F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

BackgroundFlux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.ResultsWe present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.ConclusionsWe propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.

[1]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[2]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[3]  Markus J. Herrgård,et al.  Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. , 2004, Genome research.

[4]  Alexander Bockmayr,et al.  A new constraint-based description of the steady-state flux cone of metabolic networks , 2009, Discret. Appl. Math..

[5]  Balázs Papp,et al.  Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling. , 2007, Genome research.

[6]  Adam M. Feist,et al.  Modeling methanogenesis with a genome‐scale metabolic reconstruction of Methanosarcina barkeri , 2006 .

[7]  Eytan Ruppin,et al.  Metabolic modeling of endosymbiont genome reduction on a temporal scale , 2011, Molecular systems biology.

[8]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[9]  Chen Qian,et al.  Comparative study of computational methods to detect the correlated reaction sets in biochemical networks , 2011, Briefings Bioinform..

[10]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

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

[12]  Abdelhalim Larhlimi,et al.  New concepts and tools in constraint-based analysis of metabolic networks , 2009 .

[13]  Bas Teusink,et al.  Co-Regulation of Metabolic Genes Is Better Explained by Flux Coupling Than by Network Distance , 2008, PLoS Comput. Biol..

[14]  B. L. Clarke Stability of Complex Reaction Networks , 2007 .

[15]  B. Palsson,et al.  Identifying constraints that govern cell behavior: a key to converting conceptual to computational models in biology? , 2003, Biotechnology and bioengineering.

[16]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[17]  Steffen Klamt,et al.  FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps , 2003, Bioinform..

[18]  Bernhard O. Palsson,et al.  Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions , 2000, BMC Bioinformatics.

[19]  D. Fell,et al.  Reaction routes in biochemical reaction systems: Algebraic properties, validated calculation procedure and example from nucleotide metabolism , 2002, Journal of mathematical biology.

[20]  C. Maranas,et al.  Improved computational performance of MFA using elementary metabolite units and flux coupling. , 2010, Metabolic engineering.

[21]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[22]  C. Pál,et al.  Adaptive evolution of bacterial metabolic networks by horizontal gene transfer , 2005, Nature Genetics.

[23]  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.

[24]  Monika Heiner,et al.  Steady state analysis of metabolic pathways using Petri nets , 2003, Silico Biol..

[25]  Bernhard O. Palsson,et al.  Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets , 2007 .

[26]  Erwin P. Gianchandani,et al.  Flux balance analysis in the era of metabolomics , 2006, Briefings Bioinform..

[27]  S. Oliver,et al.  An integrated approach to characterize genetic interaction networks in yeast metabolism , 2011, Nature Genetics.

[28]  B. Palsson,et al.  Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. , 2000, Journal of theoretical biology.

[29]  E. Ruppin,et al.  Regulatory on/off minimization of metabolic flux changes after genetic perturbations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Arnau Montagud,et al.  Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803. , 2011, Biotechnology journal.

[31]  Krin A. Kay,et al.  The implications of human metabolic network topology for disease comorbidity , 2008, Proceedings of the National Academy of Sciences.

[32]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[33]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[34]  Robert Urbanczik,et al.  Functional stoichiometric analysis of metabolic networks , 2005, Bioinform..

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

[36]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[37]  Alexander Bockmayr,et al.  A New Approach to Flux Coupling Analysis of Metabolic Networks , 2006, CompLife.

[38]  Bas E Dutilh,et al.  Asymmetric relationships between proteins shape genome evolution , 2009, Genome Biology.

[39]  Alexander Bockmayr,et al.  FFCA: a feasibility-based method for flux coupling analysis of metabolic networks , 2011, BMC Bioinformatics.

[40]  Juan Carlos Nuño,et al.  METATOOL: for studying metabolic networks , 1999, Bioinform..