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Size-Independent Differences between the Mean of Discrete Stochastic Systems and the Corresponding Continuous Deterministic Systems

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Abstract

In this paper, it is shown that for a class of reaction networks, the discrete stochastic nature of the reacting species and reactions results in qualitative and quantitative differences between the mean of exact stochastic simulations and the prediction of the corresponding deterministic system. The differences are independent of the number of molecules of each species in the system under consideration. These reaction networks are open systems of chemical reactions with no zero-order reaction rates. They are characterized by at least two stationary points, one of which is a nonzero stable point, and one unstable trivial solution (stability based on a linear stability analysis of the deterministic system). Starting from a nonzero initial condition, the deterministic system never reaches the zero stationary point due to its unstable nature. In contrast, the result presented here proves that this zero-state is a stable stationary state for the discrete stochastic system, and other finite states have zero probability of existence at large times. This result generalizes previous theoretical studies and simulations of specific systems and provides a theoretical basis for analyzing a class of systems that exhibit such inconsistent behavior. This result has implications in the simulation of infection, apoptosis, and population kinetics, as it can be shown that for certain models the stochastic simulations will always yield different predictions for the mean behavior than the deterministic simulations.

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References

  • Artyomov, M.N., , 2007. Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities. Proc. Nat. Acad. Sci. 104(48), 18958–18963.

    Article  Google Scholar 

  • Brandt, H., Sigmund, K., 2006. The good, the bad and the discriminator–errors in direct and indirect reciprocity. J. Theor. Biol. 239(2), 183–194.

    Article  MathSciNet  Google Scholar 

  • Butler, D., 2007. The petaflop challenge. Nature 448(7149), 6–7.

    Article  Google Scholar 

  • Ding, M., Wille, L.T., 1993. Statistical properties of spatiotemporal dynamical systems. Phys. Rev. E 48(3), R1605.

    Article  Google Scholar 

  • Erdi, P., Toth, J., 1989. Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models. Manchester University Press, Manchester.

    MATH  Google Scholar 

  • Feller, W., 1968. An Introduction to Probability Theory and Its Applications (v. 1). Wiley, New York.

    Google Scholar 

  • Forger, D.B., Peskin, C.S., 2005. Stochastic simulation of the mammalian circadian clock. Proc. Nat. Acad. Sci. U.S.A. 102(2), 321–324.

    Article  Google Scholar 

  • Gadgil, C., , 2005. A stochastic analysis of first-order reaction networks. Bull. Math. Biol. 67(5), 901–946.

    Article  MathSciNet  Google Scholar 

  • Gomez-Uribe, C.A., Verghese, G.C., 2007. Mass fluctuation kinetics: Capturing stochastic effects in systems of chemical reactions through coupled mean-variance computations. J. Chem. Phys. 126(2), 024109.

    Article  Google Scholar 

  • Goutsias, J., 2007. Classical versus stochastic kinetics modeling of biochemical reaction systems. Biophys. J. 92(7), 2350–2365.

    Article  Google Scholar 

  • Haseltine, E.L., Rawlings, J.B., 2002. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics. J. Chem. Phys. 117(15), 6959–6969.

    Article  Google Scholar 

  • Imhof, L.A., , 2005. From the cover: Evolutionary cycles of cooperation and defection. Proc. Natl. Acad. Sci. 102(31), 10797–10800.

    Article  Google Scholar 

  • Jampa, M.P.K., , 2007. Synchronization in a network of model neurons. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. 75(2), 026215–026310.

    MathSciNet  Google Scholar 

  • Kessler, D.A., Levine, H., 1998. Fluctuation-induced diffusive instabilities. Nature 394(6693), 556–558.

    Article  Google Scholar 

  • Kurtz, T.G., 1972. The relationship between stochastic and deterministic models for chemical reactions. J. Chem. Phys. 57(7), 2976–2978.

    Article  Google Scholar 

  • Leonard, D., Reichl, L.E., 1990. Stochastic analysis of a driven chemical reaction. J. Chem. Phys. 92(10), 6004–6010.

