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Generic flux coupling analysis. (English) Zbl 1315.92035

Summary: Flux coupling analysis (FCA) has become a useful tool for aiding metabolic reconstructions and guiding genetic manipulations. Originally, it was introduced for constraint-based models of metabolic networks that are based on the steady-state assumption. Recently, we have shown that the steady-state assumption can be replaced by a weaker lattice-theoretic property related to the supports of metabolic fluxes. In this paper, we further extend our approach and develop an efficient algorithm for generic flux coupling analysis that works with any kind of qualitative pathway model. We illustrate our method by thermodynamic flux coupling analysis (tFCA), which allows studying steady-state metabolic models with loop-law thermodynamic constraints. These models do not satisfy the lattice-theoretic properties required in our previous work. For a selection of genome-scale metabolic network reconstructions, we discuss both theoretically and practically, how thermodynamic constraints strengthen the coupling results that can be obtained with classical FCA. A prototype implementation of tFCA is available at http://hoverboard.io/L4FC.

MSC:

92C42 Systems biology, networks
92C40 Biochemistry, molecular biology
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[1] Aho, A. V.; Garey, M. R.; Ullman, J. D., The transitive reduction of a directed graph, SIAM J. Comput., 1, 2, 131-137 (1972) · Zbl 0247.05128
[2] Robert, A. A., Thermodynamics of Biochemcial Reactions (2003), Massachusetts Institute of Technology: Massachusetts Institute of Technology Cambridge, MA
[3] Beard, D. A.; Babson, E.; Curtis, E.; Qian, H., Thermodynamic constraints for biochemical networks, J. Theor. Biol., 228, 327-333 (2004) · Zbl 1439.92091
[4] Beard, D. A.; Liang, S.d.; Qian, H., Energy balance for analysis of complex metabolic networks, Biophys. J., 83, 79-86 (2002)
[5] Beard, D. A.; Qian, H., Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism, Am. J. Physiol. - Endocrinol. Metab., 288, E633-E644 (2005)
[6] Bordbar, A.; Monk, J. M.; King, Z. A.; Palsson, B., Constraint-based models predict metabolic and associated cellular functions, Nat. Rev. Genet., 15, 2, 107-120 (2014)
[7] Bundy, J. G.; Papp, B. A.; Harmston, R.; Browne, R. A.; Clayson, E. M.; Burton, N.; Reece, R. J.; Oliver, S. G.; Brindle, K. M., Evaluation of predicted network modules in yeast metabolism using nmr-based metabolite profiling, Genome Res., 17, 4, 510-519 (2007)
[8] Burgard, A. P.; Nikolaev Evgeni, V.; Schilling, C. H.; Maranas, C. D., Flux coupling analysis of genome-scale metabolic network reconstructions, Genome Res., 14, 2, 301-312 (2004)
[9] Carbonell, P.; Fichera, D.; Pandit, S.; Faulon, J. L., Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms, BMC Syst. Biol., 6, 1, 10 (2012)
[10] Cogne, G.; Rügen, M.; Bockmayr, A.; Titical, M.; Dussap, C. G.; Cornet, J. F.A.o.; Legrand, J., A model-based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae, Chlamydomonas reinhardtii, Biotechnol. Prog., 27, 3, 631-640 (2011)
[11] Brian, A. D., Introduction to Lattices and Order (2002), Cambridge University Press
[12] David, L.; Marashi, S. A.; Larhlimi, A.; Mieth, B.; Bockmayr, A., Ffca: a feasibility-based method for flux coupling analysis of metabolic networks, BMC Bioinformatics, 12, 236 (2011)
[14] Fleming, R. M.T.; Thiele, I.; Nasheuer, H. P., Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli, Biophys. Chem., 145, 47-56 (2009)
[15] Fleming, R. M.T.; Thiele, I.; Provan, G.; Nasheuer, H. P., Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism, J. Theor. Biol., 264, 683-692 (2010) · Zbl 1406.92180
[16] Fleming, R. M.T.; Maes, C. M.; Saunders, M. A.; Ye, Y.; Palsson, B. O., A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks, J. Theor. Biol., 292, 71-77 (2012) · Zbl 1307.92104
[17] Goldstein, Y. A.B.; Bockmayr, A., A lattice-theoretic framework for metabolic pathway analysis, (Gupta, A.; Henzinger, T. A., Computational Methods in Systems Biology, Lecture Notes in Computer Science, 8130 (2013), Springer: Springer Berlin/Heidelberg), 178-191
[18] Haus, U. U.; Klamt, S.; Stephen, T., Computing knockout strategies in metabolic networks, J. Comput. Biol., 15, 3, 259-268 (2008)
[19] Henry, C. S.; Broadbelt, L. J.; Hatzimanikatis, V., Thermodynamics-based metabolic flux analysis, Biophys. J., 92, 1792-1805 (2007)
