Metabolic pathway analysis in presence of biological constraints
2020
https://doi.org/10.1101/2020.06.27.175455Abstract
Metabolic pathway analysis is a key method to study metabolism at steady state and the elementary fluxes (EFs) is one major concept allowing one to analyze the network in terms of non-decomposable pathways. The supports of the EFs contain in particular the supports of the elementary flux modes (EFMs), which are the support-minimal pathways, and EFs coincide with EFMs when the only flux constraints are given by the irreversibility of certain reactions. Practical use of both EFMs and EFs has been hampered by the combinatorial explosion of their number in large, genome-scale, systems. The EFs give the possible pathways at steady state but the real pathways are limited by biological constraints, such as thermodynamic or, more generally, kinetic constraints and regulatory constraints from the genetic network. We provide results about the mathematical structure and geometrical characterization of the solutions space in presence of such biological constraints and revisit the concept of EFM...
References (47)
- V. Acuña, F. Chierichetti, V. Lacroix, A. Marchetti-Spaccamela, M.-F. Sagot, and L. Stougie. Modes and cuts in metabolic networks: complexity and algorithms. BioSystems, 95:51-60, 2009.
- V. Acuña, A. Marchetti-Spaccamela, M.-F. Sagot, and L. Stougie. A note on the complexity of finding and enumerating elementary modes. BioSystems, 99(3):210-214, 2010.
- P. Atkins and J. de Paula. Physical Chemistry. Freeman, tenth edition, 2014.
- K. Ballerstein, A. von Kamp, S. Klamt, and U. Haus. Minimal cut sets in a metabolic network are elementary modes in a dual network. Bioinformatics, 28(3):381-387, 2012.
- A. P. Burgard, E. V. Nikolaev, C. H. Schilling, and C. D. Maranas. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome research, 14:301-312, 2004.
- B. L. Clarke. Stoichiometry network analysis. Cell Biophys., 12:237-253, 1988.
- L. F. de Figueiredo, A. Podhorski, A. Rubio, C. Kaleta, J. E. Beasley, S. Schuster, and F. J. Planes. Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics, 25(23):3158-3165, 2009.
- K. Fukuda and A. Prodon. Double description method revisited. In M. Deza, R. Euler, and I. Manoussakis, editors, Combinatorics and Computer Science, volume 1120 of Lecture Notes in Computer Science, pages 91-111. Springer, 1996.
- J. Gagneur and S. Klamt. Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics, 5(175), 2004.
- M. P. Gerstl, C. Jungreuthmayer, S. Muller, and J. Zanghellini. Which sets of elementary flux modes form thermodynamically feasible flux distributions? FEBS Journal, 283:1782-1794, 2016.
- M. P. Gerstl, C. Jungreuthmayer, and J. Zanghellini. tEFMA: computing thermodynamically fea- sible elementary flux modes in metabolic networks. Bioinformatics, 31(13):2232-2234, 2015.
- M. P. Gerstl, S. Müller, G. Regensburger, and J. Zanghellini. Flux tope analysis: studying the coordination of reaction directions in metabolic networks. Bioinformatics, 35(2):266-273, 2019.
- M. P. Gerstl, D. E. Ruckerbauer, D. Mattanovich, C. Jungreuthmayer, and J. Zanghellini. Metabolomics integrated elementary flux mode analysis in large metabolic networks. Scientific Reports, 8930(5), 2015.
- S. Gudmundsson and I. Thiele. Computationally efficient flux variability analysis. BMC Bioinfor- matics, 11(1):489, 2010.
- U.-U. Haus, S. Klamt, and T. Stephen. Computing knock-out strategies in metabolic networks. Journal of Computational Biology, 15(3):259-268, 2008.
- C. S. Henry, L. J. Broadbelt, and V. Hatzimanikatis. Thermodynamics-based metabolic flux analysis. Biophysical journal, 92(5):1792-1805, 2007.
- D. Jevremovic, C. T. Trinh, F. Srienc, and D. Boley. On algebraic properties of extreme pathways in metabolic networks. Journal of Computational Biology, 17(2):107-119, 2010.
- C. Jungreuthmayer, G. Nair, S. Klamt, and J. Zanghellini. Comparison and improvement of algo- rithms for computing minimal cut sets. BMC Bioinformatics, 14:318, 2013.
- C. Jungreuthmayer, D. E. Ruckerbauer, M. P. Gerstl, M. Hanscho, and J. Zanghellini. Avoiding the enumeration of infeasible elementary flux modes by including transcriptional regulatory rules in the enumeration process saves computational costs. PLoS ONE, 10(6):e0129840, 2015.
- C. Jungreuthmayer, D. E. Ruckerbauer, and J. Zanghellini. Utilizing gene regulatory information to speed up the calculation of elementary flux modes. arXiv:1208.1853 [q-bio.MN], Aug. 2012.
- C. Jungreuthmayer, D. E. Ruckerbauer, and J. Zanghellini. regEfmtool : speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic. BioSystems, 113(1):37-39, 2013.
