Probabilistic Thermodynamic Analysis of metabolic networks.
Probabilistic Thermodynamic Analysis (PTA) 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) 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 identify 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) allows to sample the thermodynamic and flux spaces of a network. The method provides estimates of metabolite concentrations, reactions directions, and flux distributions.
Installation and usage
Please see the online documentation.
Cite us
If you use PTA in a scientific publication, please cite our paper:
Gollub, M.G., Kaltenbach, H.M., Stelling, J., 2021. "Probabilistic Thermodynamic Analysis of Metabolic Networks". Bioinformatics. - DOI
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Metadata
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Release files for pta 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| pta-0.6.0.tar.gz | 3.1 MB | Details |
Release files / pta-0.6.0.tar.gz
| Download URL | pta-0.6.0.tar.gz |
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| Size | 3.1 MB |
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5b7805ffc05085d6efc4cd24ab9e3cbf330a17a05ac888b59e87b09786765501
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twine/4.0.2 CPython/3.12.1
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