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A Nash Equilibrium Approach to Metabolic Network Analysis

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Machine Learning, Optimization, and Big Data (MOD 2016)

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Abstract

A novel approach to metabolic network analysis using a Nash Equilibrium formulation is proposed. Enzymes are considered to be players in a multi-player game in which each player attempts to minimize the dimensionless Gibbs free energy associated with the biochemical reaction(s) it catalyzes subject to elemental mass balances. Mathematical formulation of the metabolic network as a set of nonlinear programming (NLP) sub-problems and appropriate solution methodologies are described. A small example representing part of the production cycle for acetyl-CoA is used to demonstrate the efficacy of the proposed Nash Equilibrium framework and show that it represents a paradigm shift in metabolic network analysis.

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Abbreviations

c :

objective function coefficients in any LP formulation

C :

number of chemical species

f :

objective function

g :

gradient

G :

Gibbs free energy

H :

enthalpy, Hessian matrix

L :

level set value

n :

dimension of space

\(n_p\) :

number of products

\(n_r\) :

number of reactants

N :

number of sub-problems

R :

gas constant, number of reactions

\(\mathfrak {R}^n\) :

real space of dimension n

s :

stoichiometric numbers

S :

stoichiometric coefficients

T :

temperature

x :

mole fraction

Z :

unknown variables

v :

unknown fluxes

\(\phi \) :

fugacity coefficient

f :

formation

i :

component index

j :

sub-problem or node index

\(-j\) :

excluding j

0:

reference state

R :

reaction

L :

lower bound

U :

upper bound

\(*\) :

optimal value

References

  1. Varma, A., Palsson, B.O.: Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotechnol. 12, 994–998 (1994)

    Article  Google Scholar 

  2. Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491–496 (2003)

    Article  Google Scholar 

  3. Holzhutter, H.G.: The principles of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922 (2004)

    Article  Google Scholar 

  4. Julius, A.A., Imielinski, M., Pappas, G.J.: Metabolic networks analysis using convex optimization. In: Proceedings of the 47th IEEE Conference on Decision and Control, p. 762 (2008)

    Google Scholar 

  5. Smallbone, K., Simeonidis, E.: Flux balance analysis: a geometric perspective. J. Theor. Biol. 258, 311–315 (2009)

    Article  MathSciNet  Google Scholar 

  6. Murabito, E., Simeonidis, E., Smallbone, K., Swinton, J.: Capturing the essence of a metabolic network: a flux balance analysis approach. J. Theor. Biol. 260(3), 445–452 (2009)

    Article  MathSciNet  Google Scholar 

  7. Lee, S., Phalakornkule, C., Domach, M.M., Grossmann, I.E.: Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput. Chem. Eng. 24, 711–716 (2000)

    Article  Google Scholar 

  8. Henry, C.S., Broadbelt, L.J., Hatzimanikatis, V.: Thermodynamic metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007)

    Article  Google Scholar 

  9. Mahadevan, R., Edwards, J.S., Doyle, F.J.: Dynamic flux balance analysis in diauxic growth in Escherichia coli. Biophys. J. 83, 1331–1340 (2002)

    Article  Google Scholar 

  10. Patane, A., Santoro, A., Costanza, J., Nicosia, G.: Pareto optimal design for synthetic biology. IEEE Trans. Biomed. Circuits Syst. 9(4), 555–571 (2015)

    Article  Google Scholar 

  11. Angione, C., Costanza, J., Carapezza, G., Lio, P., Nicosia, G.: Multi-target analysis and design of mitochondrial metabolism. PLOS One 9, 1–22 (2015)

    MATH  Google Scholar 

  12. Alberty, R.A.: Thermodynamics of Biochemical Reactions. Wiley, Hoboken (2003)

    Book  Google Scholar 

  13. Elliott, J.R., Lira, C.T.: Introductory Chemical Engineering Thermodynamics, 2nd edn. Prentice Hall, Upper Saddle (2012)

    Google Scholar 

  14. Kummel, A., Panke, S., Heinemann, M.: Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinform. 7, 512–523 (2006)

    Article  Google Scholar 

  15. Flamholz, A., Noor, E., Bar-Even, A., Milo, R.: eQuilibrator - the biochemical thermodynamics calculator. Nucleic Acids Res. 40 (2011). doi:10.1093/nar/gkr874

    Google Scholar 

  16. Yuan, Y.: A trust region algorithm for nash equilibrium problems. Pac. J. Optim. 7, 125–138 (2011)

    MathSciNet  MATH  Google Scholar 

  17. Facchinei, F., Kanzow, C.: Generalized nash equilibrium problems. Ann. Oper. Res. 175, 177–211 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. von Heusinger, A.: Numerical methods for the solution of generalized nash equilibrium problems. Ph.D. thesis, Universitat Wurzburg, Wurzburg, Germany (2009)

    Google Scholar 

  19. Lucia, A., Feng, Y.: Global terrain methods. Comput. Chem. Eng. 26, 529–546 (2002)

    Article  Google Scholar 

  20. Lucia, A., Feng, Y.: Multivariable terrain methods. AIChE J. 49, 2553–2563 (2003)

    Article  Google Scholar 

  21. Lucia, A., DiMaggio, P.A., Depa, P.: A geometric terrain methodology for global optimization. J. Global Optim. 29, 297–314 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  22. Orth, J.D., Conrad, T.M., Na, J., Lerman, J.A., Nam, H., Feist, A.M., Palsson, B.O.: A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 11(7), 535 (2011). doi:10.1038/msb.2011.65

    Google Scholar 

  23. Zhang, Z., Shen, T., Rui, B., Zhou, W., Zhou, X., Shang, C., Xin, C., Liu, X., Li, G., Jiang, J., Li, C., Li, R., Han, M., You, S., Yu, G., Yi, Y., Wen, H., Liu, Z., Xie, X.: CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics. Nucleic Acids Res. 43 (2015). doi:10.1093/nar/gku1137

    Google Scholar 

  24. Holms, H.: Flux analysis and control of the central metabolic pathways in Escherichia coli. FEMS Microbiol. Rev. 19, 85–116 (1996)

    Article  Google Scholar 

  25. Klein, A.H., Shulla, A., Reimann, S.A., Keating, D.H., Wolfe, A.J.: The intracellular concentration of acetyl phosphate in Escherichia coli is sufficient for direct phosphorylation of two-component response regulators. J. Bacteriol. 189(15), 5574–5581 (2007)

    Article  Google Scholar 

  26. King, Z.A., Lu, J.S., Drager, A., Miller, P.C., Federowicz, S., Lerman, J.A., Ebrahim, A., Palsson, B.O., Lewis, N.E.: BiGG models: a platform for integrating, standardizing, and sharing genome-scale models. Nucleic Acids Res. (2015). doi:10.1093/nar/gkv1049

    Google Scholar 

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Correspondence to Angelo Lucia .

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Appendix

Appendix

1.1 Biochemical Reactions for a Simplified Metabolic Network for Acetyl CoA Production

The biochemical reactions involved at each node in the metabolic network are as follows:

  1. 1.

    Acetyl CoA production from pyruvate and acetate

    $$\begin{aligned} C_3 H_3 O_3 \; + \; C_2 H_3 O_2 \; + \; 2 C_{21} H_{32} N_7 O_{16} P_3 S \; \rightleftharpoons \; 2 C_{23} H_{34} N_7 O_{17} P_3 S \; + \; C O_2 \; + \; H_2 O \end{aligned}$$
    (20)

    Pyruvate \(\rightarrow \) acetyl CoA: pyruvate dehydrogenase (genes: lpd, aceE, and aceF) [26]

    Acetate \(\rightarrow \) acetyl CoA: acetyl CoA synthetase (genes: acs) [26]

  2. 2.

    Acetyl phosphate production from acetyl CoA

    $$\begin{aligned} C_{23} H_{34} N_7 O_{17} P_3 S \; + \; H O_4 P \; \rightleftharpoons \; C_2 H_3 O_5 P \; + \; C_{21} H_{32} N_7 O_{16} P_3 S \end{aligned}$$
    (21)

    Acetyl CoA \(\leftrightarrow \) acetyl phosphate: phosphotransacetylase (genes: pta or eutD) [26]

  3. 3.

    Acetate production from acetyl phosphate

    $$\begin{aligned} C_2 H_3 O_5 P \; \rightleftharpoons \; C_2 H_3 O_2^{-} \; + \; P O_3^{3-} \end{aligned}$$
    (22)

    Acetyl phosphate \(\leftrightarrow \) acetate: acetate kinase (genes: ackA, tdcD, or purT) [26]

Table 4. Unknown fluxes, components and Gibbs energy of formation.

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Lucia, A., DiMaggio, P.A. (2016). A Nash Equilibrium Approach to Metabolic Network Analysis. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_4

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