Dataset Information

Probabilistic Thermodynamic Analysis of Metabolic Networks.

ABSTRACT:

Motivation

Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism's potential or actual metabolic operations.

Results

We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli's metabolic capabilities.

Availability

Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gollub MG

PROVIDER: S-EPMC8479673 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Probabilistic thermodynamic analysis of metabolic networks.

Gollub Mattia G MG Kaltenbach Hans-Michael HM Stelling Jörg J

Bioinformatics (Oxford, England) 20210901 18

<h4>Motivation</h4>Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism's potential or actual metabolic operations.<h4>Results</h4>We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods ...[more]

PMID: 33755125

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