn81_rl(e)  0.002123   \n",
+              "5      Bacteroides  ERR1883195     0.0001     EX_MGlcn24(e) -0.002457   \n",
+              "6      Bacteroides  ERR1883195     0.0001    EX_MGlcn165(e)  0.004506   \n",
+              "...            ...         ...        ...               ...       ...   \n",
+              "22046       medium  ERR1883294     0.0001  EX_MGlcn170_rl_m -0.000192   \n",
+              "22050       medium  ERR1883294     0.0001          EX_nmn_m -0.100040   \n",
+              "22056       medium  ERR1883294     0.0001          EX_akg_m  0.000115   \n",
+              "22070       medium  ERR1883294     0.0001          EX_oaa_m  0.000154   \n",
+              "22078       medium  ERR1883294     0.0001         EX_xtsn_m  0.000457   \n",
+              "\n",
+              "       abundance     metabolite direction  \n",
+              "0       0.208787    MGlcn150[e]    import  \n",
+              "1       0.208787    MGlcn180[e]    import  \n",
+              "4       0.208787  MGlcn81_rl[e]    export  \n",
+              "5       0.208787     MGlcn24[e]    import  \n",
+              "6       0.208787    MGlcn165[e]    export  \n",
+              "...          ...            ...       ...  \n",
+              "22046        NaN  MGlcn170_rl_m    import  \n",
+              "22050        NaN          nmn_m    import  \n",
+              "22056        NaN          akg_m    export  \n",
+              "22070        NaN          oaa_m    export  \n",
+              "22078        NaN         xtsn_m    export  \n",
+              "\n",
+              "[10597 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
taxonsample_idtolerancereactionfluxabundancemetabolitedirection
0BacteroidesERR18831950.0001EX_MGlcn150(e)-0.0018650.208787MGlcn150[e]import
1BacteroidesERR18831950.0001EX_MGlcn180(e)-0.0018790.208787MGlcn180[e]import
4BacteroidesERR18831950.0001EX_MGlcn81_rl(e)0.0021230.208787MGlcn81_rl[e]export
5BacteroidesERR18831950.0001EX_MGlcn24(e)-0.0024570.208787MGlcn24[e]import
6BacteroidesERR18831950.0001EX_MGlcn165(e)0.0045060.208787MGlcn165[e]export
...........................
22046mediumERR18832940.0001EX_MGlcn170_rl_m-0.000192NaNMGlcn170_rl_mimport
22050mediumERR18832940.0001EX_nmn_m-0.100040NaNnmn_mimport
22056mediumERR18832940.0001EX_akg_m0.000115NaNakg_mexport
22070mediumERR18832940.0001EX_oaa_m0.000154NaNoaa_mexport
22078mediumERR18832940.0001EX_xtsn_m0.000457NaNxtsn_mexport
\n",
+              "
10597 rows Γ— 8 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 102
+        }
+      ],
+      "source": [
+        "growth.exchanges"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pu5XtkUl1YG1"
+      },
+      "source": [
+        "So we see how much of each metabolite is either consumed or produced by each taxon in each sample. `tolerance` denotes the accuracy of the solver and tells you the smallest absolute flux that is likely different form zero (i.e., substantial flux). *All of the fluxes are normalized to 1g dry weight of bacteria*. So, you can directly compare fluxes between taxa, even if they are present at very different abundances.\n",
+        "\n",
+        "If you're curious what the abbreviation for each of these metabolites represents, that can be found in the annotations dataframe. For instance, let's find out what `\"tre[e]\"` represents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "DphXa9hw1yxM",
+        "outputId": "4bf58c2c-2cc4-47b0-e4af-e7e7b827e54d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          metabolite       name  molecular_weight  C_number  N_number  \\\n",
+              "reaction                                                                \n",
+              "EX_tre(e)     tre[e]  Trehalose         342.29648        12         0   \n",
+              "\n",
+              "                hmdb inchi kegg.compound pubchem.compound chebi   reaction  \n",
+              "reaction                                                                    \n",
+              "EX_tre(e)  HMDB00975   NaN        C01083             7427   NaN  EX_tre(e)  "
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
metabolitenamemolecular_weightC_numberN_numberhmdbinchikegg.compoundpubchem.compoundchebireaction
reaction
EX_tre(e)tre[e]Trehalose342.29648120HMDB00975NaNC010837427NaNEX_tre(e)
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ],
+      "source": [
+        "anns = growth.annotations\n",
+        "anns[anns.metabolite == \"tre[e]\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GVHLD2dm4a6B"
+      },
+      "source": [
+        "Trehalose! Interesting, [that's an important metabolite](https://pubmed.ncbi.nlm.nih.gov/34277467/) in the context of CDI! All of these annotations and more information at are also available at https://vmh.life, maintained by Dr. Ines Thiele's lab."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CImtzqRJbbGj"
+      },
+      "source": [
+        "\n",
+        "# πŸ“Š Visualizations\n",
+        "\n",
+        "Let's visualize our results. Because of the rich output of these models, it can be overwhelming to represent it all, but don't worry! There are tools in place for this already.\n",
+        "\n",
+        "We will use the standard visualizations included in MICOM. These tools take in the growth results we obtained before and create visualizations in standalone HTML files that bundle the plots and raw data and can be viewed directly in your browser.\n",
+        "\n",
+        "First, let's look at the growth rates of each taxon across samples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "EaplMHFLcMT7"
+      },
+      "outputs": [],
+      "source": [
+        "from micom.viz import *\n",
+        "\n",
+        "viz = plot_growth(growth, filename=\"growthrates.html\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Normally, we could call `viz.view()` afterwards and it would open it in our web browser. However, this will not work in Colab. However, the plot function creates the file `growth_rates_[DATE].html` in your `materials` folder. To open it, simply download that file and view it in your web browser. We can see that there are many things going on, but it's not super clear. Let's continue."
