×

Weakly reversible single linkage class realizations of polynomial dynamical systems: an algorithmic perspective. (English) Zbl 07796566

Summary: Systems of differential equations with polynomial right-hand sides are very common in applications. In particular, when restricted to the positive orthant, they appear naturally (according to the law of mass-action kinetics) in ecology, population dynamics, as models of biochemical interaction networks, and models of the spread of infectious diseases. Their mathematical analysis is very challenging in general; in particular, it is very difficult to answer questions about the long-term dynamics of the variables (species) in the model, such as questions about persistence and extinction. Even if we restrict our attention to mass-action systems, these questions still remain challenging. On the other hand, if a polynomial dynamical system has a weakly reversible single linkage class \((WR^1)\) realization, then its long-term dynamics is known to be remarkably robust: all the variables are persistent (i.e., no species goes extinct), irrespective of the values of the parameters in the model. Here we describe an algorithm for finding \(WR^1\) realizations of polynomial dynamical systems, whenever such realizations exist.

MSC:

37N25 Dynamical systems in biology
34C07 Theory of limit cycles of polynomial and analytic vector fields (existence, uniqueness, bounds, Hilbert’s 16th problem and ramifications) for ordinary differential equations
92E20 Classical flows, reactions, etc. in chemistry
92D25 Population dynamics (general)
92D40 Ecology
92D30 Epidemiology
80A32 Chemically reacting flows

References:

