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• W4387364163 endingPage "111632" @default.
• W4387364163 startingPage "111632" @default.
• W4387364163 abstract "Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network." @default.
• W4387364163 created "2023-10-06" @default.
• W4387364163 creator A5000770856 @default.
• W4387364163 creator A5009680404 @default.
• W4387364163 date "2023-11-01" @default.
• W4387364163 modified "2023-10-13" @default.
• W4387364163 title "A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks" @default.
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• W4387364163 doi "https://doi.org/10.1016/j.jtbi.2023.111632" @default.
• W4387364163 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37804942" @default.
• W4387364163 hasPublicationYear "2023" @default.
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