+      ],
+      "metadata": {
+        "id": "_sjNIuCSXkyb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We're interested in understanding the invasion potential of C. diff so lets extract the predicted C. diff growth rates. In addition to C. diff growth rate we can also look at what fraction of the community growth rate this represents.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qPTCyw-7RqFm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 121,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 436
+        },
+        "id": "9YlWytSp-yKI",
+        "outputId": "c77dda37-1dd1-4ccf-a99c-cdb1519c5e8a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    abundance  growth_rate  reactions  metabolites           taxon  tradeoff  \\\n",
+              "1    0.100000     0.078512       1844         1401  Clostridioides       0.8   \n",
+              "8    0.100000     0.143653       1844         1401  Clostridioides       0.8   \n",
+              "13   0.100000     0.144461       1844         1401  Clostridioides       0.8   \n",
+              "20   0.111638     0.157821       1844         1401  Clostridioides       0.8   \n",
+              "25   0.100000     0.377698       1844         1401  Clostridioides       0.8   \n",
+              "30   0.105784     0.176446       1844         1401  Clostridioides       0.8   \n",
+              "42   0.100000     0.141583       1844         1401  Clostridioides       0.8   \n",
+              "48   0.192591     0.360678       1844         1401  Clostridioides       0.8   \n",
+              "\n",
+              "     sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  ...  \\\n",
+              "1   ERR1883195          12           2011-10-24                  26  ...   \n",
+              "8   ERR1883207          24           2012-01-12                  44  ...   \n",
+              "13  ERR1883214          32           2011-07-26                   0  ...   \n",
+              "20  ERR1883212          30           2012-10-10                 135  ...   \n",
+              "25  ERR1883240          58           2012-02-14             pre-FMT  ...   \n",
+              "30  ERR1883250          68           2011-12-23             pre-FMT  ...   \n",
+              "42  ERR1883225          43           2011-07-26                  54  ...   \n",
+              "48  ERR1883294         112           2011-09-29                   0  ...   \n",
+              "\n",
+              "   host_age_units host_body_mass_index        host_height host_height_units  \\\n",
+              "1           years    Restricted access  Restricted access                 m   \n",
+              "8           years    Restricted access  Restricted access                 m   \n",
+              "13          years                 29.3              165.1                 m   \n",
+              "20          years    Restricted access  Restricted access                 m   \n",
+              "25          years                 35.5               1.55                 m   \n",
+              "30          years                 34.4               1.56                 m   \n",
+              "42          years    Restricted access  Restricted access                 m   \n",
+              "48          years                 32.5              1.727                 m   \n",
+              "\n",
+              "   host_subject_id        host_weight host_weight_units               race  \\\n",
+              "1            Donor  Restricted access                kg  Restricted access   \n",
+              "8            Donor  Restricted access                kg  Restricted access   \n",
+              "13             CD1               80.1                kg              white   \n",
+              "20           Donor  Restricted access                kg  Restricted access   \n",
+              "25             CD9               85.1                kg              white   \n",
+              "30            CD13               83.9                kg              white   \n",
+              "42           Donor  Restricted access                kg  Restricted access   \n",
+              "48             CD3               97.3                kg              white   \n",
+              "\n",
+              "                  sex abundance_weighted_growth_rate  \n",
+              "1   Restricted access                       0.007851  \n",
+              "8   Restricted access                       0.014365  \n",
+              "13             female                       0.014446  \n",
+              "20  Restricted access                       0.017619  \n",
