An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics

TL;DR

This paper introduces an efficient algorithm that combines molecular fluctuation simulation with population dynamics to accurately model gene expression and heterogeneity in cell populations under various conditions.

Contribution

The paper presents a novel combined algorithm for stochastic gene expression simulation and population dynamics, validated against analytical solutions and applied to stress adaptation models.

Findings

Algorithm accurately reproduces steady-state and dynamic gene expression patterns.

Efficiently simulates population heterogeneity during growth and stress.

Framework integrates molecular noise with physiological variability.

Abstract

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios, including steadystate and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within "bet-hedging" cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details and phenotypic variability.