examples/03-kinetic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b80f6b69",
   "metadata": {},
   "source": [
    "# Kinetic Models in MEWpy\n",
    "\n",
    "Author: Vitor Pereira. \n",
    "\n",
    "License: [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)\n",
    "\n",
    "-------\n",
    "\n",
    "In this tutorial:\n",
    "\n",
    "- You will learn how you may use MEWpy to simulate and run optimization on SBML kinetic models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6891de10",
   "metadata": {},
   "source": [
    "## Checking and setting the ODE solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f322f7ec",
   "metadata": {},
   "source": [
    "MEWpy allows using distinct ODE solvers (presently  MEWpy offers interfaces for **scipy**, **scikits-ode** and **odespy**). \n",
    "\n",
    "For stiff equations please consider installing [scikits.ode](https://scikits-odes.readthedocs.io/en/latest/) and [Sundials](https://computing.llnl.gov/projects/sundials).\n",
    "\n",
    "You may want to start by checking the ODE solvers available in the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "116fa03d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mewpy.solvers import ode_solvers, set_default_ode_solver, get_default_ode_solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10cd3573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scikits', 'scipy']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ode_solvers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4aa240",
   "metadata": {},
   "source": [
    "If more than one solver is present, you may check the one used by default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efa7102f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'scikits'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_default_ode_solver()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ace22c",
   "metadata": {},
   "source": [
    "And change it if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f13eafb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set_default_ode_solver('scipy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93df8e93",
   "metadata": {},
   "source": [
    "## Loading and browsing an SBML model\n",
    "\n",
    "\n",
    "Start by loading the SBML kinetic model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1bc7fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mewpy.io.sbml import load_ODEModel\n",
    "model = load_ODEModel('models/kinetic/chassagnole2002.xml')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce9b0ec",
   "metadata": {},
   "source": [
    "The SBML file is loaded into an ODEModel instance that you may use to run distinct tasks. \n",
    "\n",
    "Let us start by listing the metabolites and their default initial concentrations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58f6dc98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>compartment</th>\n",
       "      <th>formula</th>\n",
       "      <th>charge</th>\n",
       "      <th>y0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cpep</th>\n",
       "      <td>Phosphoenol pyruvate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cglcex</th>\n",
       "      <td>Extracellular Glucose</td>\n",
       "      <td>extracellular</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cg6p</th>\n",
       "      <td>Glucose-6-Phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpyr</th>\n",
       "      <td>Pyruvate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cf6p</th>\n",
       "      <td>Fructose-6-Phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cg1p</th>\n",
       "      <td>Glucose-1-Phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpg</th>\n",
       "      <td>6-Phosphogluconate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cfdp</th>\n",
       "      <td>Fructose-1,6-bisphosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>csed7p</th>\n",
       "      <td>sedoheptulose-7-phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cgap</th>\n",
       "      <td>Glyceraldehyde-3-Phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ce4p</th>\n",
       "      <td>Erythrose-4-phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cxyl5p</th>\n",
       "      <td>Xylulose-5-phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crib5p</th>\n",
       "      <td>Ribose-5-phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdhap</th>\n",
       "      <td>Dihydroxyacetonephosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpgp</th>\n",
       "      <td>1,3-diphosphosphoglycerate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpg3</th>\n",
       "      <td>3-Phosphoglycerate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpg2</th>\n",
       "      <td>2-Phosphoglycerate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cribu5p</th>\n",
       "      <td>Ribulose-5-phosphate</td>\n",
       "      <td>cytosol</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name    compartment formula charge     y0\n",
       "id                                                                      \n",
       "cpep           Phosphoenol pyruvate        cytosol                 2.670\n",
       "cglcex        Extracellular Glucose  extracellular                 2.000\n",
       "cg6p            Glucose-6-Phosphate        cytosol                 3.480\n",
       "cpyr                       Pyruvate        cytosol                 2.670\n",
       "cf6p           Fructose-6-Phosphate        cytosol                 0.600\n",
       "cg1p            Glucose-1-Phosphate        cytosol                 0.653\n",
       "cpg              6-Phosphogluconate        cytosol                 0.808\n",
       "cfdp      Fructose-1,6-bisphosphate        cytosol                 0.272\n",
       "csed7p    sedoheptulose-7-phosphate        cytosol                 0.276\n",
       "cgap     Glyceraldehyde-3-Phosphate        cytosol                 0.218\n",
       "ce4p          Erythrose-4-phosphate        cytosol                 0.098\n",
       "cxyl5p         Xylulose-5-phosphate        cytosol                 0.138\n",
       "crib5p           Ribose-5-phosphate        cytosol                 0.398\n",
       "cdhap     Dihydroxyacetonephosphate        cytosol                 0.167\n",
       "cpgp     1,3-diphosphosphoglycerate        cytosol                 0.008\n",
       "cpg3             3-Phosphoglycerate        cytosol                 2.130\n",
       "cpg2             2-Phosphoglycerate        cytosol                 0.399\n",
       "cribu5p        Ribulose-5-phosphate        cytosol                 0.111"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find_metabolites()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3b9531",
   "metadata": {},
   "source": [
    "and the reactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e1428db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>stoichiometry</th>\n",
       "      <th>law</th>\n",
       "      <th>reversible</th>\n",
       "      <th>parameters</th>\n",
       "      <th>modifiers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vPTS</th>\n",
       "      <td>Phosphotransferase system</td>\n",
       "      <td>{'cglcex': -1.0, 'cpep': -1.0, 'cg6p': 1.0, 'c...</td>\n",
       "      <td>extracellular * rmaxPTS * cglcex * (cpep / cpy...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPTS': 7829.78, 'KPTSa1': 3082.3, 'KPTSa2...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGI</th>\n",
       "      <td>Glucose-6-phosphate isomerase</td>\n",
       "      <td>{'cg6p': -1.0, 'cf6p': 1.0}</td>\n",
       "      <td>cytosol * rmaxPGI * (cg6p - cf6p / KPGIeq) / (...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxPGI': 650.9878687, 'KPGIeq': 0.1725, 'KP...</td>\n",
       "      <td>[cpg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGM</th>\n",
       "      <td>Phosphoglucomutase</td>\n",
       "      <td>{'cg6p': -1.0, 'cg1p': 1.0}</td>\n",
       "      <td>cytosol * rmaxPGM * (cg6p - cg1p / KPGMeq) / (...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxPGM': 0.8398242773, 'KPGMeq': 0.196, 'KP...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG6PDH</th>\n",
       "      <td>Glucose-6-phosphate dehydrogenase</td>\n",
       "      <td>{'cg6p': -1.0, 'cpg': 1.0}</td>\n",
       "      <td>cytosol * rmaxG6PDH * cg6p * cnadp / ((cg6p + ...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxG6PDH': 1.380196955, 'KG6PDHg6p': 14.4, ...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPFK</th>\n",
       "      <td>Phosphofructokinase</td>\n",
       "      <td>{'cf6p': -1.0, 'cfdp': 1.0}</td>\n",
       "      <td>cytosol * rmaxPFK * catp * cf6p / ((catp + KPF...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPFK': 1840.584747, 'KPFKatps': 0.123, 'K...</td>\n",
       "      <td>[cpep]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTA</th>\n",
       "      <td>Transaldolase</td>\n",
       "      <td>{'cgap': -1.0, 'csed7p': -1.0, 'cf6p': 1.0, 'c...</td>\n",
       "      <td>cytosol * rmaxTA * (cgap * csed7p - ce4p * cf6...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxTA': 10.87164108, 'KTAeq': 1.05}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTKA</th>\n",
       "      <td>Transketolase a</td>\n",
       "      <td>{'crib5p': -1.0, 'cxyl5p': -1.0, 'cgap': 1.0, ...</td>\n",
       "      <td>cytosol * rmaxTKa * (crib5p * cxyl5p - csed7p ...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxTKa': 9.473384783, 'KTKaeq': 1.2}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTKB</th>\n",
       "      <td>Transketolase b</td>\n",
       "      <td>{'ce4p': -1.0, 'cxyl5p': -1.0, 'cgap': 1.0, 'c...</td>\n",
       "      <td>cytosol * rmaxTKb * (cxyl5p * ce4p - cf6p * cg...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxTKb': 86.55855855, 'KTKbeq': 10.0}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vMURSyNTH</th>\n",
       "      <td>Mureine synthesis</td>\n",
       "      <td>{'cf6p': -2.0}</td>\n",
       "      <td>cytosol * rmaxMurSynth</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxMurSynth': 0.00043711}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vALDO</th>\n",
       "      <td>Aldolase</td>\n",
       "      <td>{'cfdp': -1.0, 'cdhap': 1.0, 'cgap': 1.0}</td>\n",
       "      <td>cytosol * rmaxALDO * (cfdp - cgap * cdhap / kA...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxALDO': 17.41464425, 'kALDOeq': 0.144, 'k...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGAPDH</th>\n",
       "      <td>Glyceraldehyde-3-phosphate dehydrogenase</td>\n",
       "      <td>{'cgap': -1.0, 'cpgp': 1.0}</td>\n",
       "      <td>cytosol * rmaxGAPDH * (cgap * cnad - cpgp * cn...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxGAPDH': 921.5942861, 'KGAPDHeq': 0.63, '...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTIS</th>\n",
       "      <td>Triosephosphate isomerase</td>\n",
       "      <td>{'cdhap': -1.0, 'cgap': 1.0}</td>\n",
       "      <td>cytosol * rmaxTIS * (cdhap - cgap / kTISeq) / ...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxTIS': 68.67474392, 'kTISeq': 1.39, 'kTIS...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTRPSYNTH</th>\n",
       "      <td>Tryptophan synthesis</td>\n",
       "      <td>{'cpyr': 1.0, 'cgap': 1.0}</td>\n",
       "      <td>cytosol * rmaxTrpSynth</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxTrpSynth': 0.001037}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG3PDH</th>\n",
       "      <td>Glycerol-3-phosphate dehydrogenase</td>\n",
       "      <td>{'cdhap': -1.0}</td>\n",
       "      <td>cytosol * rmaxG3PDH * cdhap / (KG3PDHdhap + cd...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxG3PDH': 0.01162042696, 'KG3PDHdhap': 1.0}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGK</th>\n",
       "      <td>Phosphoglycerate kinase</td>\n",
       "      <td>{'cpgp': -1.0, 'cpg3': 1.0}</td>\n",
       "      <td>cytosol * rmaxPGK * (cadp * cpgp - catp * cpg3...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxPGK': 3021.773771, 'KPGKeq': 1934.4, 'KP...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vsersynth</th>\n",
       "      <td>Serine synthesis</td>\n",
       "      <td>{'cpg3': -1.0}</td>\n",
       "      <td>cytosol * rmaxSerSynth * cpg3 / (KSerSynthpg3 ...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxSerSynth': 0.025712107, 'KSerSynthpg3': ...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vrpGluMu</th>\n",
       "      <td>Phosphoglycerate mutase</td>\n",
       "      <td>{'cpg3': -1.0, 'cpg2': 1.0}</td>\n",
       "      <td>cytosol * rmaxPGluMu * (cpg3 - cpg2 / KPGluMue...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxPGluMu': 89.04965407, 'KPGluMueq': 0.188...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vENO</th>\n",
       "      <td>Enolase</td>\n",
       "      <td>{'cpg2': -1.0, 'cpep': 1.0}</td>\n",
