Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks

TL;DR

This paper establishes a mathematical connection between generalized density-dependent Markov chains and piecewise-deterministic Markov processes, providing theoretical foundations for modeling bursty gene expression dynamics in single cells.

Contribution

It proves a limit theorem showing convergence of GDDMCs to PDMPs and demonstrates existence, uniqueness, and ergodicity of the stationary distribution under certain conditions.

Findings

GDDMCs converge to PDMPs as system size increases

Stationary distribution exists and is unique for the PDMP

Stationary distributions of GDDMC and PDMP models converge

Abstract

Stochastic gene regulatory networks with bursting dynamics can be modeled mesocopically as a generalized density-dependent Markov chain (GDDMC) or macroscopically as a piecewise-deterministic Markov process (PDMP). Here we prove a limit theorem showing that each family of GDDMCs will converge to a PDMP as the system size tends to infinity. Moreover, under a simple dissipative condition, we prove the existence and uniqueness of the stationary distribution and the exponential ergodicity for the PDMP limit via the coupling method. Further extensions and applications to single-cell stochastic gene expression kinetics and bursty stochastic gene regulatory networks are also discussed and the convergence of the stationary distribution of the GDDMC model to that of the PDMP model is also proved.