+              "25             female                       0.037770  \n",
+              "30             female                       0.018665  \n",
+              "42  Restricted access                       0.014158  \n",
+              "48               male                       0.069463  \n",
+              "\n",
+              "[8 rows x 23 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
abundancegrowth_ratereactionsmetabolitestaxontradeoffsample_idUnnamed: 0collection_timestampday_relative_to_fmt...host_age_unitshost_body_mass_indexhost_heighthost_height_unitshost_subject_idhost_weighthost_weight_unitsracesexabundance_weighted_growth_rate
10.1000000.07851218441401Clostridioides0.8ERR1883195122011-10-2426...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.007851
80.1000000.14365318441401Clostridioides0.8ERR1883207242012-01-1244...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.014365
130.1000000.14446118441401Clostridioides0.8ERR1883214322011-07-260...years29.3165.1mCD180.1kgwhitefemale0.014446
200.1116380.15782118441401Clostridioides0.8ERR1883212302012-10-10135...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.017619
250.1000000.37769818441401Clostridioides0.8ERR1883240582012-02-14pre-FMT...years35.51.55mCD985.1kgwhitefemale0.037770
300.1057840.17644618441401Clostridioides0.8ERR1883250682011-12-23pre-FMT...years34.41.56mCD1383.9kgwhitefemale0.018665
420.1000000.14158318441401Clostridioides0.8ERR1883225432011-07-2654...yearsRestricted accessRestricted accessmDonorRestricted accesskgRestricted accessRestricted access0.014158
480.1925910.36067818441401Clostridioides0.8ERR18832941122011-09-290...years32.51.727mCD397.3kgwhitemale0.069463
\n",
+              "
8 rows Γ— 23 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 121
+        }
+      ],
+      "source": [
+        "cdiff = growth_rates[growth_rates.taxon=='Clostridioides'].copy()\n",
+        "cdiff"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we've extracted the C. diff specific growth rates lets take a look at how they compare between patients with healthy and disbiotic gut microbiomes"
+      ],
+      "metadata": {
+        "id": "bQKYxSZTSPk1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 122,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "AWG3U094-_OI",
+        "outputId": "ef7e9ff0-9348-48ba-af7f-d05c65ad91d6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 122
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "sns.boxplot(x='disease_state',y='growth_rate',data=cdiff)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Looks like C. diff is predicted to grow in all samples but its predicted growth rate is ~2x higher in the Pre-FMT samples. You can see there is also a decent amount of variation in the Pre-FMT results."
+      ],
+      "metadata": {
+        "id": "MEuym_PVui-k"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G1JbbKrLcVye"
+      },
+      "source": [
+        "## Growth niches\n",
+        "\n",
+        "Another thing we can look at is whether individual taxa inhabit different growth niches across different disease contexts. Here we can use the `plot_exchanges_per_taxon` function to see how exchanges differ within and between taxa, within and across human populations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "id": "NlZrfv38esj8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6df836fd-71ac-4cfd-fd1c-883e9d31f311"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ],
+      "source": [
+        "plot_exchanges_per_taxon(growth, perplexity=4, direction=\"import\", filename=\"niche.html\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qXnbUCCs2yVG"
+      },
+      "source": [
+        "\n",
+        "This function projects the full set of import or export fluxes onto a two dimensional plane, and arranges taxa so that more similar flux patterns lie nearer together. Taxa closer to one another compete for a more similar set of resources (and/or produce a more similar set of metabolites). The center of the plot signifies a more competitive nutrient space, whereas clusters on the outskirts denote more isolated niches.\n",
+        "\n",
+        "You can tune [TSNE parameters](https://distill.pub/2016/misread-tsne/), such as perplexity, to get a more meaningful grouping. We will lower the perplexity here since we don't have a lot of data points.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "One small take away from this analysis is the speration between C. diff pre- and post- FMT samples, suggesting that C. diff may leverage different metabolic strategies in these contexts. Lets take a closer look at this..."
