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A Comparison of Bimolecular Reaction Models for Stochastic Reaction–Diffusion Systems

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Abstract

Stochastic reaction–diffusion models have become an important tool in studying how both noise in the chemical reaction process and the spatial movement of molecules influences the behavior of biological systems. There are two primary spatially-continuous models that have been used in recent studies: the diffusion limited reaction model of Smoluchowski, and a second approach popularized by Doi. Both models treat molecules as points undergoing Brownian motion. The former represents chemical reactions between two reactants through the use of reactive boundary conditions, with two molecules reacting instantly upon reaching a fixed separation (called the reaction-radius). The Doi model uses reaction potentials, whereby two molecules react with a fixed probability per unit time, λ, when separated by less than the reaction radius. In this work, we study the rigorous relationship between the two models. For the special case of a protein diffusing to a fixed DNA binding site, we prove that the solution to the Doi model converges to the solution of the Smoluchowski model as λ→∞, with a rigorous \(O(\lambda^{-\frac{1}{2} + \epsilon})\) error bound (for any fixed ϵ>0). We investigate by numerical simulation, for biologically relevant parameter values, the difference between the solutions and associated reaction time statistics of the two models. As the reaction-radius is decreased, for sufficiently large but fixed values of λ, these differences are found to increase like the inverse of the binding radius.

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References

  • Andrews, S. S., & Bray, D. (2004). Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol., 1, 137–151.

    Article  Google Scholar 

  • BelHadjAli, H., Amor, A. B., & Brasche, J. F. (2011). Large coupling convergence: overview and new results. In M. Demuth, B. W. Schulze, & I. Witt (Eds.), Operator theory: advances and applications: Vol. 211. Partial differential equations and spectral theory (pp. 73–117). Basel: Springer.

    Chapter  Google Scholar 

  • Demuth, M. (1980). On scattering of diffusion process generators. Lett. Math. Phys., 4(5), 417–424.

    Article  MathSciNet  MATH  Google Scholar 

  • Demuth, M., Jeske, F., & Kirsch, W. (1993). Rate of convergence for large coupling limits by Brownian motion. Ann. Inst. Henri Poincaré, a Phys. Théor., 59(3), 327–355.

    MathSciNet  MATH  Google Scholar 

  • Doi, M. (1976a). Second quantization representation for classical many-particle system. J. Phys. A, Math. Gen., 9(9), 1465–1477.

    Article  Google Scholar 

  • Doi, M. (1976b). Stochastic theory of diffusion-controlled reaction. J. Phys. A, Math. Gen., 9(9), 1479–1495.

    Article  Google Scholar 

  • Donev, A., Bulatov, V. V., Oppelstrup, T., Gilmer, G. H., Sadigh, B., & Kalos, M. H. (2010). A first-passage kinetic Monte Carlo algorithm for complex diffusion–reaction systems. J. Comp. Physiol., 229(9), 3214–3236.

    Article  MathSciNet  MATH  Google Scholar 

  • Drawert, B., Engblom, S., & Hellander, A. (2012). URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries. BMC Syst. Biol., 6(1), 76.

    Article  Google Scholar 

  • Dushek, O., van der Merwe, P. A., & Shahrezaei, V. (2011). Ultrasensitivity in multisite phosphorylation of membrane-anchored proteins. Biophys. J., 100(5), 1189–1197.

    Article  Google Scholar 

  • Elf, J., & Ehrenberg, M. (2004). Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. IET Syst. Biol., 1(2), 230–236.

    Article  Google Scholar 

  • Erban, R., & Chapman, S. J. (2009). Stochastic modelling of reaction–diffusion processes: algorithms for bimolecular reactions. Phys. Biol., 6(4), 046001.

    Article  Google Scholar 

  • Erban, R., Chapman, S. J., & Maini, P. K. (2007). A practical guide to stochastic simulations of reaction-diffusion processes. arXiv:0704.1908 [q-bio.SC].

  • Fange, D., Berg, O. G., Sjöberg, P., & Elf, J. (2010). Stochastic reaction–diffusion kinetics in the microscopic limit. Proc. Natl. Acad. Sci. USA, 107(46), 19820–19825.

    Article  MATH  Google Scholar 

  • Fange, D., Mahmutovic, A., & Elf, J. (2012). MesoRD 1.0: stochastic reaction–diffusion simulations in the microscopic limit. Bioinformatics, 28(23), 3155–3157.

    Article  Google Scholar 

  • Gardiner, C. W. (1996). Springer series in synergetics: Vol. 13. Handbook of stochastic methods: for physics, chemistry, and the natural sciences (2nd ed.). New York: Springer.

