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Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.

ABSTRACT: MOTIVATION:Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. RESULTS:Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. AVAILABILITY AND IMPLEMENTATION:Software is freely available at https://github.com/biogacop/Boost_LP_EFM. CONTACT:fguil@um.es. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Guil F

PROVIDER: S-EPMC7390993 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.

Guil Francisco F Hidalgo José F JF García José M JM

Bioinformatics (Oxford, England) 20200801 14

<h4>Motivation</h4>Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs.<h4>Results</h4>Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on ...[more]

PMID: 32348455

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Project description:Background(13)C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis.ResultsMetabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (Corynebacterium glutamicum) and a plant species (Brassica napus) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate i.e. a sugar source for C. glutamicum and a nitrogen source for B. napus - of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In B. napus, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed.ConclusionThis consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement (13)C metabolic flux analysis, by allowing the prediction of changes in internal fluxes before carbon labelling experiments.

Dataset Information

Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.

Publications

Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.

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