       "      <td>cytosol * rmaxENO * (cpg2 - cpep / KENOeq) / (...</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxENO': 330.4476151, 'KENOeq': 6.73, 'KENO...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPK</th>\n",
       "      <td>Pyruvate kinase</td>\n",
       "      <td>{'cpep': -1.0, 'cpyr': 1.0}</td>\n",
       "      <td>cytosol * rmaxPK * cpep * pow(cpep / KPKpep + ...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPK': 0.06113150238, 'KPKpep': 0.31, 'nPK...</td>\n",
       "      <td>[cfdp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpepCxylase</th>\n",
       "      <td>PEP carboxylase</td>\n",
       "      <td>{'cpep': -1.0}</td>\n",
       "      <td>cytosol * rmaxpepCxylase * cpep * (1 + pow(cfd...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxpepCxylase': 0.1070205858, 'KpepCxylasef...</td>\n",
       "      <td>[cfdp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSynth1</th>\n",
       "      <td>Synthesis 1</td>\n",
       "      <td>{'cpep': -1.0}</td>\n",
       "      <td>cytosol * rmaxSynth1 * cpep / (KSynth1pep + cpep)</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxSynth1': 0.01953897003, 'KSynth1pep': 1.0}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSynth2</th>\n",
       "      <td>Synthesis 2</td>\n",
       "      <td>{'cpyr': -1.0}</td>\n",
       "      <td>cytosol * rmaxSynth2 * cpyr / (KSynth2pyr + cpyr)</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxSynth2': 0.07361855055, 'KSynth2pyr': 1.0}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vDAHPS</th>\n",
       "      <td>DAHP synthesis</td>\n",
       "      <td>{'ce4p': -1.0, 'cpep': -1.0}</td>\n",
       "      <td>cytosol * rmaxDAHPS * pow(ce4p, nDAHPSe4p) * p...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxDAHPS': 0.1079531227, 'nDAHPSe4p': 2.6, ...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPDH</th>\n",
       "      <td>Pyruvate dehydrogenase</td>\n",
       "      <td>{'cpyr': -1.0}</td>\n",
       "      <td>cytosol * rmaxPDH * pow(cpyr, nPDH) / (KPDHpyr...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPDH': 6.059531017, 'nPDH': 3.68, 'KPDHpy...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vMethSynth</th>\n",
       "      <td>Methionine synthesis</td>\n",
       "      <td>{'cpyr': 1.0}</td>\n",
       "      <td>cytosol * rmaxMetSynth</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxMetSynth': 0.0022627}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGDH</th>\n",
       "      <td>6-Phosphogluconate dehydrogenase</td>\n",
       "      <td>{'cpg': -1.0, 'cribu5p': 1.0}</td>\n",
       "      <td>cytosol * rmaxPGDH * cpg * cnadp / ((cpg + KPG...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPGDH': 16.23235977, 'KPGDHpg': 37.5, 'KP...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vR5PI</th>\n",
       "      <td>Ribose-phosphate isomerase</td>\n",
       "      <td>{'cribu5p': -1.0, 'crib5p': 1.0}</td>\n",
       "      <td>cytosol * rmaxR5PI * (cribu5p - crib5p / KR5PIeq)</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxR5PI': 4.83841193, 'KR5PIeq': 4.0}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRu5P</th>\n",
       "      <td>Ribulose-phosphate epimerase</td>\n",
       "      <td>{'cribu5p': -1.0, 'cxyl5p': 1.0}</td>\n",
       "      <td>cytosol * rmaxRu5P * (cribu5p - cxyl5p / KRu5Peq)</td>\n",
       "      <td>True</td>\n",
       "      <td>{'rmaxRu5P': 6.739029475, 'KRu5Peq': 1.4}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPPK</th>\n",
       "      <td>Ribose phosphate pyrophosphokinase</td>\n",
       "      <td>{'crib5p': -1.0}</td>\n",
       "      <td>cytosol * rmaxRPPK * crib5p / (KRPPKrib5p + cr...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxRPPK': 0.01290045226, 'KRPPKrib5p': 0.1}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG1PAT</th>\n",
       "      <td>Glucose-1-phosphate adenyltransferase</td>\n",
       "      <td>{'cg1p': -1.0}</td>\n",
       "      <td>cytosol * rmaxG1PAT * cg1p * catp * (1 + pow(c...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxG1PAT': 0.007525458026, 'KG1PATfdp': 0.1...</td>\n",
       "      <td>[cfdp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG6P</th>\n",
       "      <td>G6P degradation</td>\n",
       "      <td>{'cg6p': -1.0}</td>\n",
       "      <td>cytosol * Dil * cg6p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf6P</th>\n",
       "      <td>F6P degradation</td>\n",
       "      <td>{'cf6p': -1.0}</td>\n",
       "      <td>cytosol * Dil * cf6p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vfdP</th>\n",
       "      <td>FDP degradation</td>\n",
       "      <td>{'cfdp': -1.0}</td>\n",
       "      <td>cytosol * Dil * cfdp</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGAP</th>\n",
       "      <td>GAP degradation</td>\n",
       "      <td>{'cgap': -1.0}</td>\n",
       "      <td>cytosol * Dil * cgap</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vDHAP</th>\n",
       "      <td>DHAP degradation</td>\n",
       "      <td>{'cdhap': -1.0}</td>\n",
       "      <td>cytosol * Dil * cdhap</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGP</th>\n",
       "      <td>PGP degradation</td>\n",
       "      <td>{'cpgp': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpgp</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPG3</th>\n",
       "      <td>PG3 degradation</td>\n",
       "      <td>{'cpg3': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpg3</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpg2</th>\n",
       "      <td>PG2 degradation</td>\n",
       "      <td>{'cpg2': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpg2</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPEP</th>\n",
       "      <td>PEP degradation</td>\n",
       "      <td>{'cpep': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpep</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRibu5p</th>\n",
       "      <td>Ribu5P dilution</td>\n",
       "      <td>{'cribu5p': -1.0}</td>\n",
       "      <td>cytosol * Dil * cribu5p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRIB5P</th>\n",
       "      <td>Rib5P dilution</td>\n",
       "      <td>{'crib5p': -1.0}</td>\n",
       "      <td>cytosol * Dil * crib5p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vXYL5P</th>\n",
       "      <td>XYL5P dilution</td>\n",
       "      <td>{'cxyl5p': -1.0}</td>\n",
       "      <td>cytosol * Dil * cxyl5p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSED7P</th>\n",
       "      <td>SED7P dilution</td>\n",
       "      <td>{'csed7p': -1.0}</td>\n",
       "      <td>cytosol * Dil * csed7p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpyr</th>\n",
       "      <td>Pyruvate dilution</td>\n",
       "      <td>{'cpyr': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpyr</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPG</th>\n",
       "      <td>PG dilution</td>\n",
       "      <td>{'cpg': -1.0}</td>\n",
       "      <td>cytosol * Dil * cpg</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vE4P</th>\n",
       "      <td>E4P dilution</td>\n",
       "      <td>{'ce4p': -1.0}</td>\n",
       "      <td>cytosol * Dil * ce4p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGLP</th>\n",
       "      <td>GLP dilution</td>\n",
       "      <td>{'cg1p': -1.0}</td>\n",
       "      <td>cytosol * Dil * cg1p</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vEXTER</th>\n",
       "      <td>Extracellular glucose kinetics</td>\n",
       "      <td>{'cglcex': 1.0}</td>\n",
       "      <td>extracellular * Dil * (cfeed - cglcex)</td>\n",
       "      <td>False</td>\n",
       "      <td>{'cfeed': 110.96}</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 name  \\\n",
       "id                                                      \n",
       "vPTS                        Phosphotransferase system   \n",
       "vPGI                    Glucose-6-phosphate isomerase   \n",
       "vPGM                               Phosphoglucomutase   \n",
       "vG6PDH              Glucose-6-phosphate dehydrogenase   \n",
       "vPFK                              Phosphofructokinase   \n",
       "vTA                                     Transaldolase   \n",
       "vTKA                                  Transketolase a   \n",
       "vTKB                                  Transketolase b   \n",
       "vMURSyNTH                           Mureine synthesis   \n",
       "vALDO                                        Aldolase   \n",
       "vGAPDH       Glyceraldehyde-3-phosphate dehydrogenase   \n",
       "vTIS                        Triosephosphate isomerase   \n",
       "vTRPSYNTH                        Tryptophan synthesis   \n",
       "vG3PDH             Glycerol-3-phosphate dehydrogenase   \n",
       "vPGK                          Phosphoglycerate kinase   \n",
       "vsersynth                            Serine synthesis   \n",
       "vrpGluMu                      Phosphoglycerate mutase   \n",
       "vENO                                          Enolase   \n",
       "vPK                                   Pyruvate kinase   \n",
       "vpepCxylase                           PEP carboxylase   \n",
       "vSynth1                                   Synthesis 1   \n",
       "vSynth2                                   Synthesis 2   \n",
       "vDAHPS                                 DAHP synthesis   \n",
       "vPDH                           Pyruvate dehydrogenase   \n",
       "vMethSynth                       Methionine synthesis   \n",
       "vPGDH                6-Phosphogluconate dehydrogenase   \n",
       "vR5PI                      Ribose-phosphate isomerase   \n",
       "vRu5P                    Ribulose-phosphate epimerase   \n",
       "vPPK               Ribose phosphate pyrophosphokinase   \n",
       "vG1PAT          Glucose-1-phosphate adenyltransferase   \n",
       "vG6P                                  G6P degradation   \n",
       "vf6P                                  F6P degradation   \n",
       "vfdP                                  FDP degradation   \n",
       "vGAP                                  GAP degradation   \n",
       "vDHAP                                DHAP degradation   \n",
       "vPGP                                  PGP degradation   \n",
       "vPG3                                  PG3 degradation   \n",
       "vpg2                                  PG2 degradation   \n",
       "vPEP                                  PEP degradation   \n",
       "vRibu5p                               Ribu5P dilution   \n",
       "vRIB5P                                 Rib5P dilution   \n",
       "vXYL5P                                 XYL5P dilution   \n",
       "vSED7P                                 SED7P dilution   \n",
       "vpyr                                Pyruvate dilution   \n",
       "vPG                                       PG dilution   \n",
       "vE4P                                     E4P dilution   \n",
       "vGLP                                     GLP dilution   \n",
       "vEXTER                 Extracellular glucose kinetics   \n",
       "\n",
       "                                                 stoichiometry  \\\n",
       "id                                                               \n",
       "vPTS         {'cglcex': -1.0, 'cpep': -1.0, 'cg6p': 1.0, 'c...   \n",
       "vPGI                               {'cg6p': -1.0, 'cf6p': 1.0}   \n",
       "vPGM                               {'cg6p': -1.0, 'cg1p': 1.0}   \n",
       "vG6PDH                              {'cg6p': -1.0, 'cpg': 1.0}   \n",
       "vPFK                               {'cf6p': -1.0, 'cfdp': 1.0}   \n",
       "vTA          {'cgap': -1.0, 'csed7p': -1.0, 'cf6p': 1.0, 'c...   \n",
       "vTKA         {'crib5p': -1.0, 'cxyl5p': -1.0, 'cgap': 1.0, ...   \n",
       "vTKB         {'ce4p': -1.0, 'cxyl5p': -1.0, 'cgap': 1.0, 'c...   \n",
       "vMURSyNTH                                       {'cf6p': -2.0}   \n",
       "vALDO                {'cfdp': -1.0, 'cdhap': 1.0, 'cgap': 1.0}   \n",
       "vGAPDH                             {'cgap': -1.0, 'cpgp': 1.0}   \n",
       "vTIS                              {'cdhap': -1.0, 'cgap': 1.0}   \n",
       "vTRPSYNTH                           {'cpyr': 1.0, 'cgap': 1.0}   \n",
       "vG3PDH                                         {'cdhap': -1.0}   \n",
       "vPGK                               {'cpgp': -1.0, 'cpg3': 1.0}   \n",
       "vsersynth                                       {'cpg3': -1.0}   \n",
       "vrpGluMu                           {'cpg3': -1.0, 'cpg2': 1.0}   \n",
       "vENO                               {'cpg2': -1.0, 'cpep': 1.0}   \n",
       "vPK                                {'cpep': -1.0, 'cpyr': 1.0}   \n",
       "vpepCxylase                                     {'cpep': -1.0}   \n",
       "vSynth1                                         {'cpep': -1.0}   \n",
       "vSynth2                                         {'cpyr': -1.0}   \n",
