Toolbox for including enzyme constraints on a genome-scale model.

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GECKO logo

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 following 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

GECKO was written by Benjamin J. Sanchez (@BenjaSanchez), Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, and Cheng Zhang, Science for Life Laboratory, KTH - Royal Institute of Technology.

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.

Scripts for building a GECKO model

Only tested on Windows, probably works on other platforms as well with adjusted installation procedure.

Required software for running the Python scripts

  • Python 2.7
  • setuptools for python 2.7, accessible here
  • SOAPpy: for this, open command prompt as admin, and then do:
    cd C:\Python27\Scripts
    easy_install-2.7 SOAPpy

Required software for using the Matlab scripts

  • 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 Sánchez et al. (2017)

Using the geckopy Python package for obtaining an adjusted GECKO model object

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')