Literature DB >> 27300857

Breakdown of the reaction-diffusion master equation with nonelementary rates.

Stephen Smith1, Ramon Grima1.   

Abstract

The chemical master equation (CME) is the exact mathematical formulation of chemical reactions occurring in a dilute and well-mixed volume. The reaction-diffusion master equation (RDME) is a stochastic description of reaction-diffusion processes on a spatial lattice, assuming well mixing only on the length scale of the lattice. It is clear that, for the sake of consistency, the solution of the RDME of a chemical system should converge to the solution of the CME of the same system in the limit of fast diffusion: Indeed, this has been tacitly assumed in most literature concerning the RDME. We show that, in the limit of fast diffusion, the RDME indeed converges to a master equation but not necessarily the CME. We introduce a class of propensity functions, such that if the RDME has propensities exclusively of this class, then the RDME converges to the CME of the same system, whereas if the RDME has propensities not in this class, then convergence is not guaranteed. These are revealed to be elementary and nonelementary propensities, respectively. We also show that independent of the type of propensity, the RDME converges to the CME in the simultaneous limit of fast diffusion and large volumes. We illustrate our results with some simple example systems and argue that the RDME cannot generally be an accurate description of systems with nonelementary rates.

Year:  2016        PMID: 27300857     DOI: 10.1103/PhysRevE.93.052135

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  8 in total

1.  Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.

Authors:  David J Warne; Ruth E Baker; Matthew J Simpson
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2.  Revisiting the Reduction of Stochastic Models of Genetic Feedback Loops with Fast Promoter Switching.

Authors:  James Holehouse; Ramon Grima
Journal:  Biophys J       Date:  2019-08-27       Impact factor: 4.033

3.  Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry.

Authors:  M E Johnson; A Chen; J R Faeder; P Henning; I I Moraru; M Meier-Schellersheim; R F Murphy; T Prüstel; J A Theriot; A M Uhrmacher
Journal:  Mol Biol Cell       Date:  2020-11-25       Impact factor: 4.138

4.  Single-cell variability in multicellular organisms.

Authors:  Stephen Smith; Ramon Grima
Journal:  Nat Commun       Date:  2018-01-24       Impact factor: 14.919

Review 5.  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

6.  pSpatiocyte: a high-performance simulator for intracellular reaction-diffusion systems.

Authors:  Satya N V Arjunan; Atsushi Miyauchi; Kazunari Iwamoto; Koichi Takahashi
Journal:  BMC Bioinformatics       Date:  2020-01-29       Impact factor: 3.169

7.  Memory functions reveal structural properties of gene regulatory networks.

Authors:  Edgar Herrera-Delgado; Ruben Perez-Carrasco; James Briscoe; Peter Sollich
Journal:  PLoS Comput Biol       Date:  2018-02-22       Impact factor: 4.475

8.  Hybrid CME-ODE method for efficient simulation of the galactose switch in yeast.

Authors:  David M Bianchi; Joseph R Peterson; Tyler M Earnest; Michael J Hallock; Zaida Luthey-Schulten
Journal:  IET Syst Biol       Date:  2018-08       Impact factor: 1.615

  8 in total

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