Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

[1]  K. Ülgen,et al.  Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks , 2004, Biotechnology and bioengineering.

[2]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.

[3]  Eugénio C. Ferreira,et al.  Random sampling of elementary flux modes in large-scale metabolic networks , 2012, Bioinform..

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

[5]  F. Srienc,et al.  Hexoses and Pentoses Efficient Production of Ethanol from Cell for the Most Escherichia Coli Minimal Supplemental Material , 2022 .

[6]  Ali R. Zomorrodi,et al.  Mathematical optimization applications in metabolic networks. , 2012, Metabolic engineering.

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

[8]  M. Kanehisa,et al.  Observing metabolic functions at the genome scale , 2007, Genome Biology.

[9]  Jason A. Papin,et al.  The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. , 2002, Journal of theoretical biology.

[10]  B. Palsson,et al.  Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. , 2000, Journal of theoretical biology.

[11]  Sayed-Amir Marashi,et al.  Constraint-based Analysis of Substructures of Metabolic Networks , 2011 .

[12]  Jason A. Papin,et al.  Genome-scale microbial in silico models: the constraints-based approach. , 2003, Trends in biotechnology.

[13]  Jens Nielsen,et al.  Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae , 2007, BMC Systems Biology.

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

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

[16]  Thomas Pfeiffer,et al.  Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae , 2002, Bioinform..

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

[18]  S. Schuster,et al.  Metabolic network structure determines key aspects of functionality and regulation , 2002, Nature.

[19]  Jörg Stelling,et al.  Large-scale computation of elementary flux modes with bit pattern trees , 2008, Bioinform..

[20]  Steffen Klamt,et al.  CASOP: a computational approach for strain optimization aiming at high productivity. , 2010, Journal of biotechnology.

[21]  Z. Xiu,et al.  Effect of Oxygen Level on Efficiencies of Metabolic Fluxes in Klebsiella pneumoniae , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[22]  R. Carlson,et al.  The fractional contributions of elementary modes to the metabolism of Escherichia coli and their estimation from reaction entropies. , 2006, Metabolic engineering.

[23]  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.

[24]  Komei Fukuda,et al.  Double Description Method Revisited , 1995, Combinatorics and Computer Science.

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

[26]  S. Schuster,et al.  Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. , 2009, Genome research.

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

[28]  Michel Deza,et al.  Combinatorics and Computer Science , 1996, Lecture Notes in Computer Science.

[29]  B. Palsson,et al.  Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods , 2012, Nature Reviews Microbiology.

[30]  S. Oliver,et al.  Chance and necessity in the evolution of minimal metabolic networks , 2006, Nature.

[31]  Alexander Bockmayr,et al.  Analysis of Metabolic Subnetworks by Flux Cone Projection , 2011, Algorithms for Molecular Biology.

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