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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 the publication:

Benjamin J. Sanchez, Cheng Zhang, Avlant Nilsson, Petri-Jaan Lahtvee, Eduard J. Kerkhoven, Jens Nielsen (2017). Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Molecular Systems Biology, 13(8):935

The software comes in two flavors, Python and Matlab scripts to fetch online data and build the published ecYeast7 GECKO models, and a Python package which can be used with cobrapy to obtain a ecYeast7 model object, optionally adjusted for provided proteomics data.

Last update: 2018-03-21

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.

Building a GECKO model

Required software - Python module

easy_install-2.7 SOAPpy

Required software - Matlab module

  • MATLAB (7.5 or higher) + Optimization Toolbox.
  • The COBRA toolbox for MATLAB. Note that libSBML and the SBML toolbox should both be installed. Both of them are free of charge for academic users. Aditionally, you should add the cobra folder to your MATLAB search path.


See the supporting information of Sanchez et al. (2017)

Integrating proteomic data to the yeast GECKO model

If all you need is the ecYeast7 model to use together with cobrapy you can use the geckopy Python package.

Required software

  • Python 2.7, 3.4, 3.5 or 3.6
  • cobrapy


pip install geckopy


from geckopy import GeckoModel
import pandas
some_measurements = pandas.Series({'P00549': 0.1, 'P31373': 0.1, 'P31382': 0.1})
model = GeckoModel('multi-pool')


  • Benjamin J. Sanchez (@BenjaSanchez), Chalmers University of Technology, Gothenburg Sweden
  • Ivan Domenzain (@IVANDOMENZAIN), 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