1. Quick start¶
Three tutorial files detail thoroughly normal usages of the pytfa package. They can be found at:
pytfa └── tutorials ├── figure_paper.py ├── tutorial_basics.py └── tutorial_sampling.py
figure_paper.py details how to get the figure from our paper 1, a simple use case for TFA on a reduced Escherichia coli. We show that adding thermodyamics constraints and simple concentration data allow to substantially reduce the flux space.
tutorial_basics.py shows a more realistic case with two models (reduced or full genome-scale) of Escherichia coli. It also cycles through several solvers (if more are installed), to show how simple it is to change your solver (thanks to optlang).
tutorial_sampling.py shows how to sample a variable, for example thermodynamic displacement, and generate plots to visualize the results.
If you plan to run the tutorials with full genome-scale models, we recommend you to get a commercial solver, as it has been seen that GLPK’s lack of parallelism significantly increases solving time
The next sections give more details on how the thermodynamic model is structured, and how data is managed.
The py.TFA team
Salvy, P., Fengos, G., Ataman, M., Pathier, T., Soh, K. C., & Hatzimanikatis, V. (2018). pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis. Bioinformatics, 35(1), 167-169.