All Projects → SysBioChalmers → yeast-GEM

SysBioChalmers / yeast-GEM

Licence: CC-BY-4.0 license
The consensus GEM for Saccharomyces cerevisiae

Programming Languages

matlab
3953 projects
Jupyter Notebook
11667 projects
python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to yeast-GEM

CNApy
An integrated visual environment for metabolic modeling with common methods such as FBA, FVA and Elementary Flux Modes, and advanced features such as thermodynamic methods, extended Minimal Cut Sets, OptKnock, RobustKnock, OptCouple and more!
Stars: ✭ 27 (-62.5%)
Mutual labels:  systems-biology, metabolic-models
Human-GEM
The generic genome-scale metabolic model of Homo sapiens
Stars: ✭ 48 (-33.33%)
Mutual labels:  genome-scale-models, standard-gem
newt
A web application to visualize and edit pathway models
Stars: ✭ 46 (-36.11%)
Mutual labels:  biology, systems-biology
BOFdat
Generate biomass objective function stoichiometric coefficients for genome-scale models from experimental data
Stars: ✭ 23 (-68.06%)
Mutual labels:  metabolic-models, genome-scale-models
cobrame
A COBRApy extension for genome-scale models of metabolism and expression (ME-models)
Stars: ✭ 30 (-58.33%)
Mutual labels:  systems-biology, metabolic-models
chise.js
A web application to visualize and edit the pathway models represented by SBGN Process Description Notation
Stars: ✭ 15 (-79.17%)
Mutual labels:  biology, systems-biology
pioreactor
Hardware and software for accessible, extensible, and scalable bioreactors. Built on Raspberry Pi.
Stars: ✭ 28 (-61.11%)
Mutual labels:  biology, yeast
Semester Biology
Stars: ✭ 52 (-27.78%)
Mutual labels:  biology
Escher
Build, share, and embed visualizations of metabolic pathways.
Stars: ✭ 141 (+95.83%)
Mutual labels:  biology
Awesome Biological Visualizations
A list of web-based interactive biological data visualizations.
Stars: ✭ 40 (-44.44%)
Mutual labels:  biology
Thrive
The main repository for the development of the evolution game Thrive.
Stars: ✭ 874 (+1113.89%)
Mutual labels:  biology
Python biologist
Python Programming for Biologists
Stars: ✭ 55 (-23.61%)
Mutual labels:  biology
Dspp Keras
Protein order and disorder data for Keras, Tensor Flow and Edward frameworks with automated update cycle made for continuous learning applications.
Stars: ✭ 160 (+122.22%)
Mutual labels:  biology
Yesterday I Learned
Brainfarts are caused by the rupturing of the cerebral sphincter.
Stars: ✭ 50 (-30.56%)
Mutual labels:  biology
Clustergrammer
An interactive heatmap visualization built using D3.js
Stars: ✭ 188 (+161.11%)
Mutual labels:  biology
Metasra Pipeline
MetaSRA: normalized sample-specific metadata for the Sequence Read Archive
Stars: ✭ 33 (-54.17%)
Mutual labels:  biology
Opentrons
Software for writing protocols and running them on the Opentrons OT-2
Stars: ✭ 203 (+181.94%)
Mutual labels:  biology
Deep Rules
Ten Quick Tips for Deep Learning in Biology
Stars: ✭ 179 (+148.61%)
Mutual labels:  biology
Indra
INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system interfacing with NLP systems and databases to collect knowledge, and through a process of assembly, produce causal graphs and dynamical models.
Stars: ✭ 105 (+45.83%)
Mutual labels:  biology
Riddle
Race and ethnicity Imputation from Disease history with Deep LEarning
Stars: ✭ 91 (+26.39%)
Mutual labels:  biology

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

DOI GitHub version Join the chat at https://gitter.im/SysBioChalmers/yeast-GEMMemote history

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 yeast8 paper:

    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 05-Sep-2022 8.6.2 4063 2744 1160

Gene essentiality prediction

  • Accuracy: 0.881
  • True non-essential genes: 926
  • True essential genes: 63
  • False non-essential genes: 96
  • False essential genes: 38

Growth prediction

  • Correlation coefficient R2: 0.865

Growth curve

Installation & usage

Obtain model

You can obtained 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 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:

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:

  • 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  # installs all dependencies
    touch .env                                    # creates 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:
    import code.io as io
    model = io.read_yeast_model() # loading
    io.write_yeast_model(model)   # saving

Online visualization/simulation

  • You can visualize selected pathways of yeast-GEM and perform online constraint-based simulations using Caffeine, by creating a simulation with the latest yeast-GEM version available, and choosing any S. cerevisiae map (currently only iMM904 maps are available). Learn more about Caffeine.
  • Additionally, 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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].