Bulletin of Mathematical Biology

, Volume 76, Issue 4, pp 922–946

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

Original Article

DOI: 10.1007/s11538-013-9833-6

Cite this article as:
Agbanusi, I.C. & Isaacson, S.A. Bull Math Biol (2014) 76: 922. doi:10.1007/s11538-013-9833-6


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.


Stochastic reaction–diffusion Diffusion limited reaction 

Copyright information

© Society for Mathematical Biology 2013

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsBoston UniversityBostonUSA

Personalised recommendations