+      ],
+      "metadata": {
+        "id": "weoSaLDPZYAK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4Y_XfHkB4sO8"
+      },
+      "source": [
+        "## Comparative Metabolomics\n",
+        "\n",
+        "Now let's compare the metabolomic imports between the two disease contexts. We're interested to see how the metabolomic profile of the microbiome changes when the disease state changes, as changes in microbiome context can lead to changes in host succeptibility to infection.  To look into this deeper, we'll transform the microbiome import data and then plot the metabolite exports on a heatmap."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pAtec3I78DJJ"
+      },
+      "source": [
+        "We can use the `consumption_rates` function in MICOM to calculate consumption rates from the growth results. This will tell us what the patient microbiomes are consuming and provide additional insight into available niches. To visualize the results we'll  run a centered log ratio transformation on the data, to account for the compositional nature of these data and compare all the fluxes against each other. Importantly, here we consider the consumption rates for samples with no C. diff present to understand how the initial state of the patient microbiomes may influence invasion potential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 103,
+      "metadata": {
+        "id": "DJAsxjnFdajN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 486
+        },
+        "outputId": "59b69294-cdef-4fdf-a5fa-5f1d8cab1cde"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "disease_state    Pre-FMT                                     healthy  \\\n",
+              "sample_id     ERR1883214 ERR1883240 ERR1883250 ERR1883294 ERR1883195   \n",
+              "name                                                                   \n",
+              "D-glucose      -3.253101  -3.564477  -2.064038  -3.972573   4.450353   \n",
+              "acetate         2.572101   3.594739   4.306373   4.539524  -2.642172   \n",
+              "Water           2.966225  -3.584166   3.043172  -3.972573   4.497614   \n",
+              "D-Fructose      3.219760  -3.584166  -0.695070  -3.972573   3.268810   \n",
+              "pyruvate       -3.404889   3.505223  -3.497278   3.022735  -2.642172   \n",
+              "...                  ...        ...        ...        ...        ...   \n",
+              "Thiamin        -3.404889  -2.773089  -2.982110  -3.403379  -2.388275   \n",
+              "Spermidine     -3.404889  -2.445104  -2.992604  -3.250518  -2.374584   \n",
+              "Fe2+           -2.838896  -2.091063  -2.203376  -2.370595  -1.778976   \n",
+              "Putrescine     -3.404889  -2.848543  -3.087577  -3.295817  -2.353273   \n",
+              "Menaquinone 8  -2.873638  -3.295815  -2.979719  -3.351557  -2.276149   \n",
+              "\n",
+              "disease_state                                   \n",
+              "sample_id     ERR1883207 ERR1883212 ERR1883225  \n",
+              "name                                            \n",
+              "D-glucose       4.493531   3.416515   4.569671  \n",
+              "acetate        -2.772490  -3.668369  -2.625105  \n",
+              "Water           4.280483   3.763363   4.354428  \n",
+              "D-Fructose      3.253561   2.612354   3.276231  \n",
+              "pyruvate       -2.772490  -3.668369  -2.625105  \n",
+              "...                  ...        ...        ...  \n",
+              "Thiamin        -2.444364  -2.939826  -2.378889  \n",
+              "Spermidine     -2.445049  -2.936479  -2.376260  \n",
+              "Fe2+           -1.820472  -2.611296  -1.668659  \n",
+              "Putrescine     -2.438953  -2.912851  -2.400161  \n",
+              "Menaquinone 8  -2.471199  -3.185775  -2.625105  \n",
+              "\n",
+              "[647 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
disease_statePre-FMThealthy
sample_idERR1883214ERR1883240ERR1883250ERR1883294ERR1883195ERR1883207ERR1883212ERR1883225
name
D-glucose-3.253101-3.564477-2.064038-3.9725734.4503534.4935313.4165154.569671
acetate2.5721013.5947394.3063734.539524-2.642172-2.772490-3.668369-2.625105
Water2.966225-3.5841663.043172-3.9725734.4976144.2804833.7633634.354428
D-Fructose3.219760-3.584166-0.695070-3.9725733.2688103.2535612.6123543.276231
pyruvate-3.4048893.505223-3.4972783.022735-2.642172-2.772490-3.668369-2.625105
...........................
