Genome-Scale Metabolic Modeling of a Human Astrocyte

This repository contains the data, input models, and code to construct and analyze the genome-scale metabolic model (GEM) of a human astrocyte under three conditions: healthy, inflamed with palmitate, and treated with tibolone.

The final models are available in the gibbslab/GSMs repository, which also includes a collection of other GEMs.

Cloning and Running the Repository

To clone this repository and ensure all submodules are downloaded correctly (including the COBRA Toolbox), run the following commands:

git clone --recurse-submodules https://github.com/nmendozam/masterThesis.git
cd masterThesis
git clone https://github.com/gibbslab/GSMs.git

Description of Scripts

The repository includes the following scripts:

1. DataPrep.m
   Prepares the omic data used for contextualizing the GEM model.

2. BuildAstrocyteModel.m
   Builds the baseline GEM of the human astrocyte.

3. GapFilling.m
   Fills the gaps in the astrocyte model to ensure metabolic network connectivity.

4. leakSiphonModes.m
   Semi-automated script to identify and fix leak and siphon reactions in the model.

5. FBAanalysis.m
   Performs Flux Balance Analysis (FBA) for the different astrocyte conditions.
   Note: This script requires cloning the gibbslab/GSMs repository.

Models

The four astrocyte models generated in this project are listed in this pull request and can be downloaded by cloning the gibbslab/GSMs repository.

1. Astrocyte_Mendoza2022.xml.gz
   A reduced model containing only astrocyte reactions, with all exchange reactions open. This serves as the base model for the three contextualized models.

2. Astrocyte_Healthy_Mendoza2022.xml.gz
   The model contextualized for a healthy astrocyte.

3. Astrocyte_InflamedPalmitate_Mendoza2022.xml.gz
   The model contextualized for an astrocyte inflamed with palmitate.

4. Astrocyte_TreatedTibolone_Mendoza2022.xml.gz
   The model contextualized for an astrocyte inflamed with palmitate and treated with tibolone.

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Citation

If you use this repository or any of the associated models, please cite the following manuscript (currently under review):

Angarita-Rodríguez, A., Mendoza-Mejía, N., González, J., Papin, J. A., & Pinzón, A. (2024). Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data. Manuscript under review, Frontiers.