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Bistable stochastic biochemical networks: large chemical networks and systems with many molecules. (English) Zbl 1320.92040

Summary: In this paper we continue the program started in [the authors, ibid. 51, No. 5, 1343–1375 (2013; Zbl 1320.92043)]. We describe some chemical systems exhibiting bistable behavior with reaction constants of order one, but where bistability is due to the presence of a large number of chemical species or a large number of molecules of some of the species. We derive generalizations of the classical Kramers’ formula that gives the switching times for some particular systems exhibiting a large number of species.

MSC:

92C40 Biochemistry, molecular biology
92C42 Systems biology, networks

Citations:

Zbl 1320.92043
Full Text: DOI

References:

[1] D.F. Anderson, Stochastic perturbations of biochemical reaction systems. PhD Thesis (Duke University, 2005)
[2] D.F. Anderson, J.C. Mattingly, Propagation of fluctuations in biochemical systems, II: nonlinear chains. IET Syst. Biol. 1(6), 313-325 (2007) · doi:10.1049/iet-syb:20060063
[3] D.F. Anderson, J.C. Mattinglya, H.F. Nijhoutb, M.C. Reeda, Propagation of fluctuations in biochemical systems, I: linear SSC networks. Bull. Math. Biol. 69, 1791-1813 (2007) · Zbl 1298.92040 · doi:10.1007/s11538-007-9192-2
[4] D.F. Anderson, G. Craciun, T.G. Kurtz, Product-form stationary distributions for deficiency zero chemical reaction networks. Bull. Math. Biol. (2010). doi:10.1007/s11538-010-9517-4 · Zbl 1201.92069
[5] A. Arkin, J. Ross, H.H. McAdams, Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected \[Escherichia\] Escherichia \[coli\] coli cells. Genetics 149(4), 1633-1648 (1998)
[6] K. Ball, T.G. Kurtz, L. Popovic, G. Rempala, Asymptotic analysis of multiscale approximations to reaction networks. Ann. Appl. Prob. 16(4), 1925-1961 (2006) · Zbl 1118.92031 · doi:10.1214/105051606000000420
[7] W.J. Blake, M. Kaern, C.R. Cantor, J.J. Collins, Noise in eukaryotic gene expression. Nature 422(6932), 633-637 (2003) · doi:10.1038/nature01546
[8] A. Bovier, M. Eckhoff, V. Gayrard, M. Klein, Metastability in stochastic dynamics of disordered mean-field models. Probab. Theor. Rel. Fields 119, 99-161 (2001) · Zbl 1012.82015 · doi:10.1007/PL00012740
[9] A. Bovier, M. Eckhoff, V. Gayrard, M. Klein, Metastability and low lying spectra in reversible Markov chains. Commun. Math. Phys. 228, 219-255 (2002) · Zbl 1010.60088 · doi:10.1007/s002200200609
[10] A. Bovier, M. Eckhoff, V. Gayrard, M. Klein, Metastability in reversible diffusion processes I: sharp estimates for capacities and exit times. J. Eur. Math. Soc. 6, 399-424 (2004) · Zbl 1076.82045 · doi:10.4171/JEMS/14
[11] A. Bovier, M. Eckhoff, V. Gayrard, M. Klein, Metastability in reversible diffusion processes II: precise estimates for small eigenvalues. J. Eur. Math. Soc. 7, 69-99 (2005) · Zbl 1105.82025 · doi:10.4171/JEMS/22
[12] D.L. Bunker, B. Garrett, T. Kleindienst, G.S. Long, 111. Combust. Flame 23, 373 (1974) · doi:10.1016/0010-2180(74)90120-5
[13] Y. Cao, D. Gillespie, L. Petzold, The slow-scale stochastic simulation algorithm. J. Chem. Phys. 122(1), 014116 (2006) · doi:10.1063/1.1824902
