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OptStoic python package

Perform optStoic analysis using Python code that share the same data files with GAMS code.

Note: All the examples are specific for glycolysis pathway generation.


git clone
cd optstoic-python
python install


At least one of the following optimization solvers should be installed. To solve the loopless optStoic formulation, an optimization solver other than GLPK is recommended.

  1. GLPK 4.47 installation

    tar -xvzf glpk-4.47.tar.gz
    cd  ~/glpk-4.47
    make install
    #if the program is successfully installed, you should get an output by typing
    glpsol --version
  2. GUROBI Optimization provide academic license for free ( Install gurobipy following the instruction provided by GUROBI.

  3. SCIP Optimization Suite >= v4.0.0

sudo apt-get install libgmp-dev libreadline-dev zlib1g-dev libncurses5-dev
tar xvf scipoptsuite-6.0.0.tgz
cd scipoptsuite-6.0.0/
make test
cd scip-6.0.0/
sudo make install INSTALLDIR="/usr/local/"
/usr/local/bin/scip --version
  1. CPLEX Optimizer

Current project dependencies

  1. PuLP. Run the test.
pip install pulp
  1. pandas
pip install pandas
  1. Graphviz (Optional, for drawing pathway). The Graphviz software is required before installing the graphviz python package.

    #If you have root access
    sudo apt-get install graphviz
    #If you do not have root access (you can get a different version of Graphviz from their website
    cd $HOME
    mkdir -p bin/graphviz
    tar xvf graphviz-2.38.0.tar.gz
    cd graphviz-2.38.0
    ./configure --prefix=$HOME/bin/graphviz
    make && make install
    # Check if the graphviz is working
    cd $HOME/bin/graphviz/bin
    dot -V
    # Add the following line to your .bashrc
    export PATH=$PATH:$HOME/bin/graphviz/bin
    #Install the Python graphviz package
    pip install graphviz
  2. Component-Contribution (*Optional, unless you want to perform MDF analysis)


After cloning the repo or setup, please run tests as followed:

pip install nose 
nosetests -s -v 

To test the optimization algorithms, please run tests as followed. The runtime depends on the solvers selected by PuLP.

from optstoicpy.test.testAll import test_all_optimization_scripts


Read the tutorial.


To continue development with the code, please create a virtual environment and use python develop for installation.


Please cite Chiam Yu Ng, Lin Wang, Anupam Chowdhury and Costas D. Maranas, 2018 (under review).

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