yeast-GEM: The consensus genome-scale metabolic model of Saccharomyces cerevisiae

Description

This repository contains the current consensus genome-scale metabolic model of Saccharomyces cerevisiae. It is the continuation of the legacy project yeastnet. For the latest release please click here.

Citation

If you use yeast-GEM please cite the yeast9 paper:

Zhang, C. et al. Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community. Molecular Systems Biology (2024) doi:10.1038/s44320-024-00060-7
For pre-yeast9 versions:

Lu, H. et al. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 10, 3586 (2019). doi:10.1038/s41467-019-11581-3
Additionally, all yeast-GEM releases are archived in Zenodo, for you to cite the specific version of yeast-GEM that you used in your study, to ensure reproducibility. You should always cite the original publication + the specific version, for instance:

The yeast consensus genome-scale model [Lu et al. 2019], version 8.3.4 [Sánchez et al. 2019], was used.

Find the citation details for your specific version here.

Keywords

Utilisation: experimental data reconstruction; multi-omics integrative analysis; in silico strain design; model template
Field: metabolic-network reconstruction
Type of model: reconstruction; curated
Model source: YeastMetabolicNetwork
Omic source: genomics; metabolomics
Taxonomic name: Saccharomyces cerevisiae
Taxonomy ID: taxonomy:559292
Genome ID: insdc.gca:GCA_000146045.2
Metabolic system: general metabolism
Strain: S288C
Condition: aerobic, glucose-limited, defined media

Model overview

Taxonomy	Latest update	Version	Reactions	Metabolites	Genes
Saccharomyces cerevisiae	14-Aug-2024	9.0.1	4130	2805	1162

Gene essentiality prediction

Accuracy: 0.899
True non-essential genes: 953
True essential genes: 58
False non-essential genes: 100
False essential genes: 13

Growth prediction

Correlation coefficient R²: 0.880

Installation & usage

Obtain model

You can obtained the model by any of the following methods:

If you have a Git client installed on your computer, you can clone the main branch of the yeast-GEM repository.
You can directly download the latest release as a ZIP file.
If you want to contribute to the development of yeast-GEM (see below), it is best to fork the yeast-GEM repository to your own Github account.

Required software

Basic user

If you want to use the model for your own model simulations, you can use any software that accepts SBML L3V1 FBCv3 formatted model files. This includes any of the following:

MATLAB-based
- RAVEN Toolbox version 2.8.3 or later (recommended)
- COBRA Toolbox
Python-based
- cobrapy

Please see the installation instructions for each software package.

Developer

MATLAB-based
If you want to contribute to the development of yeast-GEM, or otherwise want to run any of the provided MATLAB functions, then the following software is required:
- RAVEN Toolbox version 2.8.3 or later
Python-based
Contribution via python (cobrapy) is not yet functional. In essence, if you can retain the same format of the model files, you can still contribute to the development of yeast-GEM. However, you cannot use the MATLAB functions.

If you want to use any of the provided Python functions, you may create an environment with all requirements:
```
pip install -r code/requirements/requirements.txt  # install all dependencies
touch .env # create a .env file for locating the root
```

If you want to locally run memote run or memote report history, you should also install git lfs, as results.db (the database that stores all memote results) is tracked with git lfs.

Model usage

Make sure to load/save the model with the corresponding wrapper functions:

In Matlab:
```
cd ./code
model = loadYeastModel(); % loading
saveYeastModel(model);    % saving
```
- If RAVEN is not installed, you can also use COBRA-native functions (readCbModel, writeCbModel), but these model-files cannot be committed back to the GitHub repository.

In Python:
Before opening Python, the following command should (once) be run in the yeast-GEM root folder:

touch .env # create a .env file for locating the root

Afterwards, the model can be loaded in Python with:

import code.io as io
model = io.read_yeast_model() # loading
io.write_yeast_model(model)   # saving

Online visualization

You can interactively navigate model components and visualize 3D representations of all compartments and subsystems of yeast-GEM at Metabolic Atlas. Learn more about Metabolic Atlas.

Contributing

Contributions are always welcome! Please read the contributions guideline to get started.

Contributors

Code contributors are reported automatically by GitHub under Contributors, while other contributions come in as Issues.

SysBioChalmers/yeast-GEM