       "vDAHPS                            {'ce4p': -1.0, 'cpep': -1.0}   \n",
       "vPDH                                            {'cpyr': -1.0}   \n",
       "vMethSynth                                       {'cpyr': 1.0}   \n",
       "vPGDH                            {'cpg': -1.0, 'cribu5p': 1.0}   \n",
       "vR5PI                         {'cribu5p': -1.0, 'crib5p': 1.0}   \n",
       "vRu5P                         {'cribu5p': -1.0, 'cxyl5p': 1.0}   \n",
       "vPPK                                          {'crib5p': -1.0}   \n",
       "vG1PAT                                          {'cg1p': -1.0}   \n",
       "vG6P                                            {'cg6p': -1.0}   \n",
       "vf6P                                            {'cf6p': -1.0}   \n",
       "vfdP                                            {'cfdp': -1.0}   \n",
       "vGAP                                            {'cgap': -1.0}   \n",
       "vDHAP                                          {'cdhap': -1.0}   \n",
       "vPGP                                            {'cpgp': -1.0}   \n",
       "vPG3                                            {'cpg3': -1.0}   \n",
       "vpg2                                            {'cpg2': -1.0}   \n",
       "vPEP                                            {'cpep': -1.0}   \n",
       "vRibu5p                                      {'cribu5p': -1.0}   \n",
       "vRIB5P                                        {'crib5p': -1.0}   \n",
       "vXYL5P                                        {'cxyl5p': -1.0}   \n",
       "vSED7P                                        {'csed7p': -1.0}   \n",
       "vpyr                                            {'cpyr': -1.0}   \n",
       "vPG                                              {'cpg': -1.0}   \n",
       "vE4P                                            {'ce4p': -1.0}   \n",
       "vGLP                                            {'cg1p': -1.0}   \n",
       "vEXTER                                         {'cglcex': 1.0}   \n",
       "\n",
       "                                                           law  reversible  \\\n",
       "id                                                                           \n",
       "vPTS         extracellular * rmaxPTS * cglcex * (cpep / cpy...       False   \n",
       "vPGI         cytosol * rmaxPGI * (cg6p - cf6p / KPGIeq) / (...        True   \n",
       "vPGM         cytosol * rmaxPGM * (cg6p - cg1p / KPGMeq) / (...        True   \n",
       "vG6PDH       cytosol * rmaxG6PDH * cg6p * cnadp / ((cg6p + ...       False   \n",
       "vPFK         cytosol * rmaxPFK * catp * cf6p / ((catp + KPF...       False   \n",
       "vTA          cytosol * rmaxTA * (cgap * csed7p - ce4p * cf6...        True   \n",
       "vTKA         cytosol * rmaxTKa * (crib5p * cxyl5p - csed7p ...        True   \n",
       "vTKB         cytosol * rmaxTKb * (cxyl5p * ce4p - cf6p * cg...        True   \n",
       "vMURSyNTH                               cytosol * rmaxMurSynth       False   \n",
       "vALDO        cytosol * rmaxALDO * (cfdp - cgap * cdhap / kA...        True   \n",
       "vGAPDH       cytosol * rmaxGAPDH * (cgap * cnad - cpgp * cn...        True   \n",
       "vTIS         cytosol * rmaxTIS * (cdhap - cgap / kTISeq) / ...        True   \n",
       "vTRPSYNTH                               cytosol * rmaxTrpSynth       False   \n",
       "vG3PDH       cytosol * rmaxG3PDH * cdhap / (KG3PDHdhap + cd...       False   \n",
       "vPGK         cytosol * rmaxPGK * (cadp * cpgp - catp * cpg3...        True   \n",
       "vsersynth    cytosol * rmaxSerSynth * cpg3 / (KSerSynthpg3 ...       False   \n",
       "vrpGluMu     cytosol * rmaxPGluMu * (cpg3 - cpg2 / KPGluMue...        True   \n",
       "vENO         cytosol * rmaxENO * (cpg2 - cpep / KENOeq) / (...        True   \n",
       "vPK          cytosol * rmaxPK * cpep * pow(cpep / KPKpep + ...       False   \n",
       "vpepCxylase  cytosol * rmaxpepCxylase * cpep * (1 + pow(cfd...       False   \n",
       "vSynth1      cytosol * rmaxSynth1 * cpep / (KSynth1pep + cpep)       False   \n",
       "vSynth2      cytosol * rmaxSynth2 * cpyr / (KSynth2pyr + cpyr)       False   \n",
       "vDAHPS       cytosol * rmaxDAHPS * pow(ce4p, nDAHPSe4p) * p...       False   \n",
       "vPDH         cytosol * rmaxPDH * pow(cpyr, nPDH) / (KPDHpyr...       False   \n",
       "vMethSynth                              cytosol * rmaxMetSynth       False   \n",
       "vPGDH        cytosol * rmaxPGDH * cpg * cnadp / ((cpg + KPG...       False   \n",
       "vR5PI        cytosol * rmaxR5PI * (cribu5p - crib5p / KR5PIeq)        True   \n",
       "vRu5P        cytosol * rmaxRu5P * (cribu5p - cxyl5p / KRu5Peq)        True   \n",
       "vPPK         cytosol * rmaxRPPK * crib5p / (KRPPKrib5p + cr...       False   \n",
       "vG1PAT       cytosol * rmaxG1PAT * cg1p * catp * (1 + pow(c...       False   \n",
       "vG6P                                      cytosol * Dil * cg6p       False   \n",
       "vf6P                                      cytosol * Dil * cf6p       False   \n",
       "vfdP                                      cytosol * Dil * cfdp       False   \n",
       "vGAP                                      cytosol * Dil * cgap       False   \n",
       "vDHAP                                    cytosol * Dil * cdhap       False   \n",
       "vPGP                                      cytosol * Dil * cpgp       False   \n",
       "vPG3                                      cytosol * Dil * cpg3       False   \n",
       "vpg2                                      cytosol * Dil * cpg2       False   \n",
       "vPEP                                      cytosol * Dil * cpep       False   \n",
       "vRibu5p                                cytosol * Dil * cribu5p       False   \n",
       "vRIB5P                                  cytosol * Dil * crib5p       False   \n",
       "vXYL5P                                  cytosol * Dil * cxyl5p       False   \n",
       "vSED7P                                  cytosol * Dil * csed7p       False   \n",
       "vpyr                                      cytosol * Dil * cpyr       False   \n",
       "vPG                                        cytosol * Dil * cpg       False   \n",
       "vE4P                                      cytosol * Dil * ce4p       False   \n",
       "vGLP                                      cytosol * Dil * cg1p       False   \n",
       "vEXTER                  extracellular * Dil * (cfeed - cglcex)       False   \n",
       "\n",
       "                                                    parameters modifiers  \n",
       "id                                                                        \n",
       "vPTS         {'rmaxPTS': 7829.78, 'KPTSa1': 3082.3, 'KPTSa2...        []  \n",
       "vPGI         {'rmaxPGI': 650.9878687, 'KPGIeq': 0.1725, 'KP...     [cpg]  \n",
       "vPGM         {'rmaxPGM': 0.8398242773, 'KPGMeq': 0.196, 'KP...        []  \n",
       "vG6PDH       {'rmaxG6PDH': 1.380196955, 'KG6PDHg6p': 14.4, ...        []  \n",
       "vPFK         {'rmaxPFK': 1840.584747, 'KPFKatps': 0.123, 'K...    [cpep]  \n",
       "vTA                     {'rmaxTA': 10.87164108, 'KTAeq': 1.05}        []  \n",
       "vTKA                   {'rmaxTKa': 9.473384783, 'KTKaeq': 1.2}        []  \n",
       "vTKB                  {'rmaxTKb': 86.55855855, 'KTKbeq': 10.0}        []  \n",
       "vMURSyNTH                         {'rmaxMurSynth': 0.00043711}        []  \n",
       "vALDO        {'rmaxALDO': 17.41464425, 'kALDOeq': 0.144, 'k...        []  \n",
       "vGAPDH       {'rmaxGAPDH': 921.5942861, 'KGAPDHeq': 0.63, '...        []  \n",
       "vTIS         {'rmaxTIS': 68.67474392, 'kTISeq': 1.39, 'kTIS...        []  \n",
       "vTRPSYNTH                           {'rmaxTrpSynth': 0.001037}        []  \n",
       "vG3PDH         {'rmaxG3PDH': 0.01162042696, 'KG3PDHdhap': 1.0}        []  \n",
       "vPGK         {'rmaxPGK': 3021.773771, 'KPGKeq': 1934.4, 'KP...        []  \n",
       "vsersynth    {'rmaxSerSynth': 0.025712107, 'KSerSynthpg3': ...        []  \n",
       "vrpGluMu     {'rmaxPGluMu': 89.04965407, 'KPGluMueq': 0.188...        []  \n",
       "vENO         {'rmaxENO': 330.4476151, 'KENOeq': 6.73, 'KENO...        []  \n",
       "vPK          {'rmaxPK': 0.06113150238, 'KPKpep': 0.31, 'nPK...    [cfdp]  \n",
       "vpepCxylase  {'rmaxpepCxylase': 0.1070205858, 'KpepCxylasef...    [cfdp]  \n",
       "vSynth1       {'rmaxSynth1': 0.01953897003, 'KSynth1pep': 1.0}        []  \n",
       "vSynth2       {'rmaxSynth2': 0.07361855055, 'KSynth2pyr': 1.0}        []  \n",
       "vDAHPS       {'rmaxDAHPS': 0.1079531227, 'nDAHPSe4p': 2.6, ...        []  \n",
       "vPDH         {'rmaxPDH': 6.059531017, 'nPDH': 3.68, 'KPDHpy...        []  \n",
       "vMethSynth                         {'rmaxMetSynth': 0.0022627}        []  \n",
       "vPGDH        {'rmaxPGDH': 16.23235977, 'KPGDHpg': 37.5, 'KP...        []  \n",
       "vR5PI                 {'rmaxR5PI': 4.83841193, 'KR5PIeq': 4.0}        []  \n",
       "vRu5P                {'rmaxRu5P': 6.739029475, 'KRu5Peq': 1.4}        []  \n",
       "vPPK            {'rmaxRPPK': 0.01290045226, 'KRPPKrib5p': 0.1}        []  \n",
       "vG1PAT       {'rmaxG1PAT': 0.007525458026, 'KG1PATfdp': 0.1...    [cfdp]  \n",
       "vG6P                                                        {}        []  \n",
       "vf6P                                                        {}        []  \n",
       "vfdP                                                        {}        []  \n",
       "vGAP                                                        {}        []  \n",
       "vDHAP                                                       {}        []  \n",
       "vPGP                                                        {}        []  \n",
       "vPG3                                                        {}        []  \n",
       "vpg2                                                        {}        []  \n",
       "vPEP                                                        {}        []  \n",
       "vRibu5p                                                     {}        []  \n",
       "vRIB5P                                                      {}        []  \n",
       "vXYL5P                                                      {}        []  \n",
       "vSED7P                                                      {}        []  \n",
       "vpyr                                                        {}        []  \n",
       "vPG                                                         {}        []  \n",
       "vE4P                                                        {}        []  \n",
       "vGLP                                                        {}        []  \n",
       "vEXTER                                       {'cfeed': 110.96}        []  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8132ee",
   "metadata": {},
   "source": [
    "You may filter the list of reactions by a name pattern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21e382b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>stoichiometry</th>\n",
       "      <th>law</th>\n",
       "      <th>reversible</th>\n",
       "      <th>parameters</th>\n",
       "      <th>modifiers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vPTS</th>\n",
       "      <td>Phosphotransferase system</td>\n",
       "      <td>{'cglcex': -1.0, 'cpep': -1.0, 'cg6p': 1.0, 'c...</td>\n",
       "      <td>extracellular * rmaxPTS * cglcex * (cpep / cpy...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'rmaxPTS': 7829.78, 'KPTSa1': 3082.3, 'KPTSa2...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name  \\\n",
       "id                                \n",
       "vPTS  Phosphotransferase system   \n",
       "\n",
       "                                          stoichiometry  \\\n",
       "id                                                        \n",
       "vPTS  {'cglcex': -1.0, 'cpep': -1.0, 'cg6p': 1.0, 'c...   \n",
       "\n",
       "                                                    law  reversible  \\\n",
       "id                                                                    \n",
       "vPTS  extracellular * rmaxPTS * cglcex * (cpep / cpy...       False   \n",
       "\n",
       "                                             parameters modifiers  \n",
       "id                                                                 \n",
       "vPTS  {'rmaxPTS': 7829.78, 'KPTSa1': 3082.3, 'KPTSa2...        []  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find('vPTS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623c0df8",
   "metadata": {},
   "source": [
    "The same is true for parameters in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8083d634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parameter</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rmaxPTS</th>\n",
       "      <td>7829.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPGI</th>\n",
