GEM Modeling with COBRApy

How to Simulate Digital Twins of Cellular Metabolism from GEM Models using COBRApy in Constellab?

This agent demonstrates how to use COBRApy (Constraint-Based Reconstruction and Analysis in Python) within Constellab to model and analyze genome-scale metabolic models (GEMs). By leveraging constraint-based modeling, you can create digital twins of cellular metabolism to predict growth rates, metabolic fluxes, and cellular behavior under different environmental conditions.

We start from an existing genome-scale metabolic models. We use the well known e_coli_core model of Escherichia coli str. K-12 substr. MG1655. This model was obtained from the BiGG database and published by Orth et al, EcoSal Plus 2010.  We will follow the steps outlined in the article to reproduce the results. 

What is COBRApy?

COBRApy is a Python package for constraint-based modeling of metabolic networks. It enables:

• Analysis of genome-scale metabolic models
• Flux balance analysis (FBA)
• Simulation of metabolic phenotypes

• Optimization of cellular objectives (growth, ATP production, etc.)

How to use it within Constellab? 

Constellab makes it easy to use COBRApy to construct and analyse metabolic models. In this tutorial, we will show you how to use agents to manipulate the COBRApy package.

In your Constellab agent environment, you need to add: 

channels:

- conda-forge

dependencies:

- cobra==0.29.1

Supported Versions

You can only choose a COBRApy version supported by Anaconda. Check the latest version here.

Available Functions

You can use all the functions available in the COBRApy documentation. 

Tutorial: E. coli Metabolic Model Analysis

This example demonstrates how to:

• Load a genome-scale metabolic model
• Simulate different growth conditions (aerobic vs anaerobic)
• Optimize for different cellular objectives
• Generate comprehensive analysis reports

Start exploring metabolic models! 

If you would like more information about this tutorial, read this story.