Thermodynamic constraints for flux-based reactions, in Python.

Implements: Henry, Christopher S., Linda J. Broadbelt, and Vassily Hatzimanikatis. "Thermodynamics-based metabolic flux analysis." Biophysical journal 92.5 (2007): 1792-1805.

This module requires cobrapy to work properly. You might also want to install a dedicated solver. GLPK, CPLEX and Gurobi are supported.

You might want to use this program inside of a container. the docker/ subfolder has all the necessary information and source files to set it up.

NOTE : This step is not required if you're using the container, which bundles all this

You can install this module with pip:

pip install -e /path/to/pytfa

Make sure sphinx is installed, and install as well the theme (this is already bundled with the container):

pip install sphinx sphinx-rtd-theme

Then begin the generation :

cd work/pytfa/doc && make html

The resulting HTML files are located in work/pytfa/doc/_build.

Install pytest if you don't already have it (pip install pytest, already included in the container), then start the tests with the pytest command.

First, create your COBRApy model. Make sure to define the additional values required by pyTFA, as said in the "Models" page of the documentation.

If you already have a Matlab model with thermodynamic data, you might want to use pytfa.io.import_matlab_model. Otherwise, have a look at the COBRApy documentation, then add the required properties.

If you're using a specific solver, don't forget to tell COBRApy about it by setting the solver property of your model to the name of your solver. See the COBRApy documentation for more information about this.

You also need a thermodynamic database. Use thermoDBconverter.py if you have a thermodynamic database from Matlab you wish to import to Python.

Thermodynamic databases are stored in .thermodb files and can be easily loaded with pytfa.io.load_thermoDB.

First, prepare your model for TFBA analysis with pytfa.prepModelForTFBA. Then, call pytfa.convToTFA to add the thermodynamic-based constraints.

Finally, use the optimize method of your model to find an optimal solution.

Here is an example script :

import pytfa
from pytfa.io import import_matlab_model, load_thermoDB


cobra_model = import_matlab_model('../models/small_yeast.mat')

thermo_data = load_thermoDB('../data/thermo_data.thermodb')

mytfa = pytfa.ThermoModel(thermo_data, cobra_model)
mytfa.solver = 'optlang-cplex'

## TFA conversion
mytfa.prepare()
mytfa.convert()

## Info on the model
mytfa.print_info()

## Optimality
tfa_solution = mytfa.optimize()