       "      <td>650.987869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPGM</th>\n",
       "      <td>0.839824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxG6PDH</th>\n",
       "      <td>1.380197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPFK</th>\n",
       "      <td>1840.584747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxTA</th>\n",
       "      <td>10.871641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxTKa</th>\n",
       "      <td>9.473385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxTKb</th>\n",
       "      <td>86.558559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxMurSynth</th>\n",
       "      <td>0.000437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxALDO</th>\n",
       "      <td>17.414644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxGAPDH</th>\n",
       "      <td>921.594286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxTIS</th>\n",
       "      <td>68.674744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxTrpSynth</th>\n",
       "      <td>0.001037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxG3PDH</th>\n",
       "      <td>0.011620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPGK</th>\n",
       "      <td>3021.773771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxSerSynth</th>\n",
       "      <td>0.025712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPGluMu</th>\n",
       "      <td>89.049654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxENO</th>\n",
       "      <td>330.447615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPK</th>\n",
       "      <td>0.061132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxpepCxylase</th>\n",
       "      <td>0.107021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxSynth1</th>\n",
       "      <td>0.019539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxSynth2</th>\n",
       "      <td>0.073619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxDAHPS</th>\n",
       "      <td>0.107953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPDH</th>\n",
       "      <td>6.059531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxMetSynth</th>\n",
       "      <td>0.002263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxPGDH</th>\n",
       "      <td>16.232360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxR5PI</th>\n",
       "      <td>4.838412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxRu5P</th>\n",
       "      <td>6.739029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxRPPK</th>\n",
       "      <td>0.012900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmaxG1PAT</th>\n",
       "      <td>0.007525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Value\n",
       "Parameter                  \n",
       "rmaxPTS         7829.780000\n",
       "rmaxPGI          650.987869\n",
       "rmaxPGM            0.839824\n",
       "rmaxG6PDH          1.380197\n",
       "rmaxPFK         1840.584747\n",
       "rmaxTA            10.871641\n",
       "rmaxTKa            9.473385\n",
       "rmaxTKb           86.558559\n",
       "rmaxMurSynth       0.000437\n",
       "rmaxALDO          17.414644\n",
       "rmaxGAPDH        921.594286\n",
       "rmaxTIS           68.674744\n",
       "rmaxTrpSynth       0.001037\n",
       "rmaxG3PDH          0.011620\n",
       "rmaxPGK         3021.773771\n",
       "rmaxSerSynth       0.025712\n",
       "rmaxPGluMu        89.049654\n",
       "rmaxENO          330.447615\n",
       "rmaxPK             0.061132\n",
       "rmaxpepCxylase     0.107021\n",
       "rmaxSynth1         0.019539\n",
       "rmaxSynth2         0.073619\n",
       "rmaxDAHPS          0.107953\n",
       "rmaxPDH            6.059531\n",
       "rmaxMetSynth       0.002263\n",
       "rmaxPGDH          16.232360\n",
       "rmaxR5PI           4.838412\n",
       "rmaxRu5P           6.739029\n",
       "rmaxRPPK           0.012900\n",
       "rmaxG1PAT          0.007525"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find_parameters('rmax')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a78182a",
   "metadata": {},
   "source": [
    "### Model Reactions\n",
    "\n",
    "You may access each reaction programmatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6660ba56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$ \\frac {\\textrm{extracellular} \\times \\textrm{rmaxPTS} \\times \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}}} {\\mathopen{}\\left( \\textrm{KPTSa1} + \\textrm{KPTSa2} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} + \\textrm{KPTSa3} \\times \\textrm{cglcex} + \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} \\mathclose{}\\right) \\times \\mathopen{}\\left( \\textrm{1} + \\frac {{\\textrm{cg6p}}^{\\textrm{nPTSg6p}}} {\\textrm{KPTSg6p}} \\mathclose{}\\right)} $$"
      ],
      "text/plain": [
       "((((extracellular*7829.78)*cglcex)*(cpep/cpyr))/((((3082.3+(0.01*(cpep/cpyr)))+(245.3*cglcex))+(cglcex*(cpep/cpyr)))*(1+(pow(cg6p,3.66)/2.15))))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c3d6df",
   "metadata": {},
   "source": [
    "List the substrate and product metabolites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9331502a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cglcex', 'cpep']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.substrates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bd95b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cg6p', 'cpyr']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6080e55",
   "metadata": {},
   "source": [
    "as well as the parameters and their default values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f8e08bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('rmaxPTS', 7829.78),\n",
       "             ('KPTSa1', 3082.3),\n",
       "             ('KPTSa2', 0.01),\n",
       "             ('KPTSa3', 245.3),\n",
       "             ('nPTSg6p', 3.66),\n",
       "             ('KPTSg6p', 2.15)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09203f6",
   "metadata": {},
   "source": [
    "You may rename parameters if needed, for example if you decide to add a reaction to the model and avoid conflicting naming. The renaming is persistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff6e6928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$ \\frac {\\textrm{extracellular} \\times \\textrm{rmaxPTS_renamed} \\times \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}}} {\\mathopen{}\\left( \\textrm{KPTSa1} + \\textrm{KPTSa2} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} + \\textrm{KPTSa3} \\times \\textrm{cglcex} + \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} \\mathclose{}\\right) \\times \\mathopen{}\\left( \\textrm{1} + \\frac {{\\textrm{cg6p}}^{\\textrm{nPTSg6p}}} {\\textrm{KPTSg6p}} \\mathclose{}\\right)} $$"
      ],
      "text/plain": [
       "((((extracellular*7829.78)*cglcex)*(cpep/cpyr))/((((3082.3+(0.01*(cpep/cpyr)))+(245.3*cglcex))+(cglcex*(cpep/cpyr)))*(1+(pow(cg6p,3.66)/2.15))))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.rename_parameter('rmaxPTS','rmaxPTS_renamed')\n",
    "model.reactions.vPTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8128a09",
   "metadata": {},
   "source": [
    "Print the rate law with values defined in the kinetic reaction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a29ac72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'((((extracellular * 7829.78) * cglcex) * (cpep / cpyr)) / ((((3082.3 + (0.01 * (cpep / cpyr))) + (245.3 * cglcex)) + (cglcex * (cpep / cpyr))) * (1 + (pow(cg6p , 3.66) / 2.15))))'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.replace()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954fe476",
   "metadata": {},
   "source": [
    "You may want to use latex for ease of reading:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdba71a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$ \\frac {\\textrm{extracellular} \\times 7829.78 \\times \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}}} {\\mathopen{}\\left( 3082.3 + 0.01 \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} + 245.3 \\times \\textrm{cglcex} + \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} \\mathclose{}\\right) \\times \\mathopen{}\\left( \\textrm{1} + \\frac {{\\textrm{cg6p}}^{3.66}} {2.15} \\mathclose{}\\right)} $$"
      ],
      "text/plain": [
       "\\frac {\\textrm{extracellular} \\times 7829.78 \\times \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}}} {\\mathopen{}\\left( 3082.3 + 0.01 \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} + 245.3 \\times \\textrm{cglcex} + \\textrm{cglcex} \\times \\frac {\\textrm{cpep}} {\\textrm{cpyr}} \\mathclose{}\\right) \\times \\mathopen{}\\left( \\textrm{1} + \\frac {{\\textrm{cg6p}}^{3.66}} {2.15} \\mathclose{}\\right)}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.replace(latex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d40db966",
   "metadata": {},
   "source": [
    "MEWpy also allows for computing the reaction rate with the default values or using alternative parameters and/or metabolites concentration values. \n",
    "\n",
    "Computes the reaction rate with the default parameters and concentrations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5350b7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.238284215661543"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.calculate_rate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14863ac",
   "metadata": {},
   "source": [
    "or passing substrates concentrations and/or parameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e6525a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8123670461913133"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.vPTS.calculate_rate(parameters={'extracellular':70},substrates={'cg6p':5})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a3745c",
   "metadata": {},
   "source": [
    "# Kinetic Simulations\n",
    "\n",
    "To run kinetic simulations you need to instantiate a KineticSimulation object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b072b151",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mewpy.simulation.kinetic import KineticSimulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9dacaca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = KineticSimulation(model,timeout=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b11923",
   "metadata": {},
   "source": [
    "You may define options for all simulations\n",
    "- *parameters*, a dictionary of parameter values whom default values you want to replace;\n",
    "- *t_points*, the integration time points `[1,2,3,4,5]` or time span (a list of two values `[start,end]`);\n",
    "- *timeout*, the solver timeout in seconds. If `timeout=0`, no timeout is applied.\n",
    "\n",
    "When a time span is defined, it is divided into n time steps.\n",
    "The number of time steps is configurable: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2bb60e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mewpy.solvers import SolverConfigurations\n",
    "\n",
    "SolverConfigurations.N_STEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df48208d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SolverConfigurations.N_STEPS = 30\n",
    "SolverConfigurations.N_STEPS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e056a33e",
   "metadata": {},
   "source": [
    "You may also specify the *t_points* for all simulations, in the example a time span between 0 and 600 will be splitted evenly in 20 time points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0981a950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,  31.57894737,  63.15789474,  94.73684211,\n",
       "       126.31578947, 157.89473684, 189.47368421, 221.05263158,\n",
       "       252.63157895, 284.21052632, 315.78947368, 347.36842105,\n",
       "       378.94736842, 410.52631579, 442.10526316, 473.68421053,\n",
       "       505.26315789, 536.84210526, 568.42105263, 600.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.set_time(0,600,20)\n",
    "sim.get_time_points()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b927d310",
   "metadata": {},
   "source": [
    "Time points may be set only for a particular simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb0b708e",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = sim.simulate(t_points=range(0,800,50))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc47a4b",
   "metadata": {},
   "source": [
    "List the **steady-state** values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc7446db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flux rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reaction ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vPTS</th>\n",
       "      <td>2.004969e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGI</th>\n",
       "      <td>5.832543e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGM</th>\n",
