Publication: Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear
programming approach
Authors
Guil Asensio, Francisco ; Hidalgo Céspedes, José Francisco ; García Carrasco, José Manuel
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Publisher
Oxford Academic
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DOI
https://doi.org/10.1093/bioinformatics/btaa280
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info:eu-repo/semantics/article
Description
©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
This document is the Published, version of a Published Work that appeared in final form in Bioinformatics. To access the final edited and published work see https://doi.org/10.1093/bioinformatics/btaa280
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
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Citation
Bioinformatics, 36(14), 2020, 4163–4170
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