PTA Overview

This is the documentation for the PTA python package. PTA is available on git and via pip. See getting started for the installation instructions and a simple example.

Probabilistic Thermodynamic Analysis

Probabilistic Thermodynamic Analysis (PTA) [1] is a framework for the exploration of the thermodynamic properties of a metabolic network. In PTA, we consider the steady-state thermodynamic space of a network, that is, the space of standard reaction energies and metabolite concentrations that are compatible with steady state flux constraints. The uncertainty of the variables in the thermodynamic space is modeled with a probability distribution, allowing analysis with optimization and sampling approaches.

Probabilistic Metabolic Optimization (PMO)

PMO aims at finding the most probable values of reaction energies and metabolite concentrations that are compatible with the steady state constrain. This method is particularly useful to indentify features of the network that are thermodynamically unrealistic. For example, PMO can identify substrate channeling, incorrect cofactors or inaccurate directionalities.

Thermodynamic and Flux Sampling (TFS)

TFS allows to jointly sample the thermodynamic and flux spaces of a network. The method provides estimates of metabolite concentrations, reactions directions, and flux distributions.

References

[1]

Mattia G Gollub, Hans-Michael Kaltenbach, and Jörg Stelling. Probabilistic thermodynamic analysis of metabolic networks. Bioinformatics, 37(18):2938–2945, 2021.