ArticleBioinformatics (Oxford, England)2019
Flux tope analysis: studying the coordination of reaction directions in metabolic networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 16 citations in OpenAlex.
- A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network.Scientific reports · 2024Article
- LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models.BMC bioinformatics · 2024Article
- Local flux coordination and global gene expression regulation in metabolic modeling.Nature communications · 2023Article
- ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network.Frontiers in molecular biosciences · 2023Article
- The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility.PLoS computational biology · 2022Article
- Towards scaling elementary flux mode computation.Briefings in bioinformatics · 2020Article
- Maximum entropy and population heterogeneity in continuous cell cultures.PLoS computational biology · 2019Article
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Authors and funding
4 authors at 3 institutions in 1 country.
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Abstract
Motivation: Elementary flux mode (EFM) analysis allows an unbiased description of metabolic networks in terms of minimal pathways (involving a minimal set of reactions). To date, the enumeration of EFMs is impracticable in genome-scale metabolic models. In a complementary approach, we introduce the concept of a flux tope (FT), involving a maximal set of reactions (with fixed directions), which allows one to study the coordination of reaction directions in metabolic networks and opens a new way for EFM enumeration. Results: A FT is a (nontrivial) subset of the flux cone specified by fixing the directions of all reversible reactions. In a consistent metabolic network (without unused reactions), every FT contains a 'maximal pathway', carrying flux in all reactions. This decomposition of the flux cone into FTs allows the enumeration of EFMs (of individual FTs) without increasing the problem dimension by reaction splitting. To develop a mathematical framework for FT analysis, we build on the concepts of sign vectors and hyperplane arrangements. Thereby, we observe that FT analysis can be applied also to flux optimization problems involving additional (inhomogeneous) linear constraints. For the enumeration of FTs, we adapt the reverse search algorithm and provide an efficient implementation. We demonstrate that (biomass-optimal) FTs can be enumerated in genome-scale metabolic models of B.cuenoti and E.coli, and we use FTs to enumerate EFMs in models of M.genitalium and B.cuenoti. Availability and implementation: The source code is freely available at https://github.com/mpgerstl/FTA. Supplementary information: Supplementary data are available at Bioinformatics online.
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