DISCOPOLIS 2.0: a new recursive version of the algorithm for uniform sampling of metabolic flux distributions with linear programming

• Published in: IFAC-PapersOnLine Vol. 54; no. 3; pp. 300 - 305
• Main Authors: Bogaerts, Philippe, Rooman, Marianne
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2021
• Subjects: constrained-based modeling; Flux Balance Analysis; Flux Variability Analysis; hit-and-run methods; Metabolic Flux Analysis; Metabolic network; Most Accurate Fluxes; sampling methods; underdetermined systems; uniform sampling; hit-and-run methods; sampling methods; Metabolic network; uniform sampling; Flux Balance Analysis; constrained-based modeling; Metabolic Flux Analysis; underdetermined systems; Flux Variability Analysis; Most Accurate Fluxes
• DOI: 10.1016/j.ifacol.2021.08.258
• ISSN: 2405-8963

Metabolic 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.