Chemical diffusion master equation: Formulations of reaction–diffusion processes on the molecular level

Author:

del Razo Mauricio J.12ORCID,Winkelmann Stefanie3ORCID,Klein Rupert1ORCID,Höfling Felix13ORCID

Affiliation:

1. Department of Mathematics and Computer Science, Freie Universität Berlin 1 , Berlin, Germany

2. Dutch Institute for Emergent Phenomena 2 , 1090GL Amsterdam, The Netherlands

3. Zuse Institute Berlin 3 , Takustr. 7, 14195 Berlin, Germany

Abstract

The chemical diffusion master equation (CDME) describes the probabilistic dynamics of reaction–diffusion systems at the molecular level [del Razo et al., Lett. Math. Phys. 112, 49 (2022)]; it can be considered as the master equation for reaction–diffusion processes. The CDME consists of an infinite ordered family of Fokker–Planck equations, where each level of the ordered family corresponds to a certain number of particles and each particle represents a molecule. The equations at each level describe the spatial diffusion of the corresponding set of particles, and they are coupled to each other via reaction operators—linear operators representing chemical reactions. These operators change the number of particles in the system and, thus, transport probability between different levels in the family. In this work, we present three approaches to formulate the CDME and show the relations between them. We further deduce the non-trivial combinatorial factors contained in the reaction operators, and we elucidate the relation to the original formulation of the CDME, which is based on creation and annihilation operators acting on many-particle probability density functions. Finally, we discuss applications to multiscale simulations of biochemical systems among other future prospects.

Funder

Deutsche Forschungsgemeinschaft

Berlin Mathematics Research Center MATH+

Dutch Institute for Emergent Phenomena

Publisher

AIP Publishing

Subject

Mathematical Physics,Statistical and Nonlinear Physics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Data-driven dynamical coarse-graining for condensed matter systems;The Journal of Chemical Physics;2024-01-09

2. Stochastic Reaction Networks Within Interacting Compartments;Bulletin of Mathematical Biology;2023-08-25

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