Publication: Improving the EFMs quality by augmenting their representativeness in LP methods
Authors
Hidalgo Céspedes, José Francisco ; Egea, José A. ; Guil Asensio, Francisco ; García Carrasco, José manuel
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Publisher
BMC
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DOI
https://doi.org/10.1186/s12918-018-0619-1
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info:eu-repo/semantics/article
Description
©2018. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/
This document is the Published, version of a Published Work that appeared in final form in BMC Systems Biology. To access the final edited and published work see https://doi.org/10.1186/s12918-018-0619-1
Abstract
Background: Although cellular metabolism has been widely studied, its fully comprehension is still a challenge. A
main tool for this study is the analysis of meaningful pieces of knowledge called modes and, in particular, specially
interesting classes of modes such as pathways and Elementary Flux Modes (EFMs). Its study often has to deal with
issues such as the appearance of infeasibilities or the difficulty of finding representative enough sets of modes that are
free of repetitions. Mode extraction methods usually incorporate strategies devoted to mitigate this phenomena but
they still get a high ratio of repetitions in the set of solutions.
Results: This paper presents a proposal to improve the representativeness of the full set of metabolic reactions in the
set of computed modes by penalizing the eventual high frequency of occurrence of some reactions during the
extraction. This strategy can be applied to any linear programming based extraction existent method.
Conclusions: Our strategy enhances the quality of a set of extracted EFMs favouring the presence of every reaction
in it and improving the efficiency by mitigating the occurrence of repeated solutions. The new proposed strategy can
complement other EFMs extraction methods based on linear programming. The obtained solutions are more likely to
be diverse using less computing effort and improving the efficiency of the extraction.
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Citation
BMC Systems Biology
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