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This advanced workshop is an introduction to the stochastic simulation of Gene Regulatory Networks (GRNs) using the R package sismonr. You will learn the basics of simulating GRNs, and how to scale up simulations on a HPC.
- Familiarity with bash and R
- Basic molecular biology knowledge preferred (gene expression and regulation)
- HPC knowledge preferred
By the end of this workshop, participants should be able to:
- explain the concept of modelling and simulations, and how simulations can help answer research questions;
- briefly describe the main steps of gene expression, and explain what is a Gene Regulatory Network;
- list several classes of GRN models;
- generate a small random GRN with the sismonr package and simulate the expression of its gene;
- submit and manage jobs on a cluster using a scheduler and use software through environment modules;
- automate a large number of tasks on a HPC using array jobs.
- how to construct a mathematical or statistical model for a specific biological system of interest;
- How to estimate model parameters based on experimental data;
- How to reconstruct a GRN from experimental data;
- Any questions we don't have an answer for :)
Before getting started, have a look at the Supplementary data for instructions on how to connect to NeSI Mahuika Jupyter.
- Introduction
- Getting started with sismonr
- Scaling up your work
- Working with job scheduler
- Parallel job arrays
- Post-processing
- Supplementary-material
- Supplementary-material-2
| Day | Time | Topic |
|---|---|---|
| 1st day: | 10am-12:30pm | 1. Introduction |
| 12:30pm-1:30pm | Lunch break | |
| 1:30pm-2:30pm | 2. Getting started with sismonr | |
| 2:30pm-4pm | 3. Scaling up your work 4. Working with job scheduler |
|
| 2nd day: | 10am-12pm | 5. Automating large number of tasks |
| 12pm-1pm | Lunch break | |
| 1pm-3pm | 5. Post-processing | |
| 3pm-4pm | Question time |