    Article  Google Scholar 

  • Mao, X., , 2002. Environmental Brownian noise suppresses explosions in population dynamics. Stoch. Process. Appl. 97, 95–110.

    Article  MATH  Google Scholar 

  • McKane, A.J., Newman, T.J., 2004. Stochastic models in population biology and their deterministic analogs. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. 70(4), 041902.

    MathSciNet  Google Scholar 

  • Mcquarrie, D.A., , 1964. Kinetics of small systems II. J. Chem. Phys. 40, 2914–2921.

    Article  MathSciNet  Google Scholar 

  • Nasell, I., 1999. On the quasi-stationary distribution of the stochastic logistic epidemic. Math. Biosci. 156(1–2), 21–40.

    Article  MATH  MathSciNet  Google Scholar 

  • Nasell, I., 2001. Extinction and quasi-stationarity in the Verhulst logistic model. J. Theor. Biol. 211(1), 11–27.

    Article  Google Scholar 

  • Nicolis, G., Prigogine, I., 1971. Fluctuations in nonequilibrium systems. Proc. Nat. Acad. Sci. 68(9), 2102–2107.

    Article  MATH  MathSciNet  Google Scholar 

  • Qian, H., , 2002. Concentration fluctuations in a mesoscopic oscillating chemical reaction system. Proc. Nat. Acad. Sci. 99(16), 10376–10381.

    Article  MATH  Google Scholar 

  • Rao, C.V., Arkin, A.P., 2003. Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm. J. Chem. Phys. 118(11), 4999–5010.

    Article  Google Scholar 

  • Reddy, V.T.N., 1975. On the existence of the steady state in the stochastic Volterra-Lotka model. J. Stat. Phys. 13(1), 61–64.

    Article  Google Scholar 

  • Samoilov, M.S., Arkin, A.P., 2006. Deviant effects in molecular reaction pathways. Nat. Biotechnol. 24(10), 1235–1240.

    Article  Google Scholar 

  • Samoilov, M., , 2005. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc. Natl. Acad. Sci. U.S.A. 102(7), 2310–2315.

    Article  Google Scholar 

  • Sinha, S., 1992. Noisy uncoupled chaotic map ensembles violate the law of large numbers. Phys. Rev. Lett. 69(23), 3306.

    Article  MATH  MathSciNet  Google Scholar 

  • Srivastava, R., , 2002. Stochastic vs. deterministic modeling of intracellular viral kinetics. J. Theor. Biol. 218(3), 309–321.

    Article  Google Scholar 

  • Thakur, A.K., , 1978. Stochastic theory of second-order chemical reactions. J. Phys. Chem. 82(5), 552–558.

    Article  MathSciNet  Google Scholar 

  • Turner, T.E., , 2004. Stochastic approaches for modelling in vivo reactions. Comput. Biol. Chem. 28(3), 165–178.

    Article  MATH  Google Scholar 

  • Van Den Broeck, C., , 1994. Noise-induced nonequilibrium phase transition. Phys. Rev. Lett. 73(25), 3395.

    Article  Google Scholar 

  • Vellela, M., Qian, H., 2007. A quasistationary analysis of a stochastic chemical reaction: Keizer’s paradox. Bull. Math. Biol. 69(5), 1727–1746.

    Article  MATH  MathSciNet  Google Scholar 

  • Wolkenhauer, O., , 2004. Modeling and simulation of intracellular dynamics: Choosing an appropriate framework. IEEE Trans. Nanobiosci. 3(3), 200–207.

    Article  Google Scholar 

  • Zheng, Q., Ross, J., 1991. Comparison of deterministic and stochastic kinetics for nonlinear systems. J. Chem. Phys. 94(5), 3644–3648.

    Article  Google Scholar 

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Correspondence to Chetan J. Gadgil.

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Gadgil, C.J. Size-Independent Differences between the Mean of Discrete Stochastic Systems and the Corresponding Continuous Deterministic Systems. Bull. Math. Biol. 71, 1599–1611 (2009). https://doi.org/10.1007/s11538-009-9415-9

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  • DOI: https://doi.org/10.1007/s11538-009-9415-9

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