Advertisement

On the Explicit Use of Enzyme-Substrate Reactions in Metabolic Pathway Analysis

  • Angelo LuciaEmail author
  • Edward Thomas
  • Peter A. DiMaggio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

Flux balance (or constraint-based) analysis has been the mainstay for understanding metabolic networks for many years. However, recently Lucia and DiMaggio [1] have argued that metabolic networks are more correctly modeled using game theory, specifically Nash Equilibrium, because it (1) captures the natural competition between enzymes, (2) includes rigorous chemical reaction equilibrium thermodynamics, (3) incorporates element mass balance constraints, and therefore charge balancing, in a natural way, and (4) allows regulatory constraints to be included as additional constraints.

The novel aspects of this work center on the explicit inclusion of enzyme-substrate reactions at the cellular length scale and molecular length scale protein docking information in metabolic network modeling. This multi-scale information offers the advantages of directly (1) computing cellular enzyme concentrations and activities, (2) incorporating genetic modification of enzymes, and (3) encoding the effects of age-related changes in enzymatic behavior (e.g., protein misfolding) within any pathway. Molecular length scale binding histograms are computed using protein-ligand docking and directly up-scaled to the cellular level. A small, proof-of-concept example from the Krebs cycle is presented to illustrate key ideas. Numerical results show that the proposed approach provides a wealth of quantitative enzyme information.

References

  1. 1.
    Lucia, A., DiMaggio, P.A.: A Nash equilibrium approach to metabolic network analysis. In: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (eds.) MOD 2016. LNCS, vol. 10122, pp. 45–58. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-51469-7_4CrossRefGoogle Scholar
  2. 2.
    Varma, A., Palsson, B.O.: Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotechnol. 12, 994–998 (1994)CrossRefGoogle Scholar
  3. 3.
    Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491–496 (2003)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Smallbone, K., Simeonidis, E.: Flux balance analysis: a geometric perspective. J. Theor. Biol. 258, 311–315 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    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)MathSciNetCrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Henry, C.S., Broadbelt, L.J., Hatzimanikatis, V.: Thermodynamic metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007)CrossRefGoogle Scholar
  10. 10.
    Mahadevan, R., Edwards, J.S., Doyle, F.J.: Dynamic flux balance analysis in diauxic growth in Escherichia coli. Biophys. J. 83, 1331–1340 (2002)CrossRefGoogle Scholar
  11. 11.
    Patane, A., Santoro, A., Costanza, J., Nicosia, G.: Pareto optimal design for synthetic biology. IEEE Trans. Biomed. Circuits Syst. 9(4), 555–571 (2015)CrossRefGoogle Scholar
  12. 12.
    Angione, C., Costanza, J., Carapezza, G., Lio, P., Nicosia, G.: Multi-target analysis and design of mitochondrial metabolism. PLoS One 9, 1–22 (2015)zbMATHGoogle Scholar
  13. 13.
    Lucia, A., DiMaggio, P.A., Alonso-Martinez, D.: Metabolic pathway analysis using Nash equilibrium. J. Optim. (2017, in press)Google Scholar
  14. 14.
    Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 16, 2785–2791 (2009)CrossRefGoogle Scholar
  15. 15.
    Milo, R.: What is the total number of protein molecules per cell volume? A call to re-think some published values. BioEssays 35, 1050–1055 (2013)CrossRefGoogle Scholar
  16. 16.
    Schomburg, I., Hofmann, O., Bänsch, C., Chang, A., Schomburg, D.: Enzyme data and metabolic information: BRENDA, a resource for research in biology, biochemistry, and medicine. Gene Funct Dis. 3, 109–118 (2000)CrossRefGoogle Scholar
  17. 17.
    Villafranca, J.J., Mildvan, A.S.: The mechanism of aconitase action: I. Preparation, physical properties of the enzyme, and activation by iron (II). J. Biol. Chem. 246, 772–779 (1971)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Angelo Lucia
    • 1
    Email author
  • Edward Thomas
    • 1
  • Peter A. DiMaggio
    • 2
  1. 1.Department of Chemical EngineeringUniversity of Rhode IslandKingstonUSA
  2. 2.Department of Chemical EngineeringImperial College LondonLondonUK

Personalised recommendations