Thiamin-3.404889-2.773089-2.982110-3.403379-2.388275-2.444364-2.939826-2.378889
Spermidine-3.404889-2.445104-2.992604-3.250518-2.374584-2.445049-2.936479-2.376260
Fe2+-2.838896-2.091063-2.203376-2.370595-1.778976-1.820472-2.611296-1.668659
Putrescine-3.404889-2.848543-3.087577-3.295817-2.353273-2.438953-2.912851-2.400161
Menaquinone 8-2.873638-3.295815-2.979719-3.351557-2.276149-2.471199-3.185775-2.625105
\n",
+              "
647 rows Γ— 8 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 103
+        }
+      ],
+      "source": [
+        "from micom.measures import consumption_rates\n",
+        "import numpy as np\n",
+        "no_cdiff_growth = load_results(\"treasure_chest/no-cdiff-growth.zip\")  # Load growth results with no C. diff invasion\n",
+        "exchanges = consumption_rates(no_cdiff_growth)         # extract consumption rates\n",
+        "exchanges=pd.merge(exchanges,metadata,on='sample_id')  # add metadata\n",
+        "exchanges = pd.pivot_table(                            # convert to a matrix of samples vs. metabolites\n",
+        "    exchanges,                                         # that contains the production rates\n",
+        "    index = ['disease_state','sample_id'],\n",
+        "    columns = 'name',\n",
+        "    values = 'flux'\n",
+        ")\n",
+        "exchanges = exchanges.T.fillna(0.0)                    # if a metabolite is not produced its flux is zero\n",
+        "exchanges = exchanges.apply(                           # ...and a CLR transform again, normalizes the fluxes\n",
+        "    lambda xs: np.log(xs + 0.001) - np.log(xs.mean() + 0.001),\n",
+        "    axis=0)\n",
+        "exchanges = exchanges.reindex(                         # sort by variance, highest variance fluxes first\n",
+        "    exchanges.var(axis = 1).sort_values(ascending=False).index\n",
+        ")\n",
+        "\n",
+        "exchanges"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nYuc7Wu38nYd"
+      },
+      "source": [
+        "We can use seaborn to plot our heatmap:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 104,
+      "metadata": {
+        "id": "YHnKeFuF3qAt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "06c5b995-06e9-48ca-fd7e-25e5998af6e0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 104
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "import numpy as np\n",
+        "\n",
+        "sns.clustermap(\n",
+        "    exchanges.head(50),  # take 50 highest fluxes\n",
+        "    cmap = 'viridis',\n",
+        "    yticklabels = True,  # show all metabolite names\n",
+        "    figsize = (8, 12)    # size of the heatmap\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1i2LStZiUst"
+      },
+      "source": [
+        "We can see here that the disease context is important - there are significant differences in consumption rates between the healthy and pre-fmt microbiomes. These differences may why C. diff can exploit the pre-FMT microbiomes and achieve higher predicted growth rates."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l8maOr3w2bOo"
+      },
+      "source": [
+        "# 🏫 Exercises"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Time for you to try your hand at some analysis, lets take a closer looks at the metabolic strategies used by C. diff"
+      ],
+      "metadata": {
+        "id": "XZt3ojssnCzz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VjUustQG2_bX"
+      },
+      "source": [
+        "## Metabolic strategies used by C. diff\n",
+        "We've alread looked at the community wide consumption fluxes in the absencence of C. diff and found that they differ between disease contexts. What about the import fluxes of C. diff specifically? Can you develop a visualization to look at those?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exchanges=growth.exchanges                                           # extract exchanges from growth data\n",
+        "cdiff_exchanges=exchanges[exchanges.taxon=='Clostridioides']         # get cdiff specific exchanges\n",
+        "cdiff_imports=cdiff_exchanges[cdiff_exchanges.direction=='import']   # specifically look at imports\n",
+        "cdiff_imports=pd.merge(cdiff_imports,metadata,on='sample_id')        # add metadata\n",
+        "cdiff_imports = pd.pivot_table(                                      # convert to a matrix of samples vs. metabolites\n",
+        "    cdiff_imports,                                                   # that contains the production rates\n",