- S. Klamt, G. Regensburger, M. P. Gerstl, C. Jungreuthmayer, S. Schuster, R. Mahadevan, J. Zanghellini, and S. Müller. From elementary flux modes to elementary flux vectors: metabolic pathway analysis with arbitrary linear flux constraints. PLoS Computational Biology, 13:e1005409, Apr. 2017.
- A. Larhlimi, L. David, J. Selbig, and A. Bockmayr. F2c2: a fast tool for the computation of flux coupling in genome-scale metabolic networks. BMC bioinformatics, 13(57), 2012.
- W. Liebermeister, J. Uhlendorf, and E. Klipp. Modular rate laws for enzymatic reactions: thermo- dynamics, elasticities and implementation. Bioinformatics, 26(12):1528-1534, 2010.
- F. Llaneras and J. Picó. Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators. Journal of Biomedicine and Biotechnology, 2010:1-13, 2010.
- P. McMullen. The maximum numbers of faces of a convex polytope. Mathematika, 17(2):179-184, 1970.
- M. Morterol, P. Dague, S. Peres, and L. Simon. Minimality of metabolic flux modes under Boolean regulation constraints. In 12th International Workshop on Constraint-Based Methods for Bioinfor- matics (WCB'16), Toulouse, September 2016.
- T. S. Motzkin, H. Raiffa, G. L. Thompson, and R. M. Thrall. The double description method. In H. W. Kuhn and A. W. Tucker, editors, Contributions to the theory of games II, Annals of Math. Studies, volume 28. Princeton University Press, 1953.
- S. Müller and G. Regensburger. Elementary vectors and conformal sums in polyhedral geometry and their relevance for metabolic pathway analysis. Frontiers in Genetics, 7(90), 2016.
- S. Müller, G. Regensburger, and R. Steuer. Enzyme allocation problems in kinetic metabolic net- works: optimal solutions are elementary flux modes. Journal of Theoretical Biology, 347:182-190, 2014.
- E. Noor, A. Flamholz, W. Liebermeister, A. Bar-Even, and R. Milo. A note on the kinetics of enzyme action: a decomposition that highlights thermodynamic effects. FEBS Letters, 587:2772-2777, 2013.
- S. Peres, M. Jolicoeur, C. Moulin, P. Dague, and S. Schuster. How important is thermodynamics for identifying elementary flux modes? PLoS ONE, 12(2):e0171440, 2017.
- S. Peres, S. Schuster, and P. Dague. Thermodynamic constraints for identifying the elementary flux modes. Biochemical Society Transactions, 46(3):641-647, 2018.
- R. T. Rockafellar. The elementary vectors of a subspace of R N . In Combinatorial Mathematics and its Applications (Proc. Conf., Univ. North Carolina, Chapel Hill, N.C., 1967), pages 104-127. Univ. Carolina Press, Chapel Hill, N.C., 1969.
- A. Röhl, Y. Goldstein, and A. Bockmayr. EFM-Recorder -Faster elementary mode enumeration via reaction coupling order. In Advances in Systems and Synthetic Biology, pages 91-99, Strasbourg, March 2015.
- C. H. Schilling, D. Letscher, and B. O. Palsson. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. Journal of Theoretical Biology, 203:229-248, 2000.
- C. H. Schilling and B. O. Palsson. The underlying pathway structure of biochemical reaction networks. Proceedings of the National Academy of Sciences USA, 95:4193-4198, 1998.
- S. Schuster and C. Hilgetag. On elementary flux modes in biochemical reaction systems at steady state. Journal of Biological Systems, 2(2):165-182, 1994.
- M. Terzer and J. Stelling. Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24(19):2229-2235, 2008.
- C. T. Trinh, A. Wlaschin, and F. Srienc. Elementary mode analysis: a useful metabolic path- analysis tool for characterizing cellular metabolism. Applied Microbiology and Biotechnology, 81(5):813-826, 2009.
- R. Urbanczik and C. Wagner. An improved algorithm for stoichiometric network analysis: theory and applications. Bioinformatics, 21(7):1203-1210, 2005.
- J. B. van Klinken and K. Willems van Dijk. FluxModeCalculator: an efficient tool for large-scale flux mode computation. Bioinformatics, 32(8):1265-1266, 2016.
- A. von Kamp and S. Klamt. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Computational Biology, 10(1):e1003378, 2014.
- C. Wagner. Nullspace approach to determine the elementary modes of chemical reaction systems. J. Phys. Chem. B, 108(7):2425-2431, 2004.
- C. Wagner and R. Urbanczik. The geometry of the flux cone of a metabolic network. Biophysical Journal, 89(6):3837-3845, 2005.
- M. T. Wortel, H. Peters, J. Hulshof, B. Teusink, and F. J. Bruggeman. Metabolic states with maximal specific rate carry flux through an elementary flux mode. FEBS Journal, 281:1547-1555, 2014.
- J. Zanghellini, D. E. Ruckerbauer, M. Hanscho, and C. Jungreuthmayer. Elementary flux modes in a nutshell: properties, calculation and applications. Biotechnology Journal, 8(9):1009-1016, 2013.