Research article Special Issues

Realizations of kinetic differential equations

  • Received: 17 July 2019 Accepted: 18 October 2019 Published: 06 November 2019
  • The induced kinetic differential equations of a reaction network endowed with mass action type kinetics is a system of polynomial differential equations. The problem studied here is: Given a system of polynomial differential equations, is it possible to find a network which induces these equations; in other words: is it possible to find a kinetic realization of this system of differential equations? If yes, can we find a network with some chemically relevant properties (implying also important dynamic consequences), such as reversibility, weak reversibility, zero deficiency, detailed balancing, complex balancing, mass conservation, etc.? The constructive answers presented to a series of questions of the above type are useful when fitting differential equations to datasets, or when trying to find out the dynamic behavior of the solutions of differential equations. It turns out that some of these results can be applied when trying to solve seemingly unrelated mathematical problems, like the existence of positive solutions to algebraic equations.

    Citation: Gheorghe Craciun, Matthew D. Johnston, Gábor Szederkényi, Elisa Tonello, János Tóth, Polly Y. Yu. Realizations of kinetic differential equations[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 862-892. doi: 10.3934/mbe.2020046

    Related Papers:

  • The induced kinetic differential equations of a reaction network endowed with mass action type kinetics is a system of polynomial differential equations. The problem studied here is: Given a system of polynomial differential equations, is it possible to find a network which induces these equations; in other words: is it possible to find a kinetic realization of this system of differential equations? If yes, can we find a network with some chemically relevant properties (implying also important dynamic consequences), such as reversibility, weak reversibility, zero deficiency, detailed balancing, complex balancing, mass conservation, etc.? The constructive answers presented to a series of questions of the above type are useful when fitting differential equations to datasets, or when trying to find out the dynamic behavior of the solutions of differential equations. It turns out that some of these results can be applied when trying to solve seemingly unrelated mathematical problems, like the existence of positive solutions to algebraic equations.


