An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics (Communications in Computational Physics, 2011)
We present an algorithm for the stochastic simulation of gene expressionand heterogeneous population dynamics. The algorithm combines an exact methodto simulate molecular-level fluctuations in single cells and a constant-number MonteCarlo method to simulate time-dependent statistical characteristics of growing cellpopulations. To benchmark performance, we compare simulation results with steady-stateand time-dependent analytical solutions for several scenarios, including steady-stateand time-dependent gene expression, and the effects on population heterogeneityof cell growth, division, and DNA replication. This comparison demonstrates thatthe algorithm provides an efficient and accurate approach to simulate how complexbiological features influence gene expression. We also use the algorithm to modelgene expression dynamics within "bet-hedging" cell populations during their adaptionto environmental stress. These simulations indicate that the algorithm providesa framework suitable for simulating and analyzing realistic models of heterogeneouspopulation dynamics combining molecular-level stochastic reaction kinetics, relevantphysiological details and phenotypic variability.