    MATH  Google Scholar 

  • Gardiner, C. W., McNeil, K. J., Walls, D. F., & Matheson, I. S. (1976). Correlations in stochastic models of chemical reactions. J. Stat. Phys., 14, 307.

    Article  Google Scholar 

  • Gesztesy, F., Gurarie, D., Holder, H., Klaus, M., Sadun, L., Simon, B., & Vogl, P. (1988). Trapping and cascading of eigenvalues in the large coupling limit. Commun. Math. Phys., 118(4), 597–634.

    Article  MathSciNet  MATH  Google Scholar 

  • Girsanov, I. V. (1960). The solution of certain boundary problems for parabolic and elliptic equations with discontinuous coefficients. Sov. Math. Dokl., 1, 1373–1375.

    MathSciNet  MATH  Google Scholar 

  • Glimm, J., & Jaffe, A. (1987). Quantum physics; a functional integral point of view (2nd ed.). New York: Springer.

    MATH  Google Scholar 

  • Hellander, S., Hellander, A., & Petzold, L. (2012). Reaction–diffusion master equation in the microscopic limit. Phys. Rev. E, 85(4), 042901.

    Article  Google Scholar 

  • Hepburn, I., Chen, W., Wils, S., & De Schutter, E. (2012). STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies. BMC Syst. Biol., 6(1), 36.

    Article  Google Scholar 

  • Isaacson, S. A. (2008). Relationship between the reaction–diffusion master equation and particle tracking models. J. Phys. A, Math. Theor., 41(6), 065003.

    Article  MathSciNet  MATH  Google Scholar 

  • Isaacson, S. A. (2009). The reaction–diffusion master equation as an asymptotic approximation of diffusion to a small target. SIAM J. Appl. Math., 70(1), 77–111.

    Article  MathSciNet  MATH  Google Scholar 

  • Isaacson, S. A. (2012, submitted). A convergent reaction–diffusion master equation. Preprint. arXiv:1211.6772.

  • Isaacson, S. A., & Isaacson, D. (2009). Reaction–diffusion master equation, diffusion-limited reactions, and singular potentials. Phys. Rev. E, 80(6), 066106.

    Article  Google Scholar 

  • Isaacson, S. A., McQueen, D. M., & Peskin, C. S. (2011). The influence of volume exclusion by chromatin on the time required to find specific DNA binding sites by diffusion. Proc. Natl. Acad. Sci. USA, 108(9), 3815–3820.

    Article  Google Scholar 

  • Kang, H.-W., Zheng, L., & Othmer, H. G. (2012). A new method for choosing the computational cell in stochastic reaction–diffusion systems. J. Math. Biol., 65(6–7), 1017–1099.

    Article  MathSciNet  MATH  Google Scholar 

  • Keizer, J. (1982). Nonequilibrium statistical thermodynamics and the effect of diffusion on chemical reaction rates. J. Phys. Chem., 86, 5052–5067.

    Article  Google Scholar 

  • Kerr, R. A., et al. (2008). Fast Monte Carlo simulation methods for biological reaction–diffusion systems in solution and on surfaces. SIAM J. Sci. Comput., 30(6), 3126–3149.

    Article  MathSciNet  MATH  Google Scholar 

  • Kühner, F., Costa, L. T., Bisch, P. M., Thalhammer, S., Heckl, W. M., & Gaub, H. E. (2004). LexA-DNA bond strength by single molecule force spectroscopy. Biophys. J., 87, 2683–2690.

    Article  Google Scholar 

  • Lipkova, J., Zygalakis, K. C., Chapman, S. J., & Erban, R. (2011). Analysis of Brownian Dynamics simulations of reversible bimolecular reactions. SIAM J. Appl. Math., 71(3), 714.

    Article  MathSciNet  MATH  Google Scholar 

  • McQuarrie, D. A. (1967). Stochastic approach to chemical kinetics. J. Appl. Probab., 4, 413–478.

    Article  MathSciNet  MATH  Google Scholar 

  • Nadkarni, S., Bartol, T. M., Stevens, C. F., Sejnowski, T. J., & Levine, H. (2012). Short-term plasticity constrains spatial organization of a hippocampal presynaptic terminal. Proc. Natl. Acad. Sci. USA, 109(36), 14657–14662.

    Article  Google Scholar 

  • Olenik, O. A. (1961). Boundary-value problems for linear elliptic and parabolic equations with discontinuous coefficients. Izv. Akad. Nauk SSSR, Ser. Mat., 25(1), 3–20.

