A Law of Large Numbers in the Supremum Norm for a Multiscale Stochastic Spatial Gene Network

TL;DR

This paper establishes a new law of large numbers for multiscale stochastic spatial gene networks, showing how different abundance scales lead to coupled PDE and ODE models with convergence in the supremum norm.

Contribution

It introduces a novel multiscale analysis framework for stochastic spatial gene networks, capturing the dynamics of rare species and their spatial correlations.

Findings

High scale component converges to a PDE spatial model.

Low scale component converges to a homogeneous ODE model.

Results demonstrate convergence in the supremum norm.

Abstract

We study the asymptotic behavior of multiscale stochastic spatial gene networks. Multiscaling takes into account the difference of abundance between molecules , and captures the dynamic of rare species at a mesoscopic level. We introduce an assumption of spatial correlations for reactions involving rare species and a new law of large numbers is obtained. According to the scales, the whole system splits into two parts with different but coupled dynamics. The high scale component converges to the usual spatial model which is the solution of a partial differential equation, whereas, the low scale component converges to the usual homogeneous model which is the solution of an ordinary differential equation. Comparisons are made in the supremum norm.