       "      <td>2.364146e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG6PDH</th>\n",
       "      <td>1.397891e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPFK</th>\n",
       "      <td>1.414149e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTA</th>\n",
       "      <td>4.548648e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTKA</th>\n",
       "      <td>4.549291e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTKB</th>\n",
       "      <td>3.848041e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vMURSyNTH</th>\n",
       "      <td>4.371100e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vALDO</th>\n",
       "      <td>1.414417e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGAPDH</th>\n",
       "      <td>3.207453e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTIS</th>\n",
       "      <td>1.397714e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vTRPSYNTH</th>\n",
       "      <td>1.037000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG3PDH</th>\n",
       "      <td>1.676618e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGK</th>\n",
       "      <td>3.207456e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vsersynth</th>\n",
       "      <td>1.754707e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vrpGluMu</th>\n",
       "      <td>3.032697e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vENO</th>\n",
       "      <td>3.032831e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPK</th>\n",
       "      <td>3.814642e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpepCxylase</th>\n",
       "      <td>4.347222e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSynth1</th>\n",
       "      <td>1.424755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSynth2</th>\n",
       "      <td>5.356504e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vDAHPS</th>\n",
       "      <td>7.008729e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPDH</th>\n",
       "      <td>1.883079e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vMethSynth</th>\n",
       "      <td>2.262700e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGDH</th>\n",
       "      <td>1.397824e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vR5PI</th>\n",
       "      <td>5.581062e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRu5P</th>\n",
       "      <td>8.397224e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPPK</th>\n",
       "      <td>1.032015e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG1PAT</th>\n",
       "      <td>2.358369e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vG6P</th>\n",
       "      <td>9.705892e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf6P</th>\n",
       "      <td>1.673431e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vfdP</th>\n",
       "      <td>7.702855e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGAP</th>\n",
       "      <td>6.120326e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vDHAP</th>\n",
       "      <td>4.687338e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPGP</th>\n",
       "      <td>2.242835e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPG3</th>\n",
       "      <td>5.974355e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpg2</th>\n",
       "      <td>1.118609e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPEP</th>\n",
       "      <td>7.485367e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRibu5p</th>\n",
       "      <td>3.100386e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vRIB5P</th>\n",
       "      <td>1.111886e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vXYL5P</th>\n",
       "      <td>3.855574e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vSED7P</th>\n",
       "      <td>7.677686e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vpyr</th>\n",
       "      <td>7.425675e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vPG</th>\n",
       "      <td>2.251118e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vE4P</th>\n",
       "      <td>2.745510e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vGLP</th>\n",
       "      <td>1.818815e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vEXTER</th>\n",
       "      <td>2.004038e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Flux rate\n",
       "Reaction ID              \n",
       "vPTS         2.004969e-01\n",
       "vPGI         5.832543e-02\n",
       "vPGM         2.364146e-03\n",
       "vG6PDH       1.397891e-01\n",
       "vPFK         1.414149e-01\n",
       "vTA          4.548648e-02\n",
       "vTKA         4.549291e-02\n",
       "vTKB         3.848041e-02\n",
       "vMURSyNTH    4.371100e-04\n",
       "vALDO        1.414417e-01\n",
       "vGAPDH       3.207453e-01\n",
       "vTIS         1.397714e-01\n",
       "vTRPSYNTH    1.037000e-03\n",
       "vG3PDH       1.676618e-03\n",
       "vPGK         3.207456e-01\n",
       "vsersynth    1.754707e-02\n",
       "vrpGluMu     3.032697e-01\n",
       "vENO         3.032831e-01\n",
       "vPK          3.814642e-02\n",
       "vpepCxylase  4.347222e-02\n",
       "vSynth1      1.424755e-02\n",
       "vSynth2      5.356504e-02\n",
       "vDAHPS       7.008729e-03\n",
       "vPDH         1.883079e-01\n",
       "vMethSynth   2.262700e-03\n",
       "vPGDH        1.397824e-01\n",
       "vR5PI        5.581062e-02\n",
       "vRu5P        8.397224e-02\n",
       "vPPK         1.032015e-02\n",
       "vG1PAT       2.358369e-03\n",
       "vG6P         9.705892e-05\n",
       "vf6P         1.673431e-05\n",
       "vfdP         7.702855e-06\n",
       "vGAP         6.120326e-06\n",
       "vDHAP        4.687338e-06\n",
       "vPGP         2.242835e-07\n",
       "vPG3         5.974355e-05\n",
       "vpg2         1.118609e-05\n",
       "vPEP         7.485367e-05\n",
       "vRibu5p      3.100386e-06\n",
       "vRIB5P       1.111886e-05\n",
       "vXYL5P       3.855574e-06\n",
       "vSED7P       7.677686e-06\n",
       "vpyr         7.425675e-05\n",
       "vPG          2.251118e-05\n",
       "vE4P         2.745510e-06\n",
       "vGLP         1.818815e-05\n",
       "vEXTER       2.004038e-01"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.find()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ba4cdc",
   "metadata": {},
   "source": [
    "Getting or viewing individual fluxes by id or pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5055e9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20049693776702313\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flux rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reaction ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vPTS</th>\n",
       "      <td>0.200497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Flux rate\n",
       "Reaction ID           \n",
       "vPTS          0.200497"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(res.fluxes['vPTS'])\n",
    "# or\n",
    "res.find('vPTS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af94c76b",
   "metadata": {},
   "source": [
    "You may plot the changes on metabolite concentrations over time for one or more metabolites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bce391f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Glyceraldehyde-3-Phosphate'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SIcM3E/vA2UqFM9mlePWXIxBFDoAmImqPGH4MDFt+Wo6ztSm+mdQHJnIBfx3NwuJd56UuiYiIdMDwY2C4qWnLCvayx4KHegAAPtl6CrvP5EpcERERNRXDjwERRZEtP61gYmhHjOvrCY0IvLj2MDIKyqUuiYiImoDhx4CUVatRWcOtLVqaIAh4Z3QPBHnaoqiiBtNWJaCiWi11WURE1EgMPwakfnVnMxM5LFQKiasxbKYmciye1AeOlkqczCzGGxuTOQCaiKidYPgxINouL870ahVuNmb46ok+UMgEbEq6jO/3pkpdEhERNQLDjwHRTnNnl1erCfV1wFsjuwEAoracwj/n8iSuiIiI7oThx4BwsLM0nr7HG4/07gC1RsTMHw/jUmGF1CUREdFtMPwYEIYfaQiCgA8fCUAPd2sUlFXjzY1HpS6JiIhug+HHgNSHHyeu8dPqTE3k+OqJPpAJwK4zuThxuVjqkoiI6BYYfgxInnZrC7b8SMHb0QIjAtwAAEv3cPVnIqK2iuHHgLDbS3rPDewEAPj9yGVc5tgfIqI2ieHHgNSHHwcLdntJJcDDBv18HVCrEbGMU9+JiNokncPPnj17MGnSJPTr1w+XLl0CAKxatQp79+7VW3HUNHml7PZqC6YN8gUArI1PR1FFjcTVEBHRv+kUfjZs2IDw8HCYmZnh8OHDqKqqa3EoKirChx9+qNcCqXEqa9QoraoFwG4vqQ32c0JXFyuUVavxY1y61OUQEdG/6BR+3n//fSxevBhLly6FiYmJ9vi9996LxMREvRVHjVff5aWUy2Btyq0tpCQIAqYOrGv9Wb4vFVW13PeLiKgt0Sn8nD59GgMHDrzhuI2NDQoLC5tbE+lA2+VlqYQgCBJXQ6OC3OFqbYqckipsSrosdTlERHQdncKPq6srUlJSbji+d+9e+Pr6Nrsoarq8kvp9vdjl1RYoFTJE3OsNAFi6+zw0Gm56SkTUVugUfqZOnYpZs2YhLi4OgiDg8uXLWLNmDV555RVMnz5d3zVSI3Cae9szIbQjLFUKnM0pxc4zOVKXQ0REV+k0OOSNN96ARqPBkCFDUF5ejoEDB0KlUuGVV17Biy++qO8aqRGuhR9Oc28rrE1NMDG0I5bsPo8lu87jfn8XqUsiIiLo2PIjCALmzZuHgoICHDt2DAcOHEBubi7ee+89fddHjXRtzA9bftqSiHt9YCIXEJdagKSMQqnLISIi6Bh+Jk+ejJKSEiiVSnTv3h0hISGwtLREWVkZJk+erO8aqRFy2e3VJrnamGJUUAcAwLe7z0lcDRERATqGnxUrVqCi4sal+ysqKrBy5com3eurr76Ct7c3TE1NERoaivj4+EZ9bt26dRAEAWPGjGlwXBRFzJ8/H25ubjAzM0NYWBjOnj3bpJrao/oBzw7s9mpzpl2d9h59LAtp+WUSV0NERE0KP8XFxSgqKoIoiigpKUFxcbH2deXKFfz1119wdnZu9P3Wr1+PyMhILFiwAImJiQgKCkJ4eDhycm4/OPTChQt45ZVXMGDAgBvOLVy4EF988QUWL16MuLg4WFhYIDw8HJWVlU151Hbn2o7ubPlpa7q6WmFwVydoROC7PdzygohIak0KP7a2trC3t4cgCPDz84OdnZ325ejoiMmTJ2PGjBmNvt+iRYswdepUREREoHv37li8eDHMzc2xbNmyW35GrVZj4sSJeOedd26YVi+KIj777DO89dZbGD16NAIDA7Fy5UpcvnwZv/32W1Metd3h1hZtW33rz88JGSgoq5a4GiIi49ak2V6xsbEQRRH3338/NmzYAHt7e+05pVIJLy8vuLu7N+pe1dXVSEhIwNy5c7XHZDIZwsLCsH///lt+7t1334WzszOmTJmCPXv2NDiXmpqKrKwshIWFaY/Z2NggNDQU+/fvx/jx4296z6qqKu0WHUBdC1d7Ul2r0e4hxTE/bVM/XwcEdLDB0UtFWLn/AmaH+UldEhGR0WpS+Bk0aBCAupDh6ekJmUz3TeHz8vKgVqvh4tJw+q+LiwtOnTp108/s3bsX33//PZKSkm56PisrS3uPf9+z/tzNREVF4Z133mlC9W1LfUuCXCbA1szkDleTFARBwLSBvnhx7WGs3J+G5wZ2gplSLnVZRERGSaf04uXlBZlMhvLycpw6dQrJyckNXi2hpKQETz75JJYuXQpHR0e93nvu3LkoKirSvjIyMvR6/5ZWP97HwUIJmYxbW7RVw3u6wsPODAVl1fgl8aLU5RARGS2dFjnMzc1FREQEtmzZctPzavWdN3J0dHSEXC5HdnZ2g+PZ2dlwdXW94fpz587hwoULeOihh7THNBoNAEChUOD06dPaz2VnZ8PNza3BPXv16nXLWlQqFVSq9ttdxGnu7YNCLsOz/X3wn99P4Ls95/FESEfIGVaJiFqdTi0/s2fPRmFhIeLi4mBmZobo6GisWLECXbp0webNmxt1D6VSieDgYMTExGiPaTQaxMTEoF+/fjdc7+/vj6NHjyIpKUn7GjVqFO677z4kJSXB09MTPj4+cHV1bXDP4uJixMXF3fSehoL7erUfj9/lCVtzE6Tll2Pb8Vt3xRIRUcvRqeVnx44d2LRpE/r27QuZTAYvLy888MADsLa2RlRUFEaOHNmo+0RGRuLpp59G3759ERISgs8++wxlZWWIiIgAADz11FPo0KEDoqKiYGpqip49ezb4vK2tLQA0OD579my8//776NKlC3x8fPD222/D3d39hvWADMn1O7pT22auVODJu73w5Y4ULNl9HsN6ukIQ2PpDRNSadAo/ZWVl2vV87OzskJubCz8/PwQEBCAxMbHR9xk3bhxyc3Mxf/58ZGVloVevXoiOjtYOWE5PT2/yoOrXXnsNZWVlmDZtGgoLC9G/f39ER0fD1NS0SfdpT7jGT/vyVD9vLNl9HkkZhTh44QpCfOzv/CEiItIbncJP165dcfr0aXh7eyMoKAhLliyBt7c3Fi9e3GCsTWPMnDkTM2fOvOm5nTt33vazP/zwww3HBEHAu+++i3fffbdJdbRn2gHPbPlpF5ysVHi0jwfWxqfj293nGH6IiFqZTmN+Zs2ahczMTADAggULsGXLFnTs2BFffPEFPvzwQ70WSHeWxwHP7c7UAT4QBODvkzlIySmRuhwiIqOiU8vPpEmTtO+Dg4ORlpaGU6dOoWPHjnqfhk53llfCHd3bG18nSzzQzQXbTmRj6e5UfPxYoNQlEREZDd1XKbyOubk5+vTpw+AjEbb8tE/PDarb8uLXw5eQU2zYe88REbUlOrX8qNVq/PDDD4iJiUFOTo52vZ16O3bs0EtxdGdqjYiC8vp9vTjmpz0J9rJHsJcdEtKu4Id/LuC1Yf5Sl0REZBR0HvMza9YsqNVq9OzZE0FBQQ1e1HoKyqohioAgAPbmDD/tTf2Gp6sPpKG0qlbiaoiIjINOLT/r1q3DTz/9hBEjRui7Hmqi+i4ve3MlFHK99GJSK3qgmwt8HS1wPq8M6w9mYEp/H6lLIiIyeDr9tlQqlejcubO+ayEdcLxP+yaTCXh2QF3rz7K9qahRa+7wCSIiai6dws+cOXPw+eefQxRFfddDTaQNPxzv02490qcDHC2VuFRYgb+OZkpdDhGRwWt0t9cjjzzS4OsdO3Zgy5Yt6NGjB0xMTBqc27hxo36qozviNPf2z9REjqf7eeO/289gya7zGBXkzi0viIhaUKPDj42NTYOvH374Yb0XQ02nXd3ZguGnPZt0txe+3nkOJzKLsS8lH/27cNkIIqKW0ujws3z58pasg3SUy24vg2BnocS4uzzxwz8XsGT3OYYfIqIW1KzpQTk5OdizZw/27NmDnJwcfdVETXBtR3e2/LR3U/r7QCYAe87m4fjlIqnLISIyWDqFn+LiYjz55JPo0KEDBg0ahEGDBqFDhw6YNGkSior4Q7s15ZVwR3dD4WlvjhEBdRsDL919XuJqiIgMl07hZ+rUqYiLi8Mff/yBwsJCFBYW4o8//sChQ4fw3HPP6btGuo38Mk51NyTPDewEAPg9OROXCiskroaIyDDpFH7++OMPLFu2DOHh4bC2toa1tTXCw8OxdOlS/P777/qukW5BoxGRX8qtLQxJgIcN+vk6QK0RsWxvqtTlEBEZJJ3Cj4ODww2zv4C6GWF2dnbNLooap6iiBrWaurWWONvLcEy7uuHpuvh0FFXUSFwNEZHh0Sn8vPXWW4iMjERWVpb2WFZWFl599VW8/fbbeiuObq9+mruNmQmUCm5tYSgG+zmhq4sVyqrVWBOXJnU5REQGR6e9vb755hukpKSgY8eO6NixIwAgPT0dKpUKubm5WLJkifbaxMRE/VRKN9BOc7dkl5chEQQBUwf64pWfj2D5vguY0t8HKoVc6rKIiAyGTuFnzJgxei6DdMFp7oZrVJA7Pt16GlnFldh0+DIev8tT6pKIiAyGTuFnwYIF+q6DdFA/zd3RiuHH0CgVMkTc642oLafw7Z7zeCzYAzIZt7wgItIHnQaKZGRk4OLFi9qv4+PjMXv2bHz77bd6K4zuTLupqQW7vQzRhNCOsFQpkJJTitjTXESUiEhfdAo/TzzxBGJjYwHUDXQOCwtDfHw85s2bh3fffVevBdKtacMPu70MkrWpCZ4IrRtTt4SLHhIR6Y1O4efYsWMICQkBAPz0008ICAjAP//8gzVr1uCHH37QZ310G9oxP+z2MlgR93pDIRMQn1qApIxCqcshIjIIOoWfmpoaqFR1v3D//vtvjBo1CgDg7++PzMxM/VVHt8WWH8PnZmOG0b06AAC+3X1O4mqIiAyDTuGnR48eWLx4Mfbs2YPt27dj2LBhAIDLly/DwcFBrwXSrWlXd+ZUd4M2bWDdoofRx7KQll8mcTVERO2fTuHn448/xpIlSzB48GBMmDABQUFBAIDNmzdru8OoZYmieN06P2z5MWRdXa0wuKsTNCLw3R5ueUFE1Fw6TXUfPHgw8vLyUFxc3GA7i2nTpsHc3FxvxdGtlVTVorpWAwBw4pgfgzdtoC92ns7FzwkZePkBP9hzhh8Rkc503hNBLpejtrYWe/fuxd69e5Gbmwtvb284Ozvrsz66hfo1fixVCpiacPVfQ9fP1wEBHWxQWaPByv0XpC6HiKhd0yn8lJWVYfLkyXBzc8PAgQMxcOBAuLu7Y8qUKSgvL9d3jXQTeRzvY1QEQdCO/Vm5Pw0V1WqJKyIiar90Cj+RkZHYtWsXfv/9dxQWFqKwsBCbNm3Crl27MGfOHH3XSDfBmV7GZ3hPV3jYmaGgrBq/JF688weIiOimdAo/GzZswPfff4/hw4fD2toa1tbWGDFiBJYuXYpffvlF3zXSTdSHHwe2/BgNhVyGZ/v7AAC+23Meao0ocUVERO2TTuGnvLwcLi4uNxx3dnZmt1cr0e7rxZYfo/L4XZ6wNTdBWn45th3PkrocIqJ2Safw069fPyxYsACVlZXaYxUVFXjnnXfQr18/vRVHt5bLHd2NkrlSgSfv9gIALN59HqLI1h8ioqbSaar7559/jvDwcHh4eGjX+Dly5AhMTU2xdetWvRZIN6cd88Np7kbnqX7eWLL7PI5kFCI+tQChvlxYlIioKXQKPz179sTZs2exZs0anDp1CgAwYcIETJw4EWZmZnotkG6uPvw4ccyP0XGyUuHRPh5YG5+Ob3efZ/ghImoincIPAJibm2Pq1Kn6rIWaIJ/dXkZt6gAfrDuYjphTOTibXYIuLlZSl0RE1G7oHH7Onj2L2NhY5OTkQKPRNDg3f/78ZhdGt8ep7sbN18kSD3RzwbYT2Vi65zwWPhYkdUlERO2GTuFn6dKlmD59OhwdHeHq6gpBELTnBEFg+Glh5dW1KL+6yB3H/Biv5wb5YtuJbPx2+DJeGdoVztamUpdERNQu6BR+3n//fXzwwQd4/fXX9V0PNUJeSV2Xl6mJDBZKbm1hrIK97BHsZYeEtCtY/s8FvD7MX+qSiIjaBZ2mul+5cgVjx47Vdy3USNfv5n59qxsZn/otL1YfSENpVa3E1RARtQ86hZ+xY8di27Zt+q6FGuna6s7s8jJ2D3Rzga+jBUoqa7EuPl3qcoiI2oVGd3t98cUX2vedO3fG22+/jQMHDiAgIAAmJiYNrn3ppZf0VyHdgNPcqZ5MJuDZAb5489ej+OGfC5h8rw9kMrYGEhHdTqPDz//93/81+NrS0hK7du3Crl27GhwXBIHhp4XVj/nhTC8CgId7d8BHW07i4pUK7D6bi8FdnaUuiYioTWt0+ElNTW3JOqgJOM2drmemlOPRYA8s33cBqw+kM/wQEd1Bk8f8FBcXY/v27fjzzz+Rm5vbEjXRHVwLP+z2ojoTQzsCAHacysblwgqJqyEiatuaFH6SkpLg7++P8PBwPPTQQ+jcuTP38pKAdnVnrvFDV3V2tkKojz00IrDuYIbU5RARtWlNCj+vv/46fHx8sG/fPiQkJGDIkCGYOXNmS9VGt8BuL7qZSVd3e18Xn44ateYOVxMRGa8mLXKYkJCAbdu2oU+fPgCAZcuWwd7eHsXFxbC2tm6RAulGuQw/dBPhPVzhYKFETkkVYk7mYFhPV6lLIiJqk5rU8lNQUAAPDw/t17a2trCwsEB+fr7eC6Obq6xRo6SybjE7J4Yfuo5SIcPjd3kCANbEpUlcDRFR29Xk7S1OnDiBrKws7deiKOLkyZMoKSnRHgsMDNRPdXSD/LK68T4mcgHWZjrvS0sG6omQjli86xz2nM3DhbwyeDtaSF0SEVGb0+TfnkOGDIEoig2OPfjggxAEAaIoQhAEqNVqvRVIDeWVXF3d2YJbW9CNPO3NMcjPCTtP52JtfDrmjugmdUlERG1Ok8IP1/qRnnawsxWnudPNTQz1ws7TufjpUAYih/pBpeDmt0RE12tS+PHy8mqpOqiRONOL7uS+rk5wszFFZlEloo9lYXSvDlKXRETUpui0sen1AgICkJHBdUVaS14pt7ag21PIZRh/V92ih6sPcOAzEdG/NTv8XLhwATU1NfqohRoht4QtP3Rn4+7yhFwm4OCFKzidVXLnDxARGZFmhx9qXdzaghrD1cYUD3RzAQD8yGnvREQNNDv8DBgwAGZmZvqohRqhfmsLJ25tQXcw8e66rq+NiZdQXl0rcTVERG1Hs8PPX3/9BTc3N33UQo3AAc/UWPd2coSXgzlKqmrx+5HLUpdDRNRmNDn85OfnIzY2FgUFBQCAvLw8fPzxx3j33Xdx8uTJJhfw1VdfwdvbG6ampggNDUV8fPwtr924cSP69u2rXVm6V69eWLVqVYNrnnnmGQiC0OA1bNiwJtfVVjH8UGPJZAKeCKkf+JwucTVERG1Hk8JPfHw8OnXqhCFDhqBz585ISEhASEgIvv/+e6xcuRLBwcFITExs9P3Wr1+PyMhILFiwAImJiQgKCkJ4eDhycnJuer29vT3mzZuH/fv3Izk5GREREYiIiLhhZ/lhw4YhMzNT+1q7dm1THrPNqlFrcKW8bnA5x/xQYzwW7AGlXIajl4qQfLFQ6nKIiNqEJoWfefPmYezYsSgqKsKbb76JMWPGYMiQIThz5gxSUlIwfvx4vPfee42+36JFizB16lRERESge/fuWLx4MczNzbFs2bKbXj948GA8/PDD6NatGzp16oRZs2YhMDAQe/fubXCdSqWCq6ur9mVnZ9eUx2yzCq5ubSETADtzhh+6MwdLFUYE1G1wuoatP0REAJoYfhISEhAZGQkrKyvMmjULly9fxtSpU7XnZ86ciYMHDzbqXtXV1UhISEBYWNi1YmQyhIWFYf/+/Xf8vCiKiImJwenTpzFw4MAG53bu3AlnZ2d07doV06dPv+PGq1VVVSguLm7waovqp7nbW6ggk3FrC2qciXfXLU66+chlFFVwWQoioiaFn+rqau3MLhMTE5ibm8PR0VF73tHRsdE7vOfl5UGtVsPFxaXBcRcXlwYbp/5bUVERLC0toVQqMXLkSHz55Zd44IEHtOeHDRuGlStXIiYmBh9//DF27dqF4cOH33a/saioKNjY2Ghfnp6ejXqG1sZp7qSLvl528HOxREWNGr8dviR1OUREkmtS+PH09MT58+e1X69bt67BTK/MzMwGYaglWFlZISkpCQcPHsQHH3yAyMhI7Ny5U3t+/PjxGDVqFAICAjBmzBj88ccfOHjwYINr/m3u3LkoKirSvtrqitV5nOZOOhAEARND61p/Vh9Iu2FjYiIiY9Ok8DN+/PgGg5FHjhzZYI2fzZs3IyQkpFH3cnR0hFwuR3Z2doPj2dnZcHV1vXXBMhk6d+6MXr16Yc6cOXjssccQFRV1y+t9fX3h6OiIlJSUW16jUqlgbW3d4NUWcaYX6erhPh1gZiLH2ZxSHLxwRepyiIgk1aTws2DBAowfP/6W5+fNm4cff/yxUfdSKpUIDg5GTEyM9phGo0FMTAz69evX6Jo0Gg2qqqpuef7ixYvIz883iLWI8krY7UW6sTY1wehe7gCANVzxmYiMXLMXOdy3b582fJibm0OlanyrRGRkJJYuXYoVK1bg5MmTmD59OsrKyhAREQEAeOqppzB37lzt9VFRUdi+fTvOnz+PkydP4r///S9WrVqFSZMmAQBKS0vx6quv4sCBA7hw4QJiYmIwevRodO7cGeHh4c19VMnll3FTU9JdfdfXlqNZyC+99X8wEBEZOkVzbzB8+HAkJSXB19e3yZ8dN24ccnNzMX/+fGRlZaFXr16Ijo7WDoJOT0+HTHYtn5WVleGFF17AxYsXYWZmBn9/f6xevRrjxo0DAMjlciQnJ2PFihUoLCyEu7s7hg4divfee69JoaytYrcXNUeAhw0CPWyQfLEIvyRcxHODOkldEhGRJASxmaMfrayscOTIEZ3CT1tVXFwMGxsbFBUVtanxP8M+241TWSVYMTkEg/ycpC6H2qH1B9Px+oaj8HIwR+ycwVwygYgMSmN/f3NX93akfrYXx/yQrh4KcoeVqQJp+eXYm5IndTlERJJodvhZsmTJDWv1kP6pNSIKyuq6vZzY7UU6Mlcq8GgfDwAc+ExExqtZ4SclJQUODg7acTlcP6TlXCmvhubqX6+dBVt+SHdPhNZtdvr3yRxkFVVKXA0RUevTKfzk5+cjLCwMfn5+GDFiBDIzMwEAU6ZMwZw5c/RaINWpH+xsZ24CEzl7K0l3fi5WCPG2h1ojYv3BtrmgJxFRS9Lpt+jLL78MhUKB9PR0mJuba4+PGzcO0dHReiuOrskr4TR30p+Jd9e1/qyNT0etWiNxNURErUunqe7btm3D1q1b4eHh0eB4ly5dkJbGcQQtgdPcSZ+G9XSFvYUSWcWV2HEqB0N73HpVdSIiQ6NTy09ZWVmDFp96BQUFBrGeTlukDT/c14v0QKWQY2zf+oHP6RJXQ0TUunQKPwMGDMDKlSu1XwuCAI1Gg4ULF+K+++7TW3F0TS53dCc9eyKkrutr99lcpOeXS1wNEVHr0anba+HChRgyZAgOHTqE6upqvPbaazh+/DgKCgqwb98+fddIAPJLOeaH9MvLwQIDujhiz9k8rD2YjteH+UtdEhFRq9Cp5adnz544c+YM+vfvj9GjR6OsrAyPPPIIDh8+jE6duGR+S6jv9uIaP6RP9ft9/XQwA1W1aomrISJqHTrv7WVjY4N58+bpsxa6jWtjftjtRfoT1s0ZLtYqZBdXYevxbIwKcpe6JCKiFtfo8JOcnNzomwYGBupUDN0ap7pTS1DIZRh/V0d8HnMWaw6kMfwQkVFodPjp1asXBEGAKIoQhGubIdav6nz9MbWazef6JIoi8q9ubeHA8EN6Nj7EE1/uOIu41AKk5JSgs7OV1CUREbWoRo/5SU1Nxfnz55GamooNGzbAx8cHX3/9NZKSkpCUlISvv/4anTp1woYNG1qyXqNUVFGDGnVdyHTg1hakZ242ZhjSrW5/Pk57JyJj0OiWHy8vL+37sWPH4osvvsCIESO0xwIDA+Hp6Ym3334bY8aM0WuRxq5+vI+VqQKmJnKJqyFDNDG0I7afyMaGhIt4LdwfZkr+OyMiw6XTbK+jR4/Cx8fnhuM+Pj44ceJEs4uihnKvjvfhTC9qKQO7OMHT3gzFlbX4Pfmy1OUQEbUoncJPt27dEBUVherqau2x6upqREVFoVu3bnorjupwawtqaTKZgCdC6lp32fVFRIZOp6nuixcvxkMPPQQPDw/tzK7k5GQIgoDff/9drwUSp7lT6xjb1wOLtp/GkYxCHLtUhJ4dbKQuiYioRejU8hMSEoLz58/j/fffR2BgIAIDA/HBBx/g/PnzCAkJ0XeNRo+rO1NrcLRUYVhPNwBs/SEiw6ZTy09ZWRksLCwwbdo0fddDN8FuL2otk0I74vcjl7Ep6RLeHOEPK1MTqUsiItI7nVp+XFxcMHnyZOzdu1ff9dBNMPxQawnxsUdnZ0uUV6vx2+FLUpdDRNQidAo/q1evRkFBAe6//374+fnho48+wuXLnCHSUnK13V4c80MtSxAETAyt2+19TVy6dhFTIiJDolP4GTNmDH777TdcunQJzz//PH788Ud4eXnhwQcfxMaNG1FbW6vvOo1aXglXd6bW80hvD5iayHAqqwSJ6VekLoeISO90Cj/1nJycEBkZieTkZCxatAh///03HnvsMbi7u2P+/PkoLy/XV51GSxRF7uhOrcrG3AQPBdbt8bXmAAc+E5HhaVb4yc7OxsKFC9G9e3e88cYbeOyxxxATE4P//ve/2LhxI1d61oPSqlpU1WoAcKo7tZ5Jd9et+fPH0UxcKau+w9VERO2LTrO9Nm7ciOXLl2Pr1q3o3r07XnjhBUyaNAm2trbaa+655x4ueKgHeVfH+5gr5TBX6vTtImqyQA8b9OxgjWOXivFLwkVMHegrdUlERHqjU8tPREQE3N3dsW/fPiQlJWHmzJkNgg8AuLu7Y968efqo0ahxphdJoW7gc13rz4/x6dBoOPCZiAyHTk0JmZmZMDc3v+01ZmZmWLBggU5F0TX1g50504ta26ggd3zw50mk5pVh//l83NvZUeqSiIj0QqfwY25uDo1Gg5SUFOTk5ECj0TQ4P3DgQL0UR2z5IelYqBR4uHcHrDqQhjVxaQw/RGQwdAo/Bw4cwBNPPIG0tLQb1gERBAFqtVovxdG1MT+OVgw/1Pom3t0Rqw6kYdvxbOQUV8LZ2lTqkoiImk2nMT/PP/88+vbti2PHjqGgoABXrlzRvgoKCvRdo1Fjyw9Jyd/VGn297FCrEbH+YIbU5RAR6YVOLT9nz57FL7/8gs6dO+u7HvqXa2v8cMwPSWPi3R1xKO0K1san44X7OkMuE6QuiYioWXRq+QkNDUVKSoq+a6GbyOOO7iSx4T3dYGtugstFldh5OkfqcoiImq3RLT/Jycna9y+++CLmzJmDrKwsBAQEwMSk4c7PgYGB+qvQyNW3/HBrC5KKqYkcY4M9sHRPKtbEpWNINxepSyIiapZGh59evXpBEIQGA5wnT56sfV9/jgOe9YtT3akteCLUC0v3pCL2dA4yCsrhaX/7pS6IiNqyRoef1NTUlqyDbqKiWo2y6rogydleJCUfRwv07+yIvSl5WHcwHa+G+0tdEhGRzhodfry8vFqyDrqJ+i4vpUIGKxW3tiBpTQztiL0peVh/8CJmDfGDUtGsrQGJiCSj80+vVatW4d5774W7uzvS0tIAAJ999hk2bdqkt+KMXe51u7kLAmfYkLTCurvAyUqFvNIqbD+RLXU5REQ60yn8fPPNN4iMjMSIESNQWFioHeNja2uLzz77TJ/1GTWO96G2xEQuw/i7PAEAa+LSJK6GiEh3OoWfL7/8EkuXLsW8efMgl8u1x/v27YujR4/qrThjx2nu1NaMD+kImQD8cy4f53JLpS6HiEgnOoWf1NRU9O7d+4bjKpUKZWVlzS6K6uRzdWdqYzrYmuF+f2cAwI9x6RJXQ0SkG53Cj4+PD5KSkm44Hh0djW7dujW3JrpKu7WFFbu9qO2YGFo3+eGXhIuorOGyFkTU/ug0hSgyMhIzZsxAZWUlRFFEfHw81q5di6ioKHz33Xf6rtFosduL2qKBfk7oYGuGS4UV+DM5E48Ge0hdEhFRk+gUfp599lmYmZnhrbfeQnl5OZ544gm4u7vj888/x/jx4/Vdo9HK5erO1AbJZQKeCO2IT7aexpq4NIYfImp3dJ7qPnHiRJw9exalpaXIysrCxYsXMWXKFH3WZvSu7ejObi9qWx7v6wmFTEBieiFOXC6WuhwioiZp9ipl5ubmcHZ21kct9C/1U92d2PJDbYyTlQrhPV0BAKs57Z2I2plGd3v17t270QvtJSYm6lwQ1amqVaO4shYAx/xQ2zQp1At/Jmfil0MXMW2AL7wdLaQuiYioURrd8jNmzBiMHj0ao0ePRnh4OM6dOweVSoXBgwdj8ODBMDU1xblz5xAeHt6S9RqN/KuDnRUyATZmJhJXQ3Sju33tMdDPCdVqDd7944TU5RARNVqjW34WLFigff/ss8/ipZdewnvvvXfDNRkZGfqrzojlaQc7KyGTcWsLansEQcCCh7pj2Ge7seNUDv4+kY2w7i5Sl0VEdEc6jfn5+eef8dRTT91wfNKkSdiwYUOzi6LrBzuzy4vark5OlpjS3xcA8M4fx7nuDxG1CzqFHzMzM+zbt++G4/v27YOpqWmziyKu8UPtx4v3d4artSkyCirw7e7zUpdDRHRHOq3zM3v2bEyfPh2JiYkICQkBAMTFxWHZsmV4++239VqgsWLLD7UXFioF5o3shhfXHsZXsSl4uHcHeNqbS10WEdEt6RR+3njjDfj6+uLzzz/H6tWrAQDdunXD8uXL8fjjj+u1QGOVV3K15YdbW1A78GCgG9bEpeHA+QJ88OdJLH4yWOqSiIhuSafwAwCPP/44g04L0rb8WLDlh9o+QRDwzqieGPHFHkQfz8LuM7kY6OckdVlERDfV7EUOqWVwU1Nqb7q6WuHpft4AgP/8fhzVtRppCyIiuoVGt/zY2dk1epHDgoICnQuiOhzzQ+3R7Ae6YPORSzifW4bl+1Lx3KBOUpdERHSDRoefzz77rAXLoH/jbC9qj6xNTfDG8G545ecj+CLmLEb36gBXG84AJaK2pdHh5+mnn27JOug6tWoNrpQz/FD79EjvDvgxLg2J6YWI2nISn4/vLXVJREQNNHvMT2VlJYqLixu8qHkKyqohioBMAOwtOOaH2heZTMC7o3tCEIBNSZcRdz5f6pKIiBrQKfyUlZVh5syZcHZ2hoWFBezs7Bq8muKrr76Ct7c3TE1NERoaivj4+Fteu3HjRvTt2xe2trawsLBAr169sGrVqgbXiKKI+fPnw83NDWZmZggLC8PZs2d1eUzJ5F4d72NvoYScW1tQO9Szgw2eCOkIAFiw+Thq1Rz8TERth07h57XXXsOOHTvwzTffQKVS4bvvvsM777wDd3d3rFy5stH3Wb9+PSIjI7FgwQIkJiYiKCgI4eHhyMnJuen19vb2mDdvHvbv34/k5GREREQgIiICW7du1V6zcOFCfPHFF1i8eDHi4uJgYWGB8PBwVFZW6vKokuB4HzIErwztCltzE5zKKsHqA2lSl0NEdI2oA09PTzE2NlYURVG0srISz549K4qiKK5cuVIcPnx4o+8TEhIizpgxQ/u1Wq0W3d3dxaioqEbfo3fv3uJbb70liqIoajQa0dXVVfzkk0+05wsLC0WVSiWuXbv2lveorKwUi4qKtK+MjAwRgFhUVNToOvRpQ0KG6PX6H+LEpQck+fOJ9GX1gQui1+t/iD0XRIu5JZVSl0NEBq6oqKhRv791avkpKCiAr2/dZobW1tbaqe39+/fH7t27G3WP6upqJCQkICwsTHtMJpMhLCwM+/fvv+PnRVFETEwMTp8+jYEDBwIAUlNTkZWV1eCeNjY2CA0Nve09o6KiYGNjo315eno26hlayrVp7hzvQ+3b+Ls6omcHa5RU1mJh9CmpyyEiAqBjt5evry9SU1MBAP7+/vjpp58AAL///jtsbW0bdY+8vDyo1Wq4uLg0OO7i4oKsrKxbfq6oqAiWlpZQKpUYOXIkvvzySzzwwAMAoP1cU+85d+5cFBUVaV8ZGRmNeoaWUt/t5cBuL2rn5LK6lZ8B4KdDF5GYfkXiioiIdAw/EREROHLkCIC6fb6++uormJqa4uWXX8arr76q1wL/zcrKCklJSTh48CA++OADREZGYufOnc26p0qlgrW1dYOXlPJKuMAhGY5gLzs8FuwBAFiw6TjUGlHiiojI2Om0t9fLL7+sfR8WFoZTp04hISEBnTt3RmBgYKPu4ejoCLlcjuzs7AbHs7Oz4erqesvPyWQydO7cGQDQq1cvnDx5ElFRURg8eLD2c9nZ2XBzc2twz169ejX28SSXy24vMjCvD/PH1mNZOHqpCOsPZuCJ0I5Sl0RERqzJLT81NTUYMmRIg+njXl5eeOSRRxodfABAqVQiODgYMTEx2mMajQYxMTHo169fo++j0WhQVVUXFnx8fODq6trgnsXFxYiLi2vSPaWmne1lxZYfMgxOViq8/IAfAGDh1lO4UlYtcUVEZMyaHH5MTEyQnJyslz88MjISS5cuxYoVK3Dy5ElMnz4dZWVliIiIAAA89dRTmDt3rvb6qKgobN++HefPn8fJkyfx3//+F6tWrcKkSZMA1O0sPXv2bLz//vvYvHkzjh49iqeeegru7u4YM2aMXmpuDfUDnp3Y7UUG5Kl+XujqYoXC8hr8d/tpqcshIiOmU7fXpEmT8P333+Ojjz5q1h8+btw45ObmYv78+cjKykKvXr0QHR2tHbCcnp4OmexaPisrK8MLL7yAixcvwszMDP7+/li9ejXGjRunvea1115DWVkZpk2bhsLCQvTv3x/R0dEwNW0f+wtpNCIKyrjODxkehVyGd0b3wPhvD2BNXPrVmWA2UpdFREZIEEWxyaMPX3zxRaxcuRJdunRBcHAwLCwsGpxftGiR3gqUQnFxMWxsbFBUVNTqg5/zS6sQ/P7fAICzHwyHibzZO5AQtSkvrT2MzUcuo09HW/zy/D2QcRVzItKTxv7+1qnl59ixY+jTpw8A4MyZMw3OCQJ/kDVH/XgfW3MTBh8ySG+O6Ia/T2YjMb0Qvx6+hEevzgQjImotOoWf2NhYfddBV+WXcpo7GTZXG1O8NKQLPtpyClFbTuGBHi6wNjWRuiwiMiLNalpISUnB1q1bUVFRAaBu1WVqHk5zJ2Mw+V4f+DpaIK+0Cp//3b42Hiai9k+n8JOfn48hQ4bAz88PI0aMQGZmJgBgypQpmDNnjl4LNDZc3ZmMgVIhw39G9QAA/PDPBZzJLpG4IiIyJjqFn5dffhkmJiZIT0+Hubm59vi4ceMQHR2tt+KMEae5k7EY6OeE8B4uUGtELNh0nC3HRNRqdAo/27Ztw8cffwwPj4YDFbt06YK0tDS9FGasrm1twW4vMnxvjewOlUKG/efz8efRTKnLISIjoVP4KSsra9DiU6+goAAqFVssmiOPA57JiHjam+OFwXXb1Xzw50mUVdVKXBERGQOdws+AAQOwcuVK7deCIECj0WDhwoW477779FacMdJubcHwQ0biuUG+8LQ3Q2ZRJf4XmyJ1OURkBHSa6r5w4UIMGTIEhw4dQnV1NV577TUcP34cBQUF2Ldvn75rNCralh/u60VGwtREjvkP9sDUlYfw3Z7zGBvsAV8nS6nLIiIDplPLT8+ePXHmzBn0798fo0ePRllZGR555BEcPnwYnTp10neNRkMUReRrW3445oeMR1g3Zwzu6oQatYj//H6Cg5+JqEXp1PIDADY2Npg3b54+azF6xRW1qFZrALDbi4yLIAhY8FAP/JOyG7vP5GL7iWwM7eEqdVlEZKB0avlZvnw5fv755xuO//zzz1ixYkWzizJW9QscWqkUMDWRS1wNUevycbTAswN8AADv/nEClTVqiSsiIkOlU/iJioqCo6PjDcednZ3x4YcfNrsoY5XP8T5k5Gbe3xluNqa4eKUCi3edk7ocIjJQOoWf9PR0+Pj43HDcy8sL6enpzS7KWOVxvA8ZOXOlAvNGdgMAfLPzHDIKyiWuiIgMkU7hx9nZGcnJyTccP3LkCBwcHJpdlLGqn+nlYMGWHzJeIwPccE8nB1TVavDeHyekLoeIDJBO4WfChAl46aWXEBsbC7VaDbVajR07dmDWrFkYP368vms0GtemubPlh4yXIAh4Z1QPKGQCtp3Ixs7TOVKXREQGRqfw89577yE0NBRDhgyBmZkZzMzMMHToUNx///0c89MMXN2ZqE4XFys8c483AOCd30+gqpaDn4lIf3QKP0qlEuvXr8epU6ewZs0abNy4EefOncOyZcugVLLVQle5JVzdmajerLAucLRUITWvDMv2XpC6HCIyIDqFn9jYWACAn58fxo4diwcffBBeXl56LcwYseWH6BorUxO8OcIfAPDljrPILKqQuCIiMhQ6hZ9hw4ahU6dOeP/993Hx4kV912S06sOPE8f8EAEAHu7dAX297FBercaHf52SuhwiMhA6hZ9Lly5h5syZ+OWXX+Dj44Pw8HD89NNPqK6u1nd9RkMURbb8EP2LIAh4Z3QPyATg9yOXsf9cvtQlEZEB0Cn8ODo64uWXX0ZSUhLi4uLg5+eHF154Ae7u7njppZdw5MgRfddp8Mqq1ais4dYWRP/Ww90GE0PrutX/s/k4aq5uAUNEpCudws/1+vTpg7lz52LmzJkoLS3FsmXLEBwcjAEDBuD48eP6qNEo1K/ubGYih4VK5y3XiAzSnKF+sDM3wensEqzcnyZ1OUTUzukcfmpqavDLL79gxIgR8PLywtatW/G///0P2dnZSElJgZeXF8aOHavPWg0a1/ghujVbcyVeG1Y3+Pmz7WeQU1IpcUVE1J7pFH5efPFFuLm54bnnnoOfnx8OHz6M/fv349lnn4WFhQW8vb3x6aef4tQpDlBsrPpp7lzdmejmHu/riUAPG5RU1eLjLaelLoeI2jGdws+JEyfw5Zdf4vLly/jss8/Qs2fPG65xdHTUTomnO+NgZ6Lbk8vqVn4GgA2JF5GQViBxRUTUXukUfmJiYjBhwgSoVLf+Ra1QKDBo0CCdCzM2nOZOdGe9O9rh8b4eAIDXfknW/v+GiKgpGj2ydvPmzY2+6ahRo3Qqxpix5YeocV4b5o+dp3NxLrcM45bsx49T74aLtanUZRFRO9Lo8DNmzJhGXScIAtRq7sPTVHnc2oKoURwtVVj/XD88sfSANgCtmXo3OtiaSV0aEbUTje720mg0jXox+OiGLT9EjefjaIGfnusHT3szXMgvx+OL9yM9v1zqsoionWjSgjKVlZX4+++/8eCDDwIA5s6di6qqa33uCoUC7777LkxN2QTdVNfCD8f8EDWGp705fnquH55YGofUvDI8vmQ/1kwNRScnS6lLI6I2rkkDnn/44QcsWbJE+/X//vc//PPPPzh8+DAOHz6MVatW4euvv9Z7kcYgr/Rqt5cVW36IGsvNxgzrp92NLs6WyCquxLglB3A6q0TqsoiojWtS+FmzZg2mTZvW4NiPP/6I2NhYxMbG4pNPPsHPP/+s1wKNQWWNGqVVtQDY7UXUVM7Wplg37W50c7NGXmkVxn+7H8cuFUldFhG1YU0KPykpKQgICNB+bWpqCpns2i1CQkJw4sQJ/VVnJOq7vJRyGaxNubUFUVM5WKqwdmoogjxscKW8Bk8sPYCkjEKpyyKiNqpJ4aewsLDBGJ/c3Fx4e3trv9ZoNA3OU+PUd3k5WCohCILE1RC1T7bmSqx6NhTBXnYorqzFpO/icPACF0Ikohs1Kfx4eHjg2LFjtzyfnJwMDw+PZhdlbPJKONOLSB+sTU2wcnII+vk6oLSqFk99H49/UvKkLouI2pgmhZ8RI0Zg/vz5qKy8cVPBiooKvPPOOxg5cqTeijMWnOlFpD8WKgWWR9yFgX5OqKhRI+KHg9h5OkfqsoioDWlS+HnzzTdRUFCArl274pNPPsGmTZuwadMmLFy4EF27dsWVK1fw5ptvtlStBotr/BDpl6mJHEufCkZYN2dU1WowbWUCth3PkrosImojmhR+XFxc8M8//6Bbt25444038PDDD+Phhx/G3Llz0b17d+zduxcuLi4tVavB4jR3Iv1TKeT4emIwRgS4olqtwQtrEvFncqbUZRFRG9DkqUU+Pj6Ijo5GQUEBUlJSAACdO3eGvb293oszFrls+SFqEUqFDF+M7w2VIhm/Hr6EF9cmoqo2CI/04dhEImOm87xqe3t7hISE6LMWo3VtwDPH/BDpm0Iuw6djg6BSyLDuYAbm/HwEVbUaTAjpKHVpRCSRJnV7UcuoH/PjxJYfohYhlwn48OEAPNXPC6IIzN14FCv+uSB1WUQkEYafNoBjfohankwm4J1RPTB1gA8AYMHm4/h29zmJqyIiKTD8SKy6VoOiihoAHPND1NIEQcCbI7rhxfs7AwA+/OsUvow5K3FVRNTaGH4kVlBW1+ojlwmwNTORuBoiwycIAuYM7YpXhvoBAP67/Qw+2XoKoihKXBkRtRaGH4nVj/ext1BCJuPWFkStZeb9XfDWyG4AgK9iz+H9P08yABEZCYYfiXGaO5F0nh3gi3dH9wAAfL83FW9vOgaNhgGIyNAx/EiM09yJpPVUP298/GgABAFYfSAdb2xMhpoBiMigMfxIrH6mF6e5E0ln3F0dsejxIMgE4KdDFxH5UxJq1RqpyyKiFsLwIzHtvl6c5k4kqYd7e+B/T/SBQiZgU9JlvLj2MKprGYCIDBHDj8S4oztR2zEiwA2LJwVDKZdhy7EsTF+dgMoatdRlEZGeMfxIjDu6E7UtYd1dsPTpvlApZIg5lYOpKw+hopoBiMiQMPxILK/k6urODD9EbcYgPyf8EBECc6Uce87m4Znl8SirqpW6LCLSE4YfibHlh6ht6tfJASsnh8BSpUBcagGe/D4OxZU1UpdFRHrA8CMhtUZEQXn9vl4c80PU1vT1tseaZ0NhY2aCxPRCTPouDoVX/z9LRO0Xw4+ECsqqIYqAIAD25gw/RG1RkKct1k69G/YWSiRfLMLDX/+DnadzpC6LiJqB4UdC9V1eduZKKOT8VhC1Vd3drbF+2t1wsVYhNa8Mzyw/iCk/HERqXpnUpRGRDvgbV0Kc5k7UfnRxscL2yEF4tr8PFDIBMadyMPT/diHqr5Mo4VggonZF8vDz1VdfwdvbG6ampggNDUV8fPwtr126dCkGDBgAOzs72NnZISws7Ibrn3nmGQiC0OA1bNiwln4MnXCwM1H7Ym1qgrce7I7o2QMxyM8JNWoRS3afx/3/3YWfD2VwXzCidkLS8LN+/XpERkZiwYIFSExMRFBQEMLDw5GTc/P+9J07d2LChAmIjY3F/v374enpiaFDh+LSpUsNrhs2bBgyMzO1r7Vr17bG4zQZp7kTtU+dnS3xQ8RdWPZMX/g4WiC3pAqv/pKMh7/eh8T0K1KXR0R3IGn4WbRoEaZOnYqIiAh0794dixcvhrm5OZYtW3bT69esWYMXXngBvXr1gr+/P7777jtoNBrExMQ0uE6lUsHV1VX7srOza43HaTK2/BC1X4Ig4H5/F2ydPRBvjvCHpUqBIxeL8MjX/yByfRKyiyulLpGIbkGy8FNdXY2EhASEhYVdK0YmQ1hYGPbv39+oe5SXl6Ompgb29vYNju/cuRPOzs7o2rUrpk+fjvz8/Nvep6qqCsXFxQ1erSFXu68Xx/wQtVdKhQzTBnbCjlcGYWywBwBg4+FLuO/TnfgqNoXbYxC1QZKFn7y8PKjVari4uDQ47uLigqysrEbd4/XXX4e7u3uDADVs2DCsXLkSMTEx+Pjjj7Fr1y4MHz4cavWtfwBFRUXBxsZG+/L09NTtoZqofkd3tvwQtX/OVqb4ZGwQNs24F7072qK8Wo1Ptp7G0P/bjW3HsyCKHA9E1FYopC5AVx999BHWrVuHnTt3wtTUVHt8/Pjx2vcBAQEIDAxEp06dsHPnTgwZMuSm95o7dy4iIyO1XxcXF7dKAMorqWv5cWL4ITIYQZ622PD8Pdh05BI+2nIK6QXlmLYqAQO6OGL+g93RxcVK6hKJjJ5kLT+Ojo6Qy+XIzs5ucDw7Oxuurq63/eynn36Kjz76CNu2bUNgYOBtr/X19YWjoyNSUlJueY1KpYK1tXWDV2vgmB8iwySTCXi4twd2zBmMGfd1glIuw56zeRj2+R78Z/NxFJVzajyRlCQLP0qlEsHBwQ0GK9cPXu7Xr98tP7dw4UK89957iI6ORt++fe/451y8eBH5+flwc3PTS936otGIKCir6/Zy4Do/RAbJQqXAq+H++DtyEIZ2d4FaI+KHfy5g8KexWH0gDWpOjSeShKSzvSIjI7F06VKsWLECJ0+exPTp01FWVoaIiAgAwFNPPYW5c+dqr//444/x9ttvY9myZfD29kZWVhaysrJQWloKACgtLcWrr76KAwcO4MKFC4iJicHo0aPRuXNnhIeHS/KMt1JUUYPaqz/4GH6IDFtHB3N8+1RfrJ4SCj8XS1wpr8Fbvx3Dg1/uxYHzt5+QQUT6J2n4GTduHD799FPMnz8fvXr1QlJSEqKjo7WDoNPT05GZmam9/ptvvkF1dTUee+wxuLm5aV+ffvopAEAulyM5ORmjRo2Cn58fpkyZguDgYOzZswcqVdvqWqrv8rI2VUClkEtcDRG1hv5dHPHXSwPwn4e6w9pUgZOZxRj/7QHMWJOIi1fKpS6PyGgIIqcg3KC4uBg2NjYoKipqsfE//5zLwxNL4+DrZIEdcwa3yJ9BRG1XQVk1Fm0/jR/j0qERAZVChucHdcLzgzrBTMn/ICLSRWN/f0u+vYWx4jR3IuNmb6HE+2MC8MeLAxDqY4+qWg0+jzmLIf/did+PXObUeKIWxPAjEU5zJyKgbsf4ddPuxtcT+6CDrRkuF1XixbWHMW7JARy7VCR1eUQGieFHItzRnYjqCYKAEQFuiJkzCJEP+MHURIb4CwV46H97MXfjUeRf/XlBRPrB8CMRrvFDRP9maiLHS0O6YMecwXgoyB2iCKyNT8d9n+7E1ztTkFPC/cKI9IHhRyLaMT9WDD9E1JC7rRm+nNAbPz3XDz3crVFcWYuF0adx94cxeGZ5PDYfucw9w4iaod1ub9HeseWHiO4kxMcem2f2x6+HL2FtfDoS0q5g5+lc7DydCyuVAiMC3PBInw64y9seMpkgdblE7QbDj0TyS7m6MxHdmVwm4LFgDzwW7IHUvDL8mngRGw9fwsUrFVh/KAPrD2XA094MD/f2wCO9O8Db0ULqkonaPK7zcxMtvc6PKIro+nY0qms12PPaffC0N9f7n0FEhkujEXHwQgE2Jl7Cn0czUVpVqz0X7GWHR/p0wIMB7rAxN5GwSqLW19jf3ww/N9HS4ae4sgaB/9kGADj57jAuaEZEOquoVmPbiSxsTLyEPWdzUb9dmFIuQ1h3ZzzaxwMD/ZxgIucQTzJ8jf39zW4vCdSv8WOhlDP4EFGzmCnlGN2rA0b36oCc4kpsSrqMDYkXcSqrBH8dzcJfR7PgYKHEqF7ueLSPB3q4W0MQOD6IjBvDjwQ404uIWoKztSmmDvTF1IG+OHG5GBsSL2JT0iXklVZj+b4LWL7vAvxcLPFIHw883LsDXKxNpS6ZSBIMPxLgTC8iamnd3a3R3b075g73x56zediQeBHbTmTjTHYpPtpyCgujT+Hezo54tI8HhvZwgbmSvw7IePBfuwS4ujMRtRaFXIb7/J1xn78ziipqsOVoJjYkXsTBC1ew52we9pzNg4VSjuFXp83f7ePAafNk8Bh+JFA/5octP0TUmmzMTDA+pCPGh3REWn4Zfj18CRsTLyG9oBy/JFzELwkX0cHWDA/37oCH+3RAJydLqUsmahEMPxLI5Y7uRCQxLwcLzA7zw6whXZCQdgUbEi/ij+RMXCqswP9iU/C/2BT08rTFg4FuuNvXAd3crCFnixAZCIYfCWi7vTjgmYgkJggC+nrbo6+3PRY81AN/n8zGxsRL2HUmF0kZhUjKKAQAWKoU6ONlhxBvO9zlbY8gT1uYmnC2KrVPDD8S0IYfC475IaK2w9REjgcD3fFgoDtyS6qw+chl7Dmbi4QLV1BSVYvdZ3Kx+0wugLp1hAI9bHCXjz1CvO0R7G0Ha1MuqkjtA8OPBPI51Z2I2jgnKxWm9PfBlP4+UGtEnMoqxsHUAhy8cAXxFwqQW1KFQ2lXcCjtCr7BOQgC4O9qXdcydDUQOXMqPbVRDD8S4FR3ImpP5DIBPdxt0MPdBs/c6wNRFJGWX474CwVXA1EBLuSX42RmMU5mFmPF/jQAgJeDOe7yrgtCd/nYw9vBnAssUpvA8NPKyqtrUV6tBsCp7kTUPgmCAG9HC3g7WuDxvp4AgJziShy8cAUHLxQgPrUAJ7OKkZZfjrT8uplkQF1r0l1Xxwzd5W3PQdQkGYafVpZXUtflpVLIYKniXz8RGQZna1OMDHTDyEA3AHV7GCakXdG2DB3JKEJuSZV2yw0AsKofRO1TF4YCPWw4iJpaBX/7trLc67q82PxLRIbK2tQE93V1xn1dnQEAlTVqJF8sQnxqPuIvXEFiWt0g6l1ncrGrfhC1QoYgDxvc5W2PPh3t0NnZEh52ZlBwU1bSM4afVsZp7kRkjExN5AjxsUeIjz0AQK0RcTKzGPFXW4YOXihAXmn11a6zK9rPKeUyeDuaw9fREp2cLdDJyRKdnCzh62QBK84uIx0x/LSy+vDjxPE+RGTE5DIBPTvYoGcHG0zuXzeIOjWv7OqYoSs4kVmM87mlqKrV4Ex2Kc5klwLHG97DxVqlDUOdnCzQybnuvau1KbfooNti+Gll9WN+ONOLiOgaQRDg62QJXydLjLurIwBAoxFxqbAC53JLcS63DOdzS7Xvc0uqkF1c9/rnXH6De5kr5fBxvNZKVN9i5ONowTFFBIDhp9VxmjsRUePIZAI87c3haW+OwV0bniuqqLkahsrqAlFOXTBKyy9HebUaxy8X4/jl4gafEQTAw87sutaiuhYjXydLOFoqOQ7TiDD8tDLu6E5E1Hw2Zibo3dEOvTvaNTheo9YgvaAc5/8VilJySlFcWYuMggpkFFRg5+ncBp+zNlWgk7MlfBws4GJjChcrFVysTeFsbQoXaxWcrUyhVHDgtaFg+Gll9as7O7Dlh4hI70zkMm2rzgNw0R4XRRH5ZdVXw1BdMKpvOcq4Uo7iylocTi/E4fTCW97bwUKpDUMuVqZ1Ian+/dXjDpYqrl3UDjD8tDJ2exERtT5BEOBoqYKjpQqhvg4NzlXWqHEhvwzncsqQXlCO7OJK5JRUXh1TVImc4ipUqzXIL6tGflk1Tmbe+s+RywQ4WarqWotuCErXvrY1N2E3m4QYflpZ/To/Tlbs9iIiagtMTeTwd7WGv6v1Tc+Loogr5TXILq7UhqHs4kpkl1Qiq6jqalCqRG5JFdQaEVnFlcgqrgRQdMs/U6mQwdlKBVfrulDkZKWCtZkJbMxMYHv1f23Mr/7v1RcHa+sPw08rqqxRo6SyFgBbfoiI2gtBEGBvoYS9hRLd3G4ekIC6tYvyS6uQVXx9q9HV9yWVyCqqRE5JFQrKqlFdq8HFKxW4eKWi0XWoFLIGYehmAcnGzAS21x2rD1QqBYPT9Rh+WlF+Wd14HxO5ABszLs5FRGRI5DIBzlcHSd9OVa1aO1U/52orUV5pFYoqalBUUYvC8moUV9Rc/brupRGBqloNckqqkFNS1eTaTE1ksDVT3hCKbMxMYK6Uw0wph4VSDnOlou69Sg4zEwXM698rFTA3kcNcJYdSLmv3XXYMP60o7+o/WAcLbm1BRGSsVAo5POzM4WFn3qjrNRoRpdW1KCqvC0LFFTUo/Fc4Kqqo0Z6//lVcWQNRBCprNMiqqe+Oax65TNAGIXOlAmYmNwYk86tBqu5/685ZXPfeXCmHt4MFnCTa7YDhpxVd29qC432IiKhxZDIB1qYmsDY1gWcTP6vRiCiprNWGocKK6obhqKIWFdW1KK9WX301fF9RrUZZtRoV1WpUqzUA6rr3SqpqUVJVC6DprVD13hvdA0/289b5883B8NOKONOLiIhak0wm1I0LMm/+UIsatQblV4PQrUNS3fHr398YqurO2VtI97uQ4acV5ZVyawsiImqfTOQy2JjJDGLMKperbEW5JWz5ISIikhrDTytSa0Qo5TJubUFERCQhQRRFUeoi2pri4mLY2NigqKgI1ta3XtNBF6IoQq0RoZAzdxIREelTY39/c8xPKxMEAQo5p7kTERFJhc0PREREZFQYfoiIiMioMPwQERGRUWH4ISIiIqPC8ENERERGheGHiIiIjArDDxERERkVhh8iIiIyKgw/REREZFQYfoiIiMioMPwQERGRUWH4ISIiIqPC8ENERERGhbu634QoigCA4uJiiSshIiKixqr/vV3/e/xWGH5uoqSkBADg6ekpcSVERETUVCUlJbCxsbnleUG8UzwyQhqNBpcvX4aVlRUEQdDbfYuLi+Hp6YmMjAxYW1vr7b5tmbE9M5/XsPF5DRuft/0TRRElJSVwd3eHTHbrkT1s+bkJmUwGDw+PFru/tbW1wfxDayxje2Y+r2Hj8xo2Pm/7drsWn3oc8ExERERGheGHiIiIjArDTytSqVRYsGABVCqV1KW0GmN7Zj6vYePzGjY+r/HggGciIiIyKmz5ISIiIqPC8ENERERGheGHiIiIjArDDxERERkVhp9W9NVXX8Hb2xumpqYIDQ1FfHy81CXpZPfu3XjooYfg7u4OQRDw22+/NTgviiLmz58PNzc3mJmZISwsDGfPnm1wTUFBASZOnAhra2vY2tpiypQpKC0tbcWnaLyoqCjcddddsLKygrOzM8aMGYPTp083uKayshIzZsyAg4MDLC0t8eijjyI7O7vBNenp6Rg5ciTMzc3h7OyMV199FbW1ta35KI3yzTffIDAwULvwWb9+/bBlyxbteUN61n/76KOPIAgCZs+erT1maM/7n//8B4IgNHj5+/trzxva8wLApUuXMGnSJDg4OMDMzAwBAQE4dOiQ9rwh/czy9va+4fsrCAJmzJgBwDC/vzoRqVWsW7dOVCqV4rJly8Tjx4+LU6dOFW1tbcXs7GypS2uyv/76S5w3b564ceNGEYD466+/Njj/0UcfiTY2NuJvv/0mHjlyRBw1apTo4+MjVlRUaK8ZNmyYGBQUJB44cEDcs2eP2LlzZ3HChAmt/CSNEx4eLi5fvlw8duyYmJSUJI4YMULs2LGjWFpaqr3m+eefFz09PcWYmBjx0KFD4t133y3ec8892vO1tbViz549xbCwMPHw4cPiX3/9JTo6Oopz586V4pFua/PmzeKff/4pnjlzRjx9+rT45ptviiYmJuKxY8dEUTSsZ71efHy86O3tLQYGBoqzZs3SHje0512wYIHYo0cPMTMzU/vKzc3Vnje05y0oKBC9vLzEZ555RoyLixPPnz8vbt26VUxJSdFeY0g/s3Jychp8b7dv3y4CEGNjY0VRNLzvr64YflpJSEiIOGPGDO3XarVadHd3F6OioiSsqvn+HX40Go3o6uoqfvLJJ9pjhYWFokqlEteuXSuKoiieOHFCBCAePHhQe82WLVtEQRDES5cutVrtusrJyREBiLt27RJFse75TExMxJ9//ll7zcmTJ0UA4v79+0VRrAuMMplMzMrK0l7zzTffiNbW1mJVVVXrPoAO7OzsxO+++85gn7WkpETs0qWLuH37dnHQoEHa8GOIz7tgwQIxKCjopucM8Xlff/11sX///rc8b+g/s2bNmiV26tRJ1Gg0Bvn91RW7vVpBdXU1EhISEBYWpj0mk8kQFhaG/fv3S1iZ/qWmpiIrK6vBs9rY2CA0NFT7rPv374etrS369u2rvSYsLAwymQxxcXGtXnNTFRUVAQDs7e0BAAkJCaipqWnwzP7+/ujYsWODZw4ICICLi4v2mvDwcBQXF+P48eOtWH3TqNVqrFu3DmVlZejXr5/BPuuMGTMwcuTIBs8FGO739uzZs3B3d4evry8mTpyI9PR0AIb5vJs3b0bfvn0xduxYODs7o3fv3li6dKn2vCH/zKqursbq1asxefJkCIJgkN9fXTH8tIK8vDyo1eoG/5gAwMXFBVlZWRJV1TLqn+d2z5qVlQVnZ+cG5xUKBezt7dv834dGo8Hs2bNx7733omfPngDqnkepVMLW1rbBtf9+5pv9ndSfa2uOHj0KS0tLqFQqPP/88/j111/RvXt3g3zWdevWITExEVFRUTecM8TnDQ0NxQ8//IDo6Gh88803SE1NxYABA1BSUmKQz3v+/Hl888036NKlC7Zu3Yrp06fjpZdewooVKwAY9s+s3377DYWFhXjmmWcAGOa/Z11xV3eiJpgxYwaOHTuGvXv3Sl1Ki+ratSuSkpJQVFSEX375BU8//TR27doldVl6l5GRgVmzZmH79u0wNTWVupxWMXz4cO37wMBAhIaGwsvLCz/99BPMzMwkrKxlaDQa9O3bFx9++CEAoHfv3jh27BgWL16Mp59+WuLqWtb333+P4cOHw93dXepS2hy2/LQCR0dHyOXyG0bUZ2dnw9XVVaKqWkb989zuWV1dXZGTk9PgfG1tLQoKCtr038fMmTPxxx9/IDY2Fh4eHtrjrq6uqK6uRmFhYYPr//3MN/s7qT/X1iiVSnTu3BnBwcGIiopCUFAQPv/8c4N71oSEBOTk5KBPnz5QKBRQKBTYtWsXvvjiCygUCri4uBjU896Mra0t/Pz8kJKSYnDfXwBwc3ND9+7dGxzr1q2btqvPUH9mpaWl4e+//8azzz6rPWaI319dMfy0AqVSieDgYMTExGiPaTQaxMTEoF+/fhJWpn8+Pj5wdXVt8KzFxcWIi4vTPmu/fv1QWFiIhIQE7TU7duyARqNBaGhoq9d8J6IoYubMmfj111+xY8cO+Pj4NDgfHBwMExOTBs98+vRppKenN3jmo0ePNvgBun37dlhbW9/wg7kt0mg0qKqqMrhnHTJkCI4ePYqkpCTtq2/fvpg4caL2vSE9782Ulpbi3LlzcHNzM7jvLwDce++9NyxNcebMGXh5eQEwzJ9ZALB8+XI4Oztj5MiR2mOG+P3VmdQjro3FunXrRJVKJf7www/iiRMnxGnTpom2trYNRtS3FyUlJeLhw4fFw4cPiwDERYsWiYcPHxbT0tJEUaybNmpraytu2rRJTE5OFkePHn3TaaO9e/cW4+LixL1794pdunRpk9NGRVEUp0+fLtrY2Ig7d+5sMIW0vLxce83zzz8vduzYUdyxY4d46NAhsV+/fmK/fv205+unjw4dOlRMSkoSo6OjRScnpzY5ffSNN94Qd+3aJaamporJycniG2+8IQqCIG7btk0URcN61pu5fraXKBre886ZM0fcuXOnmJqaKu7bt08MCwsTHR0dxZycHFEUDe954+PjRYVCIX7wwQfi2bNnxTVr1ojm5ubi6tWrtdcY2s8stVotduzYUXz99ddvOGdo319dMfy0oi+//FLs2LGjqFQqxZCQEPHAgQNSl6ST2NhYEcANr6effloUxbqpo2+//bbo4uIiqlQqcciQIeLp06cb3CM/P1+cMGGCaGlpKVpbW4sRERFiSUmJBE9zZzd7VgDi8uXLtddUVFSIL7zwgmhnZyeam5uLDz/8sJiZmdngPhcuXBCHDx8umpmZiY6OjuKcOXPEmpqaVn6aO5s8ebLo5eUlKpVK0cnJSRwyZIg2+IiiYT3rzfw7/Bja844bN050c3MTlUql2KFDB3HcuHEN1rwxtOcVRVH8/fffxZ49e4oqlUr09/cXv/322wbnDe1n1tatW0UANzyDKBrm91cXgiiKoiRNTkREREQS4JgfIiIiMioMP0RERGRUGH6IiIjIqDD8EBERkVFh+CEiIiKjwvBDRERERoXhh4iIiIwKww8REREZFYYfIjI4zzzzDMaMGSN1GUTURimkLoCIqCkEQbjt+QULFuDzzz8HF68nolth+CGidiUzM1P7fv369Zg/f36DXbstLS1haWkpRWlE1E6w24uI2hVXV1fty8bGBoIgNDhmaWl5Q7fX4MGD8eKLL2L27Nmws7ODi4sLli5dirKyMkRERMDKygqdO3fGli1bGvxZx44dw/Dhw2FpaQkXFxc8+eSTyMvLa+UnJiJ9Y/ghIqOwYsUKODo6Ij4+Hi+++CKmT5+OsWPH4p577kFiYiKGDh2KJ598EuXl5QCAwsJC3H///ejduzcOHTqE6OhoZGdn4/HHH5f4SYiouRh+iMgoBAUF4a233kKXLl0wd+5cmJqawtHREVOnTkWXLl0wf/585OfnIzk5GQDwv//9D71798aHH34If39/9O7dG8uWLUNsbCzOnDkj8dMQUXNwzA8RGYXAwEDte7lcDgcHBwQEBGiPubi4AABycnIAAEeOHEFsbOxNxw+dO3cOfn5+LVwxEbUUhh8iMgomJiYNvhYEocGx+llkGo0GAFBaWoqHHnoIH3/88Q33cnNza8FKiailMfwQEd1Enz59sGHDBnh7e0Oh4I9KIkPCMT9ERDcxY8YMFBQUYMKECTh48CDOnTuHrVu3IiIiAmq1WuryiKgZGH6IiG7C3d0d+/btg1qtxtChQxEQEIDZs2fD1tYWMhl/dBK1Z4LIZVCJiIjIiPA/X4iIiMioMPwQERGRUWH4ISIiIqPC8ENERERGheGHiIiIjArDDxERERkVhh8iIiIyKgw/REREZFQYfoiIiMioMPwQERGRUWH4ISIiIqPy/8cQw1HftB93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot('cgap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55481554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Extracellular Glucose'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot(['cglcex','cpyr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f2d13e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Concentrations'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf1773f",
   "metadata": {},
   "source": [
    "You may retrieve the time point concentrations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1b147656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.        , 1.39495993, 0.90403506, 0.52864979, 0.27730819,\n",
       "       0.14145998, 0.08569682, 0.0670109 , 0.06083137, 0.05845301,\n",
       "       0.05733298, 0.05671848, 0.05635113, 0.05612171, 0.05597545,\n",
       "       0.055881  ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.get_y('cglcex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca91fca",
   "metadata": {},
   "source": [
    "Instead of a list you may want to build a dataframe, that you may save to an excel or cvs file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3240039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>1.394960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>0.904035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>0.528650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.0</th>\n",
       "      <td>0.277308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250.0</th>\n",
       "      <td>0.141460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.0</th>\n",
       "      <td>0.085697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350.0</th>\n",
       "      <td>0.067011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400.0</th>\n",
       "      <td>0.060831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450.0</th>\n",
       "      <td>0.058453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500.0</th>\n",
       "      <td>0.057333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550.0</th>\n",
       "      <td>0.056718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600.0</th>\n",
       "      <td>0.056351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650.0</th>\n",
       "      <td>0.056122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700.0</th>\n",
       "      <td>0.055975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750.0</th>\n",
       "      <td>0.055881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Concentration\n",
       "0.0         2.000000\n",
       "50.0        1.394960\n",
       "100.0       0.904035\n",
       "150.0       0.528650\n",
       "200.0       0.277308\n",
       "250.0       0.141460\n",
       "300.0       0.085697\n",
       "350.0       0.067011\n",
       "400.0       0.060831\n",
       "450.0       0.058453\n",
       "500.0       0.057333\n",
       "550.0       0.056718\n",
       "600.0       0.056351\n",
       "650.0       0.056122\n",