[14] E.B. Davies, Metastability and the Ising model. J. Stat. Phys. 27, 657-675 (1982) · Zbl 0511.60095 · doi:10.1007/BF01013440
[15] E.B. Davies, Metastable states of symmetric Markov semigroups I. Proc. Lond. Math. Soc. 45(3), 133-150 (1982) · Zbl 0498.47017 · doi:10.1112/plms/s3-45.1.133
[16] E.B. Davies, Metastable states of symmetric Markov semigroups II. J. Lond. Math. Soc. 26(2), 541-556 (1982) · Zbl 0527.47028 · doi:10.1112/jlms/s2-26.3.541
[17] M.V. Day, Recent progress on the small parameter exit problem. Stochastics 20, 121-150 (1987) · Zbl 0612.60067 · doi:10.1080/17442508708833440
[18] H. Eyring, The activated complex in chemical reactions. J. Chem. Phys. 3, 107-115 (1935) · doi:10.1063/1.1749604
[19] M.B. Elowitz, A.J. Levine, E.D. Siggia, P.S. Swain, Stochastic gene expression in a single cell. Science 297(5584), 1183-1186 (2002) · doi:10.1126/science.1070919
[20] R. Erban, I.G. Kevrekidis, D. Adalsteinsson, T.C. Elston, Gene regulatory networks: a coarse-grained, equation-free approach to multiscale computation. J. Chem. Phys. 124(8), 084106 (2006) · doi:10.1063/1.2149854
[21] R. Erban, S.J. Chapman, I.G. Kevrekidis, T. Vejchodský, Analysis of a stochastic chemical system close to a sniper bifurcation of its mean-field model. SIAM J. Appl. Math. 70(3), 984-1016 (2009) · Zbl 1200.80010 · doi:10.1137/080731360
[22] R. Erban, S.J. Chapman, Stochastic modelling of reaction-diffusion processes: algorithms for bimolecular reactions. Phys. Biol. 6(4), 046001 (2009) · doi:10.1088/1478-3975/6/4/046001
[23] M. Feinberg. Lectures of chemical reactions networks. (http://www.che.eng.ohio-state.edu/ /feinberg/LecturesOnReactionNetworks/)
[24] James E. Ferrell Jr, Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr. Opin. Chem. Biol. 6, 140-148 (2002)
[25] M.I. Freidlin, A.D. Wentzell, Random perturbations of dynamical systems. Second edition, Grundlehren Math. Wiss. 260 (Springer, NewYork, 1998) · Zbl 0922.60006
[26] C. Gadgila, C.H. Lee, H.G. Othmer, A stochastic analysis of first-order reaction networks. Bull. Math. Biol. 67, 901-946 (2005) · Zbl 1334.92473 · doi:10.1016/j.bulm.2004.09.009
[27] M.A. Gibson, J. Bruck, Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104, 1876-1889 (2000) · doi:10.1021/jp993732q
[28] D.T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 2, 403-434 (1976) · doi:10.1016/0021-9991(76)90041-3
[29] D.T. Gillespie, Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340-2361 (1977) · doi:10.1021/j100540a008
[30] D.T. Gillespie, Markov Processes: an Introduction for Physical Scientists (Academic Press, San Diego, 1992) · Zbl 0743.60001
[31] P. Hänggi, P. Talkner, M. Borkovec, Reaction rate theory: fifty years after Kramers. Rev. Mod. Phys. 62, 251-341 (1990) · doi:10.1103/RevModPhys.62.251
[32] M. Herrmann, B. Niethammer, Kramer’s formula for chemical reactions in the context of Wasserstein Gradient Flows. Preprint · Zbl 1219.35315
[33] H.J. Hwang, J.J.L. Velázquez, Bistable stochastic biochemical networks: highly specific systems with few chemicals. J. Math. Chem. 51(5), 1343-1375 (2013) · Zbl 1320.92043
[34] H.W. Kang, L. Zheng, H.G. Othmer, A new method for choosing the computational cell in stochastic reaction-diffusion systems. Preprint · Zbl 1263.80020
