About GECKO
The GECKO toolbox is a Matlab/Python package for enhancing a Genome-scale model to account for Enzyme Constraints, using Kinetics and Omics. It is the companion software to this publication, and it has two main parts:
-
geckomat
: Matlab+Python scripts to fetch online data and build/simulate enzyme-constrained models. -
geckopy
: a Python package which can be used with cobrapy to obtain a ecYeastGEM model object, optionally adjusted for provided proteomics data.
Last update: 2021-02-17
This repository is administered by Benjamin J. Sanchez (@BenjaSanchez), Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology.
geckomat: Building enzyme-constrained models
Required software - Python module
-
SOAPpy:
easy_install-2.7 SOAPpy
Required software - Matlab module
- MATLAB 9.1 (R2016b) or higher + Optimization Toolbox.
- The COBRA toolbox for MATLAB.
- The RAVEN toolbox for MATLAB.
- The libSBML MATLAB API (version 5.17.0 is recommended).
Usage
-
For creating an enzyme constrained model:
- Update the following data files in
/databases
with your organism infomation:-
databases/prot_abundance.txt
: Protein abundance Data from Pax-DB. If data is not available for your organism, then a relative proteomics dataset (in molar fractions) can be used instead. The required format is a tab-separated file, named asdatabases/relative_proteomics.txt
, with a single header line and 2 columns; the first with gene IDs and the second with the relative abundances for each protein. -
databases/uniprot.tab
: Gene-proteins data from uniprot. -
databases/chemostatData.tsv
: Chemostat data for estimating GAM (optional, called byfitGAM.m
). -
databases/manual_data.txt
: Kcat data from eventual manual curations (optional, called bymanualModifications.m
).
-
- Adapt the following functions in
/geckomat
to your organism:geckomat/getModelParameters.m
geckomat/change_model/manualModifications.m
geckomat/limit_proteins/sumProtein.m
geckomat/limit_proteins/scaleBioMass.m
-
geckomat/kcat_sensitivity_analysis/changeMedia_batch.m
(optional) -
geckomat/change_model/removeIncorrectPathways.m
(optional, called bymanualModifications.m
) -
geckomat/limit_proteins/sumBioMass.m
(optional, called bysumProtein.m
&scaleBiomass.m
)
- Run
geckomat/get_enzyme_data/updateDatabases.m
to updateProtDatabase.mat
. - Run
geckomat/enhanceGEM.m
with your metabolic model as input.
- Update the following data files in
- For performing simulations with an enzyme-constrained model: Enzyme-constrained models can be used as any other metabolic model, with toolboxes such as COBRA or RAVEN. For more information on rxn/met naming convention, see the supporting information of Sanchez et al. (2017)
geckopy: Integrating proteomic data to ecYeastGEM
If all you need is the ecYeastGEM model to use together with cobrapy you can use the geckopy
Python package.
Required software
- Python 3.6, 3.7 or 3.8
- cobrapy
Installation
pip install geckopy
Usage
from geckopy import GeckoModel
import pandas
some_measurements = pandas.Series({'P00549': 0.1, 'P31373': 0.1, 'P31382': 0.1})
model = GeckoModel('multi-pool')
model.limit_proteins(some_measurements)
model.optimize()
Contributing
Contributions are always welcome! Please read the contributing guidelines to get started.
Contributors
- Ivan Domenzain (@IVANDOMENZAIN), Chalmers University of Technology, Gothenburg Sweden
- Eduard Kerkhoven (@edkerk), Chalmers University of Technology, Gothenburg Sweden
- Benjamin J. Sanchez (@BenjaSanchez), Chalmers University of Technology, Gothenburg Sweden
- Moritz Emanuel Beber (@Midnighter), Danish Technical University, Lyngby Denmark
- Henning Redestig (@hredestig), Danish Technical University, Lyngby Denmark
- Cheng Zhang, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm Sweden