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.

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, every yeast-GEM release is archived in Zenodo. To ensure reproducibility, always cite both the original publication and the specific version you used, 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	28-Aug-2026	9.1.1	4105	2748	1143

Validated against experimental data on every pull request; see data/testResults/README.md for the full methodology and additional metrics, including anaerobic flux and fermentation-product predictions.

Gene essentiality prediction

Predicted gene essentiality vs. the Stanford yeast deletion collection (1107 genes):

• Accuracy: 0.902
• True non-essential genes: 933
• True essential genes: 65
• False non-essential genes: 94
• False essential genes: 15

Growth prediction

Predicted vs. measured growth rate across chemostat conditions from Österlund et al. (2013):

• Correlation coefficient R²: 0.902

Installation & usage

Obtain model

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

1. If you have a Git client installed on your computer, you can clone the main branch of the yeast-GEM repository.
2. You can directly download the latest release as a ZIP file.
3. If you want to contribute to the development of yeast-GEM (see Contributing), 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) — except for loading model/yeast-GEM.yml itself, which loadYeastModel does by default; see the developer note below
  • 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, except for reading or writing model/yeast-GEM.yml (what loadYeastModel/saveYeastModel do by default): that needs RAVEN's develop3 branch, until its changes reach a numbered release

• Python-based
  The yeastgem package (code/python/) is the actively maintained Python interface to the model: a cobrapy-based API built on raven-toolbox for the generic, organism-agnostic parts. It covers loading and committing the model, applying growth conditions, biomass composition, and the validation test suite. See code/python/README.md for the API map.

  pip install -e code/python/
  touch .env # create a .env file for locating the root

  The standalone functions in code/ (code/io.py and its neighbours) predate yeastgem and are being phased out; prefer yeastgem above for anything new. If you still need them:

  pip install -r code/requirements/requirements.txt  # install all dependencies
  touch .env # create a .env file for locating the root

MEMOTE scores are reported automatically — a fast score on every pull request, and the full suite at release, published here. To run MEMOTE locally instead, see memote_snapshot.py or run memote directly.

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, with the yeastgem package:
  Before opening Python, the following command only needs to be run once, 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:

  from yeastgem import read_yeast_model, commit_yeast_model
  model = read_yeast_model()      # loading
  commit_yeast_model(model)       # saving

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.