       "700.0       0.055975\n",
       "750.0       0.055881"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(res.get_y('cglcex'),columns=['Concentration'],index=res.t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbf06b2",
   "metadata": {},
   "source": [
    "### Modifiying initial concentrations and parameters\n",
    "\n",
    "When running a simulation you may also define:\n",
    "- *initcon*, a dictionary of initial concentrations (partial or for all metabolites);\n",
    "- *parameters*, a dictionary of parameters to be used in the simulation;\n",
    "- *factors*, a dictionary of factores to be applied to parameters. \n",
    "\n",
    "Some examples are next provided:\n",
    "\n",
    "- Change initial concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4fd48f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>compartment</th>\n",
       "      <th>formula</th>\n",
       "      <th>charge</th>\n",
       "      <th>y0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cglcex</th>\n",
       "      <td>Extracellular Glucose</td>\n",
       "      <td>extracellular</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name    compartment formula charge   y0\n",
       "id                                                              \n",
       "cglcex  Extracellular Glucose  extracellular                 2.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find_metabolites('cglcex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e25a3d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Concentrations'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initcon = {'cglcex':3.0}\n",
    "res = sim.simulate(initcon=initcon)\n",
    "res.plot(size=(9,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e7e3b8",
   "metadata": {},
   "source": [
    "Modify a parameter setting a new **temporary** value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5b021bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parameter</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rmaxPK</th>\n",
       "      <td>0.061132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Value\n",
       "Parameter          \n",
       "rmaxPK     0.061132"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.find_parameters('rmaxPK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "166cd18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Concentrations'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = {'rmaxPK':0.61132}\n",
    "res = sim.simulate(parameters=parameters)\n",
    "res.plot(size=(9,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004ac4e6",
   "metadata": {},
   "source": [
    "You may also apply a factor to a parameter:\n",
    "\n",
    "_(Note that if you define a new value to a parameter and apply a factor to it, both modifications will be applied.)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cfb02ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Concentrations'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors = {'rmaxPK':10} # meaning 10 folds \n",
    "res = sim.simulate(factors=factors)\n",
    "res.plot(size=(9,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e6a8b3",
   "metadata": {},
   "source": [
    "## Strain Design Optimization with SBML kinetic models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "43df4917",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mewpy.problems import KineticOUProblem\n",
    "from mewpy.optimization.evaluation import TargetFlux, ModificationType\n",
    "from mewpy.optimization import EA\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b18325",
   "metadata": {},
   "source": [
    "The rational behind the optimization is that Vmax parameters are a proxy for an enzyme concentrations times its turnover. When substrates are in \"sufficient\" concentration, changing Vmaxs values will translante into changing enzymes availability. By default the Vmax values are considered as modifications target, you might change this setting and provide a chosen list of targets.\n",
    "\n",
    "We start by defining the optimization objective(s). As a rule of thumb, consider no more than 3 or 4 objective. If more objectives are required, you may consider an aggregated sum of some (see MEWpy documentation). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "53804874",
   "metadata": {},
   "outputs": [],
   "source": [
    "objectives = [TargetFlux('vsersynth')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca2b999",
   "metadata": {},
   "source": [
    "We next define the allows fold levels to be applied to the targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "782a31c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "levels = [round(x,1) for x in np.arange(0, 3, 0.2)]\n",
    "levels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05eabd53",
   "metadata": {},
   "source": [
    "We can now define the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "44d12ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = KineticOUProblem(model, objectives, t_points=[0, 100], timeout=0, levels=levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbe8b34",
   "metadata": {},
   "source": [
    "and instantiate an optimization engine to run for 20 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dd110dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ea = EA(problem,max_generations=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebb94ac",
   "metadata": {},
   "source": [
    "To run the optimization, just call the run method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6cae0d2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running GA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 3.09224, mxstep steps taken before reaching tout.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 4.63919, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 5.3245, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 4.79242, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 4.64334, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 100\tFitness: 0.012582365820303509\n",
      "Evaluations: 200\tFitness: 0.0328191185597368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 5.03085, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 300\tFitness: 0.03811978053331872\n",
      "Evaluations: 400\tFitness: 0.04856276220682675\n",
      "Evaluations: 500\tFitness: 0.04856276220682675\n",
      "Evaluations: 600\tFitness: 0.050869305341854756\n",
      "Evaluations: 700\tFitness: 0.055359880124647994\n",
      "Evaluations: 800\tFitness: 0.055359880124647994\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 8.57982, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 900\tFitness: 0.055359880124647994\n",
      "Evaluations: 1000\tFitness: 0.0554429740004523\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 4.22832, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 1100\tFitness: 0.05811270945492204\n",
      "Evaluations: 1200\tFitness: 0.0588543052807965\n",
      "Evaluations: 1300\tFitness: 0.05890538092953184\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 7.3276, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 9.35153, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n",
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 7.11314, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 1400\tFitness: 0.059017827406469124\n",
      "Evaluations: 1500\tFitness: 0.059098802786308226\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[CVODE ERROR]  CVode\n",
      "  At t = 8.05244, the right-hand side routine failed in an unrecoverable manner.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluations: 1600\tFitness: 0.059460934584215946\n",
      "Evaluations: 1700\tFitness: 0.059460934584215946\n",
      "Evaluations: 1800\tFitness: 0.0596183047636292\n",
      "Evaluations: 1900\tFitness: 0.05966963832957868\n",
      "Evaluations: 2000\tFitness: 0.06167213660100724\n"
     ]
    }
   ],
   "source": [
    "population = ea.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1255a0c1",
   "metadata": {},
   "source": [
    "When the optimization finishes, you may want to convert the optimization results to a dataframe, and save them if it is the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eac85815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modification</th>\n",
       "      <th>Size</th>\n",
       "      <th>TargetFlux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.061672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxTIS': 1.4, 'rmax...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxR5PI': 2.2, 'rma...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.059464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxR5PI': 2.0, 'rma...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPGI': 2.8, 'rmax...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxTIS': 2.0, 'rmax...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.059370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxPGI': 2.8, 'rmax...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxTKb': 1.4, 'rmax...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.059196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxSerSynth': 2.8, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>{'rmaxPTS_renamed': 0.2, 'rmaxRu5P': 1.6, 'rma...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.059105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Modification  Size  TargetFlux\n",
       "0   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     6    0.061672\n",
       "1   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...     6    0.059670\n",
       "2   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...     6    0.059637\n",
       "3   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...     7    0.059631\n",
       "4   {'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...     6    0.059618\n",
       "5   {'rmaxPTS_renamed': 0.2, 'rmaxTIS': 1.4, 'rmax...     6    0.059607\n",
       "6   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxG...     7    0.059530\n",
       "7   {'rmaxPTS_renamed': 0.2, 'rmaxR5PI': 2.2, 'rma...     5    0.059507\n",
       "8   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     6    0.059473\n",
       "9   {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     6    0.059468\n",
       "10  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     4    0.059464\n",
       "11  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     5    0.059461\n",
       "12  {'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...     6    0.059457\n",
       "13  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     6    0.059450\n",
       "14  {'rmaxPTS_renamed': 0.2, 'rmaxR5PI': 2.0, 'rma...     7    0.059433\n",
       "15  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     5    0.059433\n",
       "16  {'rmaxPTS_renamed': 0.2, 'rmaxPGI': 2.8, 'rmax...     6    0.059431\n",
       "17  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     7    0.059430\n",
       "18  {'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...     7    0.059410\n",
       "19  {'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxS...     7    0.059410\n",
       "20  {'rmaxPTS_renamed': 0.2, 'rmaxTIS': 2.0, 'rmax...     8    0.059370\n",
       "21  {'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...     6    0.059260\n",
       "22  {'rmaxPTS_renamed': 0.2, 'rmaxPGI': 2.8, 'rmax...     7    0.059256\n",
       "23  {'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...     6    0.059254\n",
       "24  {'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...     5    0.059240\n",
       "25  {'rmaxPTS_renamed': 0.2, 'rmaxTKb': 1.4, 'rmax...     7    0.059207\n",
       "26  {'rmaxPTS_renamed': 0.2, 'rmaxMetSynth': 1.8, ...     7    0.059196\n",
       "27  {'rmaxPTS_renamed': 0.2, 'rmaxG6PDH': 0.8, 'rm...     6    0.059193\n",
       "28  {'rmaxPTS_renamed': 0.2, 'rmaxSerSynth': 2.8, ...     5    0.059130\n",
       "29  {'rmaxPTS_renamed': 0.2, 'rmaxRu5P': 1.6, 'rma...     6    0.059105"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ea.dataframe()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33908985",
   "metadata": {},
   "source": [
    "You can additionaly select a solution and analyse it further, for example, by simulating it and ploting the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fb1a47e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmaxPTS_renamed': 0.2, 'rmaxPK': 0.2, 'rmaxSerSynth': 2.8, 'rmaxGAPDH': 2.8, 'rmaxR5PI': 1.4, 'rmaxpepCxylase': 0.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Concentrations'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution = df['Modification'][0]\n",
    "print(solution)\n",
    "sim.simulate(factors=solution).plot()"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "4d63cecbd4ee7a4964b1e14fac35e632f419fb342d4e4353e8cedd6749d678e0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}