文章基本信息

标题：DISCOPOLIS 2.0: a new recursive version of the algorithm for uniform sampling of metabolic flux distributions with linear programming
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作者：Philippe Bogaerts ; Marianne Rooman
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：3
页码：300-305
DOI：10.1016/j.ifacol.2021.08.258
语种：English
出版社：Elsevier
摘要：AbstractMetabolic flux values are subject to equality (e.g., mass balances, measured fluxes) and inequality (e.g., upper and lower flux bounds) constraints. The system is generally underdetermined,i.e.with more unknown fluxes than equations, and all the admissible solutions belong to a convex polytope. Sampling that polytope allows subsequently computing marginal distributions for each metabolic flux. We propose a new version of the DISCOPOLIS algorithm (DIscrete Sampling of COnvex POlytopes via Linear program Iterative Sequences) that provides the same weight to all the samples and that approximates a uniform distribution thanks to a recursive loop that computes variable numbers (called grid points) of samples depending on the fluxes that have already been fixed in former iterations. The method is illustrated on three different case studies (with 3, 95 and 1054 fluxes) and shows interesting results in terms of flux distribution convergence and large ranges of the marginal flux distributions. Three consistent criteria are proposed to choose the optimal maximum number of grid points.
关键词：KeywordsMetabolic networkMetabolic Flux AnalysisFlux Balance AnalysisFlux Variability AnalysisMost Accurate Fluxessampling methodsuniform samplinghit-and-run methodsunderdetermined systemsconstrained-based modeling