    加载中


    [1] V. Hárs and J. Tóth, On the inverse problem of reaction kinetics, In M. Farkas, editor, Colloquia Mathematica Societatis János Bolyai, volume 30, pages 363-379. Qualitative Theory of Differential Equations, 1979.
    [2] C. P. P. Arceo, E. C. Jose, A. Marin-Sanguino, et al., Chemical reaction network approaches to biochemical systems theory, Math. Biosci., 269 (2015), 135-152.
    [3] F. Horn and R. Jackson, General mass action kinetics, Arch. Ratl. Mech. Anal., 47 (1972), 81-116.
    [4] M. Feinberg, Complex balancing in general kinetic systems, Arch. Ratl. Mech. Anal., 49 (1972), 187-194.
    [5] F. Horn, Necessary and sufficient conditions for complex balancing in chemical kinetics, Arch. Ratl. Mech. Anal., 49 (1972), 172-186.
    [6] A. I. Volpert and S. I. Hudyaev, Analyses in Classes of Discontinuous Functions and Equations of Mathematical Physics, Martinus Nijhoff Publishers, Dordrecht, 1985. Russian original: 1975.
    [7] D. F. Anderson, A proof of the global attractor conjecture in the single linkage class case, SIAM J. Appl. Math., 71 (2011), 1487-1508.
    [8] C. Pantea, On the persistence and global stability of mass-action systems, SIAM J. Math. Anal., 44 (2012), 1636-1673.
    [9] M. Gopalkrshnan, E. Miller and A. Shiu, A geometric approach to the global attractor conjecture, SIAM J. Appl. Dyn. Syst., 13 (2014), 758-797.
    [10] G. Craciun, F. Nazarov and C. Pantea, Persistence and permanence of mass-action and power-law dynamical systems, SIAM J. Appl. Math., 73 (2013), 305-329.
    [11] G. Craciun, Toric differential inclusions and a proof of the global attractor conjecture, arXiv:1501.02860, 2016.
    [12] R. Aris, Prolegomena to the rational analysis of systems of chemical reactions, Archive Ration. Mech. An., 19 (1965), 81-99.
    [13] R. Aris, Mathematical aspects of chemical reaction, IEEC Fundamentals, 61 (1969), 17-29.
    [14] M. Dukarić, H. Errami, R. Jerala, et al., On three genetic repressilator topologies, React. Kinet. Mech. Cat., 126 (2019), 3-30.
    [15] D. Lichtblau, Symbolic analysis of multiple steady states in a MAPK chemical reaction network, J. Symb. Comp., 2018. submitted.
    [16] B. Boros, On the existence of positive steady states for weakly reversible mass-action systems, SIAM J. Math. Anal., 51 (2019), 435-449.
    [17] M. Feinberg, Foundations of Chemical Reaction Network Theory, Springer International Publishing, New York, 2019.
    [18] G. Craciun and P. Y. Yu, Mathematical analysis of chemical reaction systems, Isr. J. Chem., 50, 2018.
    [19] J. Tóth, A. L. Nagy and D. Papp, Reaction Kinetics: Exercises, Programs and Theorems, Mathematica for Deterministic and Stochastic Kinetics, Springer-Verlag, New York, 2018.
    [20] G. Lente, Deterministic kinetics in chemistry and systems biology: the dynamics of complex reaction networks, Springer, 2015.
    [21] M. Feinberg and F. J. M. Horn, Chemical mechanism structure and the coincidence of the stoichiometric and kinetic subspaces, Arch. Ratl. Mech. Anal., 66 (1977), 83-97.
    [22] G. Craciun and C. Pantea, Identifiability of chemical reaction networks, J. Math. Chem., 44 (2008), 244-259.
    [23] G. Craciun, J. Jin and P. Y. Yu, An efficient characterization of complex-balanced, detailedbalanced, and weakly reversible systems, SIAM J. Appl. Math., 2019. To appear.
    [24] P. Érdi and J. Tóth, Mathematical Models of Chemical Reactions. Theory and Applications of Deterministic and Stochastic models, Princeton University Press, Princeton, New Jersey, 1989.
    [25] G. Szederkényi, Comment on "identifiability of chemical reaction networks" by G. Craciun and C. Pantea, J. Math. Chem., 45 (2009), 1172-1174.
    [26] G. Szederkényi, Computing sparse and dense realizations of reaction kinetic systems, J. Math. Chem., 47 (2010), 551-568.
    [27] G. Szederkényi, K. M. Hangos and T. Péni, Maximal and minimal realizations of reaction kinetic systems: computation and properties, MATCH Commun. Math. Comput. Chem., 65 (2011), 309-332.
    [28] B. Ács, G. Szederkényi, Z. A. Tuza, et al., Computing linearly conjugate weakly reversible kinetic structures using optimization and graph theory, MATCH Commun. Math. Comput. Chem., 74 (2015), 489-512.
    [29] G. Ács, G. Szlobodnyik and G. Szederkényi, A computational approach to the structural analysis of uncertain kinetic systems, Comput. Physics Commun., 228 (2018), 83-95.