+        "    index = ['disease_state','sample_id'],\n",
+        "    columns = 'reaction',\n",
+        "    values = 'flux'\n",
+        ")\n",
+        "cdiff_imports = abs(cdiff_imports.T.fillna(0.0))                     # fill nans with 0s\n",
+        "\n",
+        "annot=growth.annotations                                             # optionally map reactions to metabolite names\n",
+        "annot.index=annot.reaction\n",
+        "cdiff_imports.index=cdiff_imports.index.map(annot.name.to_dict())    # not necessary but makes results more human readible\n",
+        "\n",
+        "cdiff_imports = cdiff_imports.apply(                                 # ...and a CLR transform again, normalizes the fluxes\n",
+        "    lambda xs: np.log(xs + 0.001) - np.log(xs.mean() + 0.001),\n",
+        "    axis=0)\n",
+        "cdiff_imports = cdiff_imports.reindex(                               # sort by variance, highest variance fluxes first\n",
+        "    cdiff_imports.var(axis = 1).sort_values(ascending=False).index\n",
+        ")\n",
+        "\n",
+        "cdiff_imports\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 486
+        },
+        "id": "eIyRahJbn4hc",
+        "outputId": "65c9debc-66ea-44bb-f725-dc18d3df66af"
+      },
+      "execution_count": 147,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "disease_state              Pre-FMT                                   \\\n",
+              "sample_id               ERR1883214 ERR1883240 ERR1883250 ERR1883294   \n",
+              "reaction                                                              \n",
+              "D-Mannitol               -5.804770  -4.420214  -1.034209  -4.012576   \n",
+              "D-glucose                -5.804770  -4.420214  -3.732151  -4.012576   \n",
+              "L-aspartate(1-)          -5.804770  -4.420214   0.734590   3.142715   \n",
+              "Formate                   3.603973  -4.420214  -3.965938  -4.012576   \n",
+              "(R)-3-hydroxybutyrate    -5.804770   3.427428  -2.999371   2.600317   \n",
+              "...                            ...        ...        ...        ...   \n",
+              "Co2+                     -5.032875  -3.060466  -3.113042  -2.668395   \n",
+              "O2                       -5.566462  -4.232652  -3.799558  -3.812628   \n",
+              "Hexadecanoate (n-C16:0)  -3.015255  -4.162366  -3.965938  -4.012576   \n",
+              "Hypoxanthine             -0.457435  -0.330558   0.006263   0.048169   \n",
+              "L-valine                  0.714780   0.474769   0.203827   0.859942   \n",
+              "\n",
+              "disease_state              healthy                                   \n",
+              "sample_id               ERR1883195 ERR1883207 ERR1883212 ERR1883225  \n",
+              "reaction                                                             \n",
+              "D-Mannitol                3.467234   3.488661   3.020506   3.115883  \n",
+              "D-glucose                 3.013143   3.070334   2.663872   2.791790  \n",
+              "L-aspartate(1-)           1.986893   1.851587   3.110281  -3.445003  \n",
+              "Formate                   1.981723   1.819535  -3.348525   2.981770  \n",
+              "(R)-3-hydroxybutyrate    -2.852466  -3.347560   1.757644  -3.445003  \n",
+              "...                            ...        ...        ...        ...  \n",
+              "Co2+                     -2.374232  -2.584710  -2.540125  -2.703519  \n",
+              "O2                       -2.852466  -3.347560  -3.348525  -3.445003  \n",
+              "Hexadecanoate (n-C16:0)  -2.852466  -3.347560  -3.348525  -3.445003  \n",
+              "Hypoxanthine             -0.257878  -0.179446  -0.093329   0.309034  \n",
+              "L-valine                  0.526749   0.618198   0.705706   0.547314  \n",
+              "\n",
+              "[237 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
disease_statePre-FMThealthy
sample_idERR1883214ERR1883240ERR1883250ERR1883294ERR1883195ERR1883207ERR1883212ERR1883225
reaction
D-Mannitol-5.804770-4.420214-1.034209-4.0125763.4672343.4886613.0205063.115883
D-glucose-5.804770-4.420214-3.732151-4.0125763.0131433.0703342.6638722.791790
L-aspartate(1-)-5.804770-4.4202140.7345903.1427151.9868931.8515873.110281-3.445003
Formate3.603973-4.420214-3.965938-4.0125761.9817231.819535-3.3485252.981770
(R)-3-hydroxybutyrate-5.8047703.427428-2.9993712.600317-2.852466-3.3475601.757644-3.445003
...........................
