Article Dans Une Revue PLoS Computational Biology Année : 2020

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

Résumé

Metabolism is a fundamental biochemical process that enables all organisms to operate and grow by converting nutrients into energy and ‘building blocks’. Metabolic flux analysis allows the quantification of metabolic fluxes in vivo, i.e. the actual rates of biochemical conversions in biological systems, and is increasingly used to probe metabolic activity in biology, biotechnology and medicine. Isotope labeling experiments coupled with mathematical models of large metabolic networks are the most commonly used approaches to quantify fluxes within cells. However, many biological questions only require flux information from a subset of reactions, not the full network. Here, we propose a new approach with three main advantages over existing methods: better scalability (fluxes can be measured through a single reaction, a metabolic pathway or a set of pathways of interest), better robustness to missing data and information gaps, and lower requirements in terms of measurements and computational resources. We validate our method both theoretically and experimentally. ScalaFlux can be used for high-throughput flux measurements in virtually any metabolic system and paves the way to the analysis of dynamic fluxome rearrangements.

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hal-04976157 , version 1 (04-03-2025)

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Pierre Millard, Uwe Schmitt, Patrick Kiefer, Julia Vorholt, Stéphanie Heux, et al.. ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks. PLoS Computational Biology, 2020, 16 (4), pp.e1007799. ⟨10.1371/journal.pcbi.1007799⟩. ⟨hal-04976157⟩
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