[20] Heuett, W. J.; Qian, H., Combining flux and energy balance analysis to model large-scale biochemical networks, J. Bioinform. Comput. Biol., 4, 6, 1227-1243 (2006)
[21] Hoppe, A.; Hoffmann, S.; Holzhütter, H. G., Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks, BMC Syst. Biol., 1, 23 (2007)
[22] Jol, S. J.; Kümmel, A.; Terzer, M.; Stelling, J.; Heinemann, M., System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes, PLoS Comput. Biol., 8, 3 (2012)
[23] Kümmel, A.; Panke, S.; Heinemann, M., Systematic assignment of thermodynamic constraints in metabolic network models, BMC Bioinformatics, 7, 512 (2006)
[24] Larhlimi, A.; David, L.; Selbig, J.; Bockmayr, A., F2c2: a fast tool for the computation of flux coupling in gen ome-scale metabolic networks, BMC Bioinformatics, 13, 57 (2012)
[25] Larhlimi, A.; Bockmayr, A., A new approach to flux coupling analysis of metabolic networks, Comput. Life Sci. II, 4216, 205-215 (2006)
[26] Lewis, N. E.; Nagarajan, H.; Palsson, B., Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods, Nat. Rev. Microbiol., 10, 4, 291-305 (2012)
[27] Mavrovouniotis, M. L., Duality theory for thermodynamic bottlenecks in bioreaction pathways, Chem. Eng. Sci., 51, 9, 1495-1507 (1996)
[28] Müller, A. C.; Bockmayr, A., Fast thermodynamically constrained flux variability analysis, Bioinformatics, 29, 7, 903-909 (2013)
[29] Müller, A., Thermodynamic constraints in metabolic networks (2012), Freie Universität Berlin, Fachbereich Mathematik und Informatik, Master’s thesis
[30] Nigam, R.; Liang, S., Algorithm for perturbing thermodynamically infeasible metabolic networks, Comput. Biol. Med., 37, 126-133 (2007)
[31] Noor, E.; Arren, B. E.; Flamholz, A.; Reznik, E.; Liebermeister, W.; Milo, R., Pathway thermodynamics highlights kinetics obstacles in central metabolism, PLoS Comput. Biol. (2014)
[32] Noor, E.; Lewis, N. E.; Milo, R., A proof for loop-law constraints in stoichiometric metabolic networks, BMC Syst. Biol., 6, 140 (2012)
[33] Notebaart, R. A.; Teusink, B.; Siezen, R. J.; Papp, B., Co-regulation of metabolic genes is better explained by flux coupling than by network distance, PLoS Comput. Biol., 4, 1, e26 (2008)
[34] Price, N. D.; Famili, I.; Beard, D. A.; Palsson, B. O., Extreme pathways and Kirchhoff’s second law, Biophys. J., 83, 2879-2882 (2002)
[35] Price, N. D.; Thiele, I.; Palsson, B. O., Candidate states of Helicopacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints, Biophys. J., 90, 3919-3928 (2006)
[36] Qian, H.; Beard, D. A., Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium, Biophys. Chem., 114, 213-220 (2005)
[38] Schellenberger, J.; Lewis, N. E.; Palsson, B.Ø., Elimination of thermodynamically infeasible loops in steady-state metabolic models, Biophys. J., 100, 544-553 (2011)
[39] Schellenberger, J.; Park, J. O.; Conrad, T. M.; Palsson, B. O., Bigg: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions, BMC Bioinformatics, 11, 213 (2010)
[40] Schuster, S.; Hilgetag, C., On elementary flux modes in biochemical systems at steady state, J. Biol. Syst., 2, 165-182 (1994)
[41] Soh, T.; Inoue, K., Identifying necessary reactions in metabolic pathways by minimal model generation, ECAI, 277-282 (2010) · Zbl 1211.92025
[42] Terzer, M., Large scale methods to enumerate extreme rays and elementary modes (2009), Swiss Federal Institute of Technology, Zurich, Ph.D. thesis
[43] Terzer, M.; Stelling, J., Large-scale computation of elementary flux modes with bit pattern trees, Bioinformatics, 24, 19, 2229-2235 (2008)
[44] Varma, A.; Palsson, B. O., Metabolic flux balancing: basic concepts, scientific and practical use, Nat. Biotechnol., 12, 994-998 (1994)
[45] von Kamp, A.; Schuster, S., Metatool 5.0: fast and flexible elementary modes analysis, Bioinformatics, 22, 15, 1930-1931 (2006)
[46] Wright, J.; Wagner, A., Exhaustive identification of steady state cycles in large stoichiometric networks, BMC Syst. Biol., 2, 61 (2008)
[48] Yang, F.; Beard, D. A., Thermodynamically based profiling of drug metabolism and drug-drug metabolic interactions: a case study of acetaminophen and ethanol toxic interaction, Biophys. Chem., 120, 121-134 (2006)
[49] Yang, F.; Qian, H.; Beard, D. A., Ab initio prediction of thermodynamically feasible reaction directions from biochemical network stoichiometry, Metab. Eng., 7, 251-259 (2005)
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