Co2+-5.032875-3.060466-3.113042-2.668395-2.374232-2.584710-2.540125-2.703519
O2-5.566462-4.232652-3.799558-3.812628-2.852466-3.347560-3.348525-3.445003
Hexadecanoate (n-C16:0)-3.015255-4.162366-3.965938-4.012576-2.852466-3.347560-3.348525-3.445003
Hypoxanthine-0.457435-0.3305580.0062630.048169-0.257878-0.179446-0.0933290.309034
L-valine0.7147800.4747690.2038270.8599420.5267490.6181980.7057060.547314
\n",
+              "
237 rows Γ— 8 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 147
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.clustermap(\n",
+        "    cdiff_imports.head(50),  # take 50 highest fluxes\n",
+        "    cmap = 'viridis',\n",
+        "    yticklabels = True,  # show all metabolite names\n",
+        "    figsize = (8, 12)    # size of the heatmap\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "vWVMA7o1pGGD",
+        "outputId": "05feed82-9fd4-49c6-f9b7-b1ca5e726446"
+      },
+      "execution_count": 148,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 148
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CsqIRTbC7doD"
+      },
+      "source": [
+        "# πŸ”΅ Addendum\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hycoXNTi5xsH"
+      },
+      "source": [
+        "## Choosing a tradeoff value\n",
+        "\n",
+        "Even if you don't have growth rates available you can still use your data to choose a decent tradeoff value. This can be done by choosing the largest tradeoff value that still allows growth for the majority of the taxa that you observed in the sample (if they are present at an appreciable abundance, they should be able to grow). This can be done with the `tradeoff` workflow in MICOM that will run cooperative tradeoff with varying tradeoff values, which can be visualized with the `plot_tradeoff` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "manifest"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "I8NnkbnymxoE",
+        "outputId": "41feea98-0cfb-4fe5-c0d6-3fa11631782e"
+      },
+      "execution_count": 123,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    sample_id  Unnamed: 0 collection_timestamp day_relative_to_fmt  \\\n",
+              "0  ERR1883195        12.0           2011-10-24                  26   \n",
+              "1  ERR1883207        24.0           2012-01-12                  44   \n",
+              "2  ERR1883212        30.0           2012-10-10                 135   \n",
+              "3  ERR1883214        32.0           2011-07-26                   0   \n",
+              "4  ERR1883225        43.0           2011-07-26                  54   \n",
+              "5  ERR1883240        58.0           2012-02-14             pre-FMT   \n",
+              "6  ERR1883250        68.0           2011-12-23             pre-FMT   \n",
+              "7  ERR1883294       112.0           2011-09-29                   0   \n",
+              "\n",
+              "    description disease_state           host_age host_age_units  \\\n",
+              "0      Donor 11       healthy  Restricted access          years   \n",
+              "1      Donor 12       healthy  Restricted access          years   \n",
+              "2      Donor 14       healthy  Restricted access          years   \n",
+              "3     Day 0 CD1       Pre-FMT                 39          years   \n",
+              "4     Donor CD1       healthy  Restricted access          years   \n",
+              "5   CD9 pre-FMT       Pre-FMT                 47          years   \n",
+              "6  CD13 pre-FMT       Pre-FMT                 53          years   \n",
+              "7     Day 0 CD3       Pre-FMT                 61          years   \n",
+              "\n",
+              "  host_body_mass_index        host_height  ... host_subject_id  \\\n",
+              "0    Restricted access  Restricted access  ...           Donor   \n",
+              "1    Restricted access  Restricted access  ...           Donor   \n",
+              "2    Restricted access  Restricted access  ...           Donor   \n",
+              "3                 29.3              165.1  ...             CD1   \n",
+              "4    Restricted access  Restricted access  ...           Donor   \n",
+              "5                 35.5               1.55  ...             CD9   \n",
+              "6                 34.4               1.56  ...            CD13   \n",
+              "7                 32.5              1.727  ...             CD3   \n",
+              "\n",
+              "         host_weight host_weight_units               race                sex  \\\n",
+              "0  Restricted access                kg  Restricted access  Restricted access   \n",
+              "1  Restricted access                kg  Restricted access  Restricted access   \n",
+              "2  Restricted access                kg  Restricted access  Restricted access   \n",
+              "3               80.1                kg              white             female   \n",
+              "4  Restricted access                kg  Restricted access  Restricted access   \n",
+              "5               85.1                kg              white             female   \n",
+              "6               83.9                kg              white             female   \n",
+              "7               97.3                kg              white               male   \n",
+              "\n",
+              "                file found_taxa  total_taxa  found_fraction  \\\n",
+              "0  ERR1883195.pickle        4.0         4.0        1.000000   \n",
+              "1  ERR1883207.pickle        6.0         6.0        1.000000   \n",
+              "2  ERR1883212.pickle        5.0         6.0        0.833333   \n",
+              "3  ERR1883214.pickle        2.0         2.0        1.000000   \n",
+              "4  ERR1883225.pickle        4.0         4.0        1.000000   \n",
+              "5  ERR1883240.pickle        3.0         3.0        1.000000   \n",
+              "6  ERR1883250.pickle        4.0         5.0        0.800000   \n",
