@Article{CiCP-9-1,
author = {},
title = {An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics},
journal = {Communications in Computational Physics},
year = {2011},
volume = {9},
number = {1},
pages = {89--112},
abstract = {<p style="text-align: justify;">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 steady-state
and time-dependent analytical solutions for several scenarios, including steady-state
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 &quot;bet-hedging&quot; 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.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.280110.070510a},
url = {https://global-sci.com/article/80839/an-algorithm-for-the-stochastic-simulation-of-gene-expression-and-heterogeneous-population-dynamics}
}