https://github.com/sysbiochalmers/enzymeconstrained_humanmodels

Collection of scripts for enhancing humanGEM based models with kinetic and proteomics constraints and specialized simulation utilities.
https://github.com/sysbiochalmers/enzymeconstrained_humanmodels

enzyme-constraints genome-scale-models kinetics proteomics-data-integration systems-biology

Last synced: 19 days ago
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Collection of scripts for enhancing humanGEM based models with kinetic and proteomics constraints and specialized simulation utilities.

• Host: GitHub
• URL: https://github.com/sysbiochalmers/enzymeconstrained_humanmodels
• Owner: SysBioChalmers
• Created: 2017-09-18T13:40:31.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-02-16T15:23:47.000Z (almost 5 years ago)
• Last Synced: 2023-07-05T14:55:03.897Z (over 1 year ago)
• Topics: enzyme-constraints, genome-scale-models, kinetics, proteomics-data-integration, systems-biology
• Language: MATLAB
• Homepage:
• Size: 464 MB
• Stars: 1
• Watchers: 6
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Enzyme-constrained Human1 GEMs

This directory contains an automated pipeline for constructing cell-specific enzyme-constrained GEMs (ecGEMs) derived from Human-GEM (v1.3.0) based on transcriptomics and proteomics datasets.

### Required Software:

* A functional Matlab installation (MATLAB 7.3 and higher)

* The [RAVEN toolbox for MATLAB](https://github.com/SysBioChalmers/RAVEN) (version 2.3.0)

* For generating some figures, a functional [R installation](https://www.r-project.org/) (version 3.6.1)

### Dependencies - Recommended Software:

* The libSBML MATLAB API (version [5.13.0](https://sourceforge.net/projects/sbml/files/libsbml/5.13.0/stable/MATLAB%20interface/) is recommended)

* [Gurobi Optimizer](http://www.gurobi.com/registration/download-reg) for any simulation

## Regenerating the ecGEMs:

The ecGEMs are already present in the `models/` subdirectory, but the scripts and data necessary to regenerate the models are available here. The master script for generating the ecGEMs from the tINIT GEMs (provided in the `/models/humanGEM_cellLines/11models.mat` file) is `generate_human_ecModels_NCI60.m`, located in the `/ComplementaryScripts` directory. Run this `generate_human_ecModels_NCI60` script in MATLAB to regenerate the 11 ecGEMs.

## Flux variability analysis

Flux variability analysis (FVA) (corresponding to the results presented in Fig. 5B) can be run using the `comparativeFVA_humanModels.m` function in the `ComplementaryScripts/Simulation` subdirectory. Specify the name of the model (cell line) for which FVA is to be run; for example:

`results = comparativeFVA_humanModels('HOP62');`

## Prediction of growth and metabolite exchange rates:

To use the ecGEMs and non-ecGEMs to predict growth rates and metabolite exchange rates with increasing levels of constraints (as shown in Figs. 5C and 5D), run the `predict_cellLines_gRates.m` script in the `ComplementaryScripts` subdirectory.

## Generating plots for Figure 5

The R script used to generate plots shown in Fig. 5 of the main text is `plot_ecGEM_results.R`, located in the `ComplementaryScripts` subdirectory.