Improving the EFMs quality by augmenting their representativeness in LP methods

BackgroundAlthough 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.ResultsThis 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.ConclusionsOur 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.

[1]  José M. García,et al.  A New Approach to Obtain EFMs Using Graph Methods Based on the Shortest Path between End Nodes , 2015, IWBBIO.

[2]  Christoph Kaleta,et al.  Metabolic Pathway Analysis : from small to genome-scale networks , 2011 .

[3]  A. James.  2010 , 2011, Philo of Alexandria: an Annotated Bibliography 2007-2016.

[4]  Edward J. Perkins,et al.  Comparison of transcriptional responses in liver tissue and primary hepatocyte cell cultures after exposure to hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine , 2006, BMC Bioinformatics.

[5]  Francisco J. Planes,et al.  A critical examination of stoichiometric and path-finding approaches to metabolic pathways , 2008, Briefings Bioinform..

[6]  Francisco J. Planes,et al.  Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks , 2014, Bioinform..

[7]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[8]  S. Schuster,et al.  ON ELEMENTARY FLUX MODES IN BIOCHEMICAL REACTION SYSTEMS AT STEADY STATE , 1994 .

[9]  José M. García,et al.  A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes , 2016 .

[10]  C. Schilling,et al.  Flux coupling analysis of genome-scale metabolic network reconstructions. , 2004, Genome research.

[11]  Ronan M. T. Fleming,et al.  Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide. , 2010, EcoSal Plus.

[12]  Alexander Isaev,et al.  PyEvolve: a toolkit for statistical modelling of molecular evolution , 2004, BMC Bioinformatics.

[13]  Angel Rubio,et al.  Computing the shortest elementary flux modes in genome-scale metabolic networks , 2009, Bioinform..

[14]  Angel Rubio,et al.  Selection of human tissue-specific elementary flux modes using gene expression data , 2013, Bioinform..

[15]  Bernhard O. Palsson,et al.  Metabolite coupling in genome-scale metabolic networks , 2006, BMC Bioinformatics.

[16]  Nikos Vlassis,et al.  Fast Reconstruction of Compact Context-Specific Metabolic Network Models , 2013, PLoS Comput. Biol..

[17]  Angel Rubio,et al.  Advances in network-based metabolic pathway analysis and gene expression data integration , 2015, Briefings Bioinform..

[18]  Morteza Saheb Zamani,et al.  A graph-based approach to analyze flux-balanced pathways in metabolic networks , 2018, Biosyst..

[19]  Vassily Hatzimanikatis,et al.  redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models , 2017, PLoS Comput. Biol..

[20]  Joachim Selbig,et al.  F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks , 2012, BMC Bioinformatics.

[21]  Alexander Bockmayr,et al.  A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks , 2017, BMC Bioinformatics.

[22]  F. Friedler,et al.  Graph-theoretical identification of pathways for biochemical reactions , 2001, Biotechnology Letters.

[23]  Daniel Boley,et al.  Divide-and-Conquer Approach to the Parallel Computation of Elementary Flux Modes in Metabolic Networks , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[24]  D. Fell,et al.  Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. , 1999, Trends in biotechnology.

[25]  Bernhard O. Palsson,et al.  GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data , 2013, Bioinform..

[26]  Jason A. Papin,et al.  Extreme pathway lengths and reaction participation in genome-scale metabolic networks. , 2002, Genome research.

[27]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[28]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[29]  Steffen Klamt,et al.  Computation of elementary modes: a unifying framework and the new binary approach , 2004, BMC Bioinformatics.