ArticleBioinformatics (Oxford, England)2020
Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- On the representativeness and stability of a set of EFMs.Bioinformatics (Oxford, England) · 2023Article
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3 authors.
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Abstract
motivationElementary 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.
resultsHere, 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.
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