+              "7  ERR1883294.pickle        4.0         6.0        0.666667   \n",
+              "\n",
+              "   found_abundance_fraction  \n",
+              "0                  1.000000  \n",
+              "1                  1.000000  \n",
+              "2                  0.884172  \n",
+              "3                  1.000000  \n",
+              "4                  1.000000  \n",
+              "5                  1.000000  \n",
+              "6                  0.935141  \n",
+              "7                  0.444919  \n",
+              "\n",
+              "[8 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  
\n",
+              "    
\n",
+              "\n",
+              "\n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "  \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "    \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "      \n",
+              "    \n",
+              "  \n",
+              "
sample_idUnnamed: 0collection_timestampday_relative_to_fmtdescriptiondisease_statehost_agehost_age_unitshost_body_mass_indexhost_height...host_subject_idhost_weighthost_weight_unitsracesexfilefound_taxatotal_taxafound_fractionfound_abundance_fraction
0ERR188319512.02011-10-2426Donor 11healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883195.pickle4.04.01.0000001.000000
1ERR188320724.02012-01-1244Donor 12healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883207.pickle6.06.01.0000001.000000
2ERR188321230.02012-10-10135Donor 14healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883212.pickle5.06.00.8333330.884172
3ERR188321432.02011-07-260Day 0 CD1Pre-FMT39years29.3165.1...CD180.1kgwhitefemaleERR1883214.pickle2.02.01.0000001.000000
4ERR188322543.02011-07-2654Donor CD1healthyRestricted accessyearsRestricted accessRestricted access...DonorRestricted accesskgRestricted accessRestricted accessERR1883225.pickle4.04.01.0000001.000000
5ERR188324058.02012-02-14pre-FMTCD9 pre-FMTPre-FMT47years35.51.55...CD985.1kgwhitefemaleERR1883240.pickle3.03.01.0000001.000000
6ERR188325068.02011-12-23pre-FMTCD13 pre-FMTPre-FMT53years34.41.56...CD1383.9kgwhitefemaleERR1883250.pickle4.05.00.8000000.935141
7ERR1883294112.02011-09-290Day 0 CD3Pre-FMT61years32.51.727...CD397.3kgwhitemaleERR1883294.pickle4.06.00.6666670.444919
\n",
+              "
8 rows Γ— 21 columns
\n",
+              "
\n",
+              "    
\n",
+              "\n",
+              "  
\n",
+              "    \n",
+              "\n",
+              "  \n",
+              "\n",
+              "    \n",
+              "  
\n",
+              "\n",
+              "\n",
+              "
\n",
+              "  \n",
+              "\n",
+              "\n",
+              "\n",
+              "  \n",
+              "
\n",
+              "    
\n",
+              "  
\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 123
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 149,
+      "metadata": {
+        "id": "8_1jesZTHYra",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51,
+          "referenced_widgets": [
+            "d450a2f0b9de425990f12bc7a5631782",
+            "af7b0c394a064469a6f7e8216b197336"
+          ]
+        },
+        "outputId": "b86ed405-c9b6-499c-b124-0915e21af8fa"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d450a2f0b9de425990f12bc7a5631782"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "metadata": {},
+          "execution_count": 149
+        }
+      ],
+      "source": [
+        "from micom.workflows import tradeoff\n",
+        "import micom\n",
+        "\n",
+        "tradeoff_results = tradeoff(manifest, \"models\", medium, threads=2)\n",
+        "tradeoff_results.to_csv(\"tradeoff.csv\", index=False)\n",
+        "\n",
+        "plot_tradeoff(tradeoff_results, tolerance=1e-4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y9703vhK6d6c"
+      },
+      "source": [
+        "After opeing `tradeoff_[DATE].html` you will see that, for our example here, all tradeoff values work great. This is because we modeled very few taxa, which keeps the compettion down. If you would allow for fewer abundant taxa in the models, this would change drastically. For instance, here is an example from a colorectal cancer data set:\n",
+        "\n",
+        "[![tradeoff example](https://micom-dev.github.io/micom/_images/tradeoff.png)](https://micom-dev.github.io/micom/_static/tradeoff.html)\n",
+        "\n",
+        "You can see how not using the cooperative tradeoff would give you nonsense results where only 10% of all observed taxa grew. A tradeoff value of 0.6-0.8 would probably be a good choice for this particular data set."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "c991a7ed881363492957ff225bb30af9d5174cd8515a21cbef71fcaa303e4050"
+      }
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "37b0134de4bd4fe38c1afe7bc2bbdc59": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_bc2776a5195f4a8c8dda0e22e2fd9ea3",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:05:08\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:05:08\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "bc2776a5195f4a8c8dda0e22e2fd9ea3": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "59428de9a88b4204885d03de441cbde3": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_901fb7e8d8c844a8b2e86735ca2bead3",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:09:32\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:09:32\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "901fb7e8d8c844a8b2e86735ca2bead3": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d450a2f0b9de425990f12bc7a5631782": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_af7b0c394a064469a6f7e8216b197336",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:32:42\u001b[0m\n",
+                  "text/html": "
Running ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:32:42\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "af7b0c394a064469a6f7e8216b197336": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file