Literature DB >> 23579988

A comparison of bimolecular reaction models for stochastic reaction-diffusion systems.

I C Agbanusi1, S A Isaacson.   

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 [Formula: see text] 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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Year:  2013        PMID: 23579988     DOI: 10.1007/s11538-013-9833-6

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  10 in total

1.  Editorial: special issue on stochastic modelling of reaction-diffusion processes in biology.

Authors:  Radek Erban; Hans G Othmer
Journal:  Bull Math Biol       Date:  2014-04       Impact factor: 1.758

2.  Unified path integral approach to theories of diffusion-influenced reactions.

Authors:  Thorsten Prüstel; Martin Meier-Schellersheim
Journal:  Phys Rev E       Date:  2017-08-25       Impact factor: 2.529

3.  Reaction rates for mesoscopic reaction-diffusion kinetics.

Authors:  Stefan Hellander; Andreas Hellander; Linda Petzold
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-02-23

4.  Therapy operating characteristic curves: tools for precision chemotherapy.

Authors:  Harrison H Barrett; David S Alberts; James M Woolfenden; Luca Caucci; John W Hoppin
Journal:  J Med Imaging (Bellingham)       Date:  2016-05-02

5.  Quantifying and Reducing Uncertainties in Cancer Therapy.

Authors:  Harrison H Barrett; David S Alberts; James M Woolfenden; Zhonglin Liu; Luca Caucci; John W Hoppin
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2015-02-21

6.  Stochastic analysis of reaction-diffusion processes.

Authors:  Jifeng Hu; Hye-Won Kang; Hans G Othmer
Journal:  Bull Math Biol       Date:  2013-05-30       Impact factor: 1.758

7.  Stochastic reaction-diffusion processes with embedded lower-dimensional structures.

Authors:  Siyang Wang; Johan Elf; Stefan Hellander; Per Lötstedt
Journal:  Bull Math Biol       Date:  2013-10-26       Impact factor: 1.758

8.  Space-time histories approach to fast stochastic simulation of bimolecular reactions.

Authors:  Thorsten Prüstel; Martin Meier-Schellersheim
Journal:  J Chem Phys       Date:  2021-04-28       Impact factor: 3.488

9.  Particle-based simulations of polarity establishment reveal stochastic promotion of Turing pattern formation.

Authors:  Michael Pablo; Samuel A Ramirez; Timothy C Elston
Journal:  PLoS Comput Biol       Date:  2018-03-12       Impact factor: 4.475

Review 10.  Spatial Stochastic Intracellular Kinetics: A Review of Modelling Approaches.

Authors:  Stephen Smith; Ramon Grima
Journal:  Bull Math Biol       Date:  2018-05-21       Impact factor: 1.758

  10 in total

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