1. Installation

1.1. Using PIP

ETFBA is compatible with Python versions 3.8 through 3.11 and can be easily installed using pip from PyPI. To get started, first upgrade pip using the following command:

python -m pip install --upgrade pip

Next, install ETFBA with the command:

pip install etfba

Alternatively, you can install FreeFlux from the source code by cloning the GitHub repository using the following command (assuming you have git installed):

git clone https://github.com/Chaowu88/etfba.git /path/to/etfba

Then, install ETFBA using pip:

pip install /path/to/etfba

Note

It’s recommended to install ETFBA within a virtual environment to avoid conflicts with other Python packages. Refer to these instructions on creating virtual environments or here for Conda environments.

1.2. Solver Installation

ETFBA uses the modeling language Pyomo to formulate linear programming (LP) and mixed integer linear programming (MILP) problems. For small-sized models, the freely available solver glpk is capable of handling the workload, which can be installed by:

conda install -c conda-forge glpk

For larger models, such as genome scale models, it is highly recommended to use the commercial optimizer Gurobi and install the Python support:

conda install -c gurobi gurobi