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We present an algorithm for the stochastic simulation of gene expression andheterogeneous population dynamics. The algorithm combines an exact method tosimulate molecular-level fluctuations in single cells and a constant-numberMonte Carlo method to simulate time-dependent statistical characteristics ofgrowing cell populations. To benchmark performance, we compare simulationresults with steadystate and time-dependent analytical solutions for severalscenarios, including steadystate and time-dependent gene expression, and theeffects on population heterogeneity of cell growth, division, and DNAreplication. This comparison demonstrates that the algorithm provides anefficient and accurate approach to simulate how complex biological featuresinfluence gene expression. We also use the algorithm to model gene expressiondynamics within "bet-hedging" cell populations during their adaption toenvironmental stress. These simulations indicate that the algorithm provides aframework suitable for simulating and analyzing realistic models ofheterogeneous population dynamics combining molecular-level stochastic reactionkinetics, relevant physiological details and phenotypic variability.

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