    MathSciNet  Google Scholar 

  • Smoluchowski, M. V. (1917). Mathematical theory of the kinetics of the coagulation of colloidal solutions. Z. Phys. Chem., 92, 129–168.

    Google Scholar 

  • Takahashi, K., Tanase-Nicola, S., & ten Wolde, P. R. (2010). Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proc. Natl. Acad. Sci. USA, 107(6), 2473–2478.

    Article  Google Scholar 

  • Taylor, M. E. (1996). Applied mathematical sciences: Vol. 116. Partial differential equations II: qualitative studies of linear equations. New York: Springer.

    MATH  Google Scholar 

  • Teramoto, E., & Shigesada, N. (1967). Theory of bimolecular reaction processes in liquids. Prog. Theor. Phys., 37(1), 29–51.

    Article  Google Scholar 

  • Van Kampen, N. G. (2001). Stochastic processes in physics and chemistry. Amsterdam: North-Holland.

    MATH  Google Scholar 

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Acknowledgements

SAI and ICA are supported by NSF grant DMS-0920886. ICA was also supported by the Center for Biodynamics NSF RTG grant DMS-0602204. The authors thank the referees for their helpful comments and suggestions.

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Correspondence to S. A. Isaacson.

Appendices

Appendix A: Cumulative Binding Time Distributions

The cumulative binding time distributions we evaluated in Sect. 3, \(\operatorname{Prob}[T_{\textrm{Smol}} < t ]\) and \(\operatorname{Prob}[T_{\textrm{Doi}}< t ]\) are given by the series expansions

and

where for

$$ h(\alpha) = \frac{1}{\alpha^{\frac{3}{2}} R} \bigl[ \bigl( (R-r_{\textrm{b}}) \sqrt{\alpha} \bigr) \cos \bigl((R-r_{\textrm{b}}) \sqrt{\alpha} \bigr) - (1 + R r_{\textrm {b}}\alpha ) \sin \bigl( (R-r_{\textrm{b}}) \sqrt{\alpha} \bigr) \bigr], $$

we have

and

with

$$ \ell_n = \frac{\tfrac{1}{R\sqrt{\mu_n}}\sin (\sqrt{\mu_n}(R-r_{\textrm{b}}))-\cos(\sqrt{\mu_n}(R-r_{\textrm {b}}))}{\lambda- \mu_n}. $$

Appendix B: Mean Binding Time

Let \(\mathbb {E}[T_{\textrm{Doi}} ]\) denote the mean time at which the two molecules in the Doi model (3) first react when initially separated by r 0. \(\mathbb {E}[T_{\textrm{Doi}} ]\) can be shown to satisfy (Gardiner 1996; Van Kampen 2001)

$$ \varDelta_{r_0} \mathbb {E}[T_{\textrm{Doi}} ](r_0) - \hat{\lambda } \, \mathbf {1}_{\{r < r_{\textrm{b}}\}}(r) \mathbb {E}[T_{\textrm {Doi}} ](r_0) =-\frac{1}{D}, \quad0 \leq r_0 < R, $$
(24)

with the boundary condition,

(Here, \(\hat{\lambda} = \lambda/ D\).) The solution to (24) is given by (12).

The mean time, \(\mathbb {E}[T_{\textrm{Smol}} ](r_{0})\), at which the two molecules in the Smoluchowski model (2) first react when initially separated by r 0 can be shown to satisfy (Gardiner 1996; Van Kampen 2001)

$$ \varDelta_{r_0} \mathbb {E}[T_{\textrm{Smol}} ](r_0) =-\frac{1}{D}, \quad r_{\textrm{b}}< r_0 < R, $$
(25)

with the boundary conditions,

$$\mathbb {E}[T_{\textrm{Smol}} ](r_{\textrm{b}}) = 0, \qquad \frac {\partial \mathbb {E}[T_{\textrm{Smol}} ]}{\partial r_0}(R) = 0. $$

The solution to (25) is given by (13).

Appendix C: Discrete Space and Time Points

The spatial evaluation points, r i , are generated in MATLAB by

Listing 1
figure 4

r i points

The time evaluation points, t j , are generated in MATLAB by

Listing 2
figure 5

t j points

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Agbanusi, I.C., Isaacson, S.A. A Comparison of Bimolecular Reaction Models for Stochastic Reaction–Diffusion Systems. Bull Math Biol 76, 922–946 (2014). https://doi.org/10.1007/s11538-013-9833-6

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