[1] Ilyashenko, Y., Centennial history of Hilbert’s 16th problem, Bull. Am. Math. Soc., 39, 3, 301-355 (2002) · Zbl 1004.34017 · doi:10.1090/S0273-0979-02-00946-1
[2] Yu, P.; Craciun, G., Mathematical analysis of chemical reaction systems, Isr. J. Chem., 58, 6-7, 733-741 (2018) · doi:10.1002/ijch.201800003
[3] Craciun, G.; Dickenstein, A.; Shiu, A.; Sturmfels, B., Toric dynamical systems, J. Symb. Comput., 44, 11, 1551-1565 (2009) · Zbl 1188.37082 · doi:10.1016/j.jsc.2008.08.006
[4] Craciun, G.; Pantea, C., Identifiability of chemical reaction networks, J. Math. Chem., 44, 1, 244-259 (2008) · Zbl 1145.92040 · doi:10.1007/s10910-007-9307-x
[5] Pantea, C., On the persistence and global stability of mass-action systems, SIAM J. Math. Anal., 44, 3, 1636-1673 (2012) · Zbl 1316.37041 · doi:10.1137/110840509
[6] Gopalkrishnan, M.; Miller, E.; Shiu, A., A geometric approach to the global attractor conjecture, SIAM J. Appl. Dyn. Syst., 13, 2, 758-797 (2014) · Zbl 1301.34063 · doi:10.1137/130928170
[7] Craciun, G.; Jin, J.; Yu, P., An efficient characterization of complex-balanced, detailed-balanced, and weakly reversible systems, SIAM J. Appl. Math., 80, 1, 183-205 (2020) · Zbl 1436.37097 · doi:10.1137/19M1244494
[8] M. Feinberg, Lectures on chemical reaction networks. Notes of lectures given at the Mathematics Research Center, University of Wisconsin, p. 49 (1979)
[9] Feinberg, M., Foundations of Chemical Reaction Network Theory (2019), Berlin: Springer, Berlin · Zbl 1420.92001 · doi:10.1007/978-3-030-03858-8
[10] Boros, B.; Hofbauer, J., Permanence of weakly reversible mass-action systems with a single linkage class, SIAM J. Appl. Dyn. Syst., 19, 1, 352-365 (2020) · Zbl 1443.92110 · doi:10.1137/19M1248431
[11] Anderson, D., A proof of the global attractor conjecture in the single linkage class case, SIAM J. Appl. Math., 71, 4, 1487-1508 (2011) · Zbl 1227.92013 · doi:10.1137/11082631X
[12] Dukarić, M.; Errami, H.; Jerala, R.; Lebar, T.; Romanovski, VG; Tóth, J.; Weber, A., On three genetic repressilator topologies, React. Kinet. Mech. Catal., 126, 3-30 (2019) · doi:10.1007/s11144-018-1519-5
[13] G. Craciun, M. Johnston, G. Szederkényi, E. Tonello, J. Tóth, P. Yu, Realizations of kinetic differential equations. arXiv preprint arXiv:1907.07266 (2019) · Zbl 1470.92407
[14] Voit, E.; Martens, H.; Omholt, S., 150 years of the mass action law, PLoS Comput. Biol., 11, 1, e1004012 (2015) · doi:10.1371/journal.pcbi.1004012
[15] Guldberg, C.; Waage, P., Studies concerning affinity, CM Forhandlinger: Videnskabs-Selskabet I Christiana, 35, 1864, 1864 (1864)
[16] J. Gunawardena, Chemical reaction network theory for in-silico biologists. http://vcp.med.harvard.edu/papers/crnt.pdf (2003)
[17] L. Adleman, M. Gopalkrishnan, M. Huang, P. Moisset, D. Reishus, On the mathematics of the law of mass action, in A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations, pp. 3-46. Springer, Berlin (2014)
[18] Hárs, V.; Tóth, J., On the inverse problem of reaction kinetics, Qual. Theory Differ. Equ., 30, 363-379 (1981) · Zbl 0504.92029
[19] Horn, F.; Jackson, R., General mass action kinetics, Arch. Ration. Mech. Anal., 47, 2, 81-116 (1972) · doi:10.1007/BF00251225
[20] Feinberg, M.; Horn, F., Chemical mechanism structure and the coincidence of the stoichiometric and kinetic subspaces, Arch. Ration. Mech. Anal., 66, 1, 83-97 (1977) · Zbl 0384.70026 · doi:10.1007/BF00250853
[21] Feinberg, M., The existence and uniqueness of steady states for a class of chemical reaction networks, Arch. Ration. Mech. Anal., 132, 4, 311-370 (1995) · Zbl 0853.92024 · doi:10.1007/BF00375614
[22] Boros, B., Existence of positive steady states for weakly reversible mass-action systems, SIAM J. Math. Anal., 51, 1, 435-449 (2019) · Zbl 1411.34062 · doi:10.1137/17M115534X
[23] Deshpande, A.; Gopalkrishnan, M., Autocatalysis in reaction networks, Bull. Math. Biol., 76, 10, 2570-2595 (2014) · Zbl 1330.92139 · doi:10.1007/s11538-014-0024-x
[24] Hordijk, W.; Hein, J.; Steel, M., Autocatalytic sets and the origin of life, Entropy, 12, 7, 1733-1742 (2010) · doi:10.3390/e12071733
[25] Hordijk, W.; Steel, M., Detecting autocatalytic, self-sustaining sets in chemical reaction systems, J. Theor. Biol., 227, 4, 451-461 (2004) · Zbl 1439.92084 · doi:10.1016/j.jtbi.2003.11.020
[26] Hordijk, W.; Steel, M.; Kauffman, S., The structure of autocatalytic sets: Evolvability, enablement, and emergence, Acta biotheoretica, 60, 4, 379-392 (2012) · doi:10.1007/s10441-012-9165-1
[27] Craciun, G.; Deshpande, A.; Joshi, B.; Yu, P., Autocatalytic recombination systems: a reaction network perspective, Math. Biosci., 345, 108784 (2022) · Zbl 1486.92061 · doi:10.1016/j.mbs.2022.108784
[28] Johnston, M.; Siegel, D.; Szederkényi, G., Computing weakly reversible linearly conjugate chemical reaction networks with minimal deficiency, Math. Biosci., 241, 1, 88-98 (2013) · Zbl 1263.92058 · doi:10.1016/j.mbs.2012.09.008
[29] G. Szederkényi, G. Lipták, J. Rudan, K. Hangos, Optimization-based design of kinetic feedbacks for nonnegative polynomial systems, in 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 67-72. IEEE (2013)
[30] Rudan, J.; Szederkényi, G.; Hangos, K., Efficiently computing alternative structures of large biochemical reaction networks using linear programming, MATCH Commun. Math. Comput. Chem, 71, 71-92 (2014)
[31] G. Szederkényi, K. Hangos, Z. Tuza, Finding weakly reversible realizations of chemical reaction networks using optimization. arXiv preprintarXiv:1103.4741 (2011)
[32] G. Craciun, A. Deshpande, J. Jin, Weakly reversible realizations that obey the deficiency one theorem: an algorithmic perspective. In preparation (2023)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.