    [30] G. Szederkényi, A. Magyar and K. M. Hangos, Analysis and control of polynomial dynamic models with biological applications, Academic Press, London, San Diego, Cambridge, MA, Oxford, 2018.
    [31] J. Tóth, A formális reakciókinetika globális determinisztikus és sztochasztikus modelljéröl (On the global deterministic and stochastic models of formal reaction kinetics with applications), MTA SZTAKI Tanulmányok, 129 (1981), 1-166. In Hungarian.
    [32] G. Lipták, G. Szederkényi and K. M. Hangos, Computing zero deficiency realizations of kinetic systems, Syst. Control Lett., 81 (2015), 24-30.
    [33] G. Szederkényi and K. M. Hangos, Finding complex balanced and detailed balanced realizations of chemical reaction networks, J. Math. Chem., 49 (2011), 1163-1179.
    [34] M. Feinberg, Necessary and sufficient conditions for detailed balancing in mass action systems of arbitrary complexity, Chem. Eng. Sci., 44 (1989), 1819-1827.
    [35] V. N. Orlov and L. I. Rozonoer, The macrodynamics of open systems and the variational principle of the local potential II. Applications, J. Franklin Ins., 318 (1984), 315-347.
    [36] B. Joshi and A. Shiu, A survey of methods for deciding whether a reaction network is multistationary, Math. Model. Nat. Pheno., 10 (2015), 47-67.
    [37] G. Szederkényi, K. M. Hangos and Z. Tuza, Finding weakly reversible realizations of chemical reaction networks using optimization, MATCH Commun. Math. Comput. Chem., 67 (2012), 193-212.
    [38] M. D. Johnston, D. Siegel and G. Szederkényi, Computing weakly reversible linearly conjugate chemical reaction networks with minimal deficiency, Math. Biosci., 241 (2013), 88-98.
    [39] S. Schuster and R. Schuster, Detecting strictly detailed balanced subnetworks in open chemical reaction networks, J. Math. Chem., 6 (1991), 17-40.
    [40] M. D. Johnston, D. Siegel and G. Szederkényi, Dynamical equivalence and linear conjugacy of chemical reaction networks: new results and methods, MATCH Commun. Math. Comput. Chem., 68 (2012), 443-468.
    [41] M. D. Johnston, D. Siegel and G. Szederkényi, A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks, J. Math. Chem., 50 (2012), 274-288.
    [42] J. Rudan, G. Szederkényi, K. Hangos, et al., Polynomial time algorithms to determine weakly reversible realizations of chemical reaction networks, J. Math. Chem., 52 (2014), 1386-1404.
    [43] D. Csercsik, G. Szederkényi and K. M. Hangos, Parametric uniqueness of deficiency zero reaction networks, J. Math. Chem., 50 (2012), 1-8.
    [44] G. Craciun, J. Jin and P. Y. Yu, Uniqueness of kinetic realizations for weakly reversible deficiency zero networks, In preparation.
    [45] B. Boros and J. Hofbauer, Permanence of weakly reversible mass-action systems with a single linkage class, arXiv:1903.03071, 2019.
    [46] L. Cardelli, M. Tribastone and M. Tschaikowski, From electric circuits to chemical networks, arXiv:1812.03308, 2018.
    [47] D. Csercsik, G. Szederkényi and K. M. Hangos, Parametric uniqueness of deficiency zero reaction networks. J. Math. Chem., 50 (2012), 1-8.
    [48] J. Rudan, G. Szederkényi and K. M. Hangos, Efficient computation of alternative structures for large kinetic systems using linear programming, MATCH Commun. Math. Comput. Chem., 71 (2014), 71-92.
    [49] J. Rudan, G. Szederkényi, K. M. Hangos, et al., Polynomial time algorithms to determine weakly reversible realizations of chemical reaction networks, J. Math. Chem., (2014), 1-19.
    [50] G. Szederkényi, K. M. Hangos and D. Csercsik, Computing realizations of reaction kinetic networks with given properties, In A. N. Gorban and D. Roose, editors, Coping with Complexity: Model Reduction and Data Analysis, volume 75, pages 253-267. Springer, 2010.
    [51] J. Rudan, G. Szederkényi and K. M. Hangos, Computing dynamically equivalent realizations of biochemical reaction networks with mass conservation, In ICNAAM 2013: 11th International Conference of Numerical Analysis and Applied Mathematics, 21-27 September, Rhodes, Greece, AIP Conference Proceedings, volume 1558, pages 2356-2359, 2013. ISBN: 978-0-7354-1184-5.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4682) PDF downloads(599) Cited by(12)

Article outline

Figures and Tables

Figures(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog