Litcius/Paper detail

Mean Field Limits of Particle-Based Stochastic Reaction-Diffusion Models

Samuel A. Isaacson, Jingwei Ma, Konstantinos Spiliopoulos

2022SIAM Journal on Mathematical Analysis23 citationsDOI

Abstract

Particle-based stochastic reaction-diffusion (PBSRD) models are a popular approach for studying biological systems involving both noise in the reaction process and diffusive transport. In this work we derive coarse-grained deterministic partial integro-differential equation (PIDE) models that provide a mean field approximation to the volume reactivity PBSRD model, a model commonly used for studying cellular processes. We formulate a weak measure-valued stochastic process (MVSP) representation for the volume reactivity PBSRD model, demonstrating for a simplified but representative system that it is consistent with the commonly used Doi Fock space representation of the corresponding forward equation. We then prove the convergence of the general volume reactivity model MVSP to the mean field PIDEs in the large-population (i.e., thermodynamic) limit.

Topics & Concepts

MathematicsStatistical physicsReaction–diffusion systemRepresentation (politics)Stochastic differential equationDiffusionLimit (mathematics)Mean field theoryParticle systemApplied mathematicsStochastic processInteracting particle systemField (mathematics)Stochastic modellingDiffusion processPopulationWiener processMathematical analysisContinuous-time stochastic processStatisticsPhysicsThermodynamicsComputer sciencePure mathematicsPolitical sciencePoliticsQuantum mechanicsSociologyInnovation diffusionDemographyLawOperating systemKnowledge managementStochastic processes and statistical mechanicsStatistical Methods and Bayesian InferenceMarkov Chains and Monte Carlo Methods