An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
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DOI：
10.4208/cicp.280110.070510a
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发表时间：
2011-01-01
影响因子：
3.7
通讯作者：
Kaern, Mads
中科院分区：
文献类型：
--
作者：
Charlebois, Daniel A.;Intosalmi, Jukka;Kaern, Mads
我们提出了一个算法的基因表达和异质种群动力学的随机模拟。该算法结合了精确的方法来模拟单细胞中的分子水平波动和常数蒙特卡罗方法来模拟生长细胞群体的时间依赖性统计特性。基准性能，我们比较模拟结果与稳态和时间依赖性的分析解决方案的几种情况下，包括稳态和时间依赖性的基因表达，以及对细胞生长，分裂和DNA复制的群体异质性的影响。这种比较表明，该算法提供了一种有效和准确的方法来模拟复杂的生物特征如何影响基因表达。我们还使用该算法来模拟基因表达动态的“赌注对冲”细胞群体在适应环境压力。这些模拟表明，该算法提供了一个框架，适合模拟和分析现实模型的异质性人口动态结合分子水平的随机反应动力学，相关的生理细节和表型变异。
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 "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.