[35] H. Kacser, J.A. Burns, The molecular basis of dominance. Genetics 97, 639-666 (1981)
[36] T. Kalmar, C. Lim, P. Hayward, S. Muñoz-Descalzo, J. Nichols, J. Garcia-Ojalvo, A. Martinez-Arias, Regulated fluctuations in Nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol. 7(7), e1000149 (2009) · Zbl 0527.47028
[37] H.A. Kramers, Brownian motion in a field of force and the diffusion model of chemical reactions. Physica 7, 284-304 (1940) · Zbl 0061.46405 · doi:10.1016/S0031-8914(40)90098-2
[38] T.G. Kurtz, The relationship between stochastic and deterministic models for chemical reactions. J. Chem. Phys. 57(7), 2976-2978 (1972) · doi:10.1063/1.1678692
[39] Landauer, R.; Moss, F. (ed.); McClintock, PVE (ed.), Noise activated escape from metastable states: an historical view, No. I, 1-16 (1989), Cambridge · doi:10.1017/CBO9780511897818.003
[40] C.H. Lee, R. Lui, A reduction method for multiple time scale stochastic reaction networks. J. Math. Chem. 46, 1292-1321 (2009) · Zbl 1196.92071 · doi:10.1007/s10910-008-9517-x
[41] C.H. Lee, Stochastic analysis of biochemical reaction networks. PhD. Thesis. (Univ. Minnesota, 2006) · Zbl 0996.92012
[42] R. Losick, C. Desplan, Stochasticity and cell fate. Science 320, 65-68 (2008) · doi:10.1126/science.1147888
[43] B.J. Matkowsky, Z. Schuss, Diffusion across characteristic boundaries. SIAM J. Appl. Math. 42, 822-834 (1982) · Zbl 0495.60078 · doi:10.1137/0142057
[44] M. Peletier, G. Savare, M. Veneroni, From diffusion to reaction via \[\Gamma\] Γ-convergence. SIAM J. Math. Anal. 42-4, 1805-1825 (2010) · Zbl 1221.35045 · doi:10.1137/090781474
[45] B.E. Munsky, The Finite State Projection Approach for the Solution of the Master Equation and its Applications to Stochastic Gene Regulatory Networks. PhD. Thesis (Univ. California at Santa Barbara, 2008) · Zbl 1366.92049
[46] J.M. Raser, E.K. Shea, Noise in gene expression: origins, consequences and control. Science 309, 2010-2013 (2005) · doi:10.1126/science.1105891
[47] F. Schlögl, Chemical reaction models for non-equilibrium phase transitions (Zeitschrift für Physik A. Hadrons and Nuclei (Springer, New York, 1972), pp. 147-161 · Zbl 1118.92031
[48] C. Schütte, Conformational Dynamics: Modelling, Theory, Algorithm, and Application to Biomolecules (FachbereichMathematik und Informatik, Freie Universit at Berlin, Habilitation Thesis, 1998) · Zbl 1012.82015
[49] C. Schütte, A. Fischer, W. Huisinga, P. Deuflhard, A direct approach to conformational dynamics based on hybrid Monte Carlo. J. Comput. Phys., Special Issue on Computational Biophysics 151, 146-168 (1999) · Zbl 0933.65145
[50] Schütte, C.; Huisinga, W.; Deuflhard, P.; Fiedler, B. (ed.), Transfer operator approach to conformational dynamics in biomolecular systems, 191-223 (2001), New York · Zbl 0996.92012 · doi:10.1007/978-3-642-56589-2_9
[51] Schütte, C.; Huisinga, W.; Ciarlet, PG (ed.), Biomolecular conformations can be identified as metastable sets of molecular dynamics, 699-744 (2003), Amsterdam · Zbl 1066.81658 · doi:10.1016/S1570-8659(03)10013-0
[52] A. Slepoy, A.P. Thompson, S.J. Plimpton, A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks. J. Chem. Phys. 128, 205101 (2008) · doi:10.1063/1.2919546
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