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Updated: Nov 11, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
Published on: December 4, 2021
Probabilistic thermodynamic analysis of metabolic networks
Mattia G Gollub1, Hans-Michael Kaltenbach1, Jörg Stelling1
1Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel 4058, Switzerland.
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 Escherichia 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 And Implementation:
Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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