标题： 基因表达和异质种群动态的随机模拟算法 显示英文标题

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

摘要： 我们提出一种用于基因表达和异质种群动态的随机模拟算法。 该算法结合了一种精确的方法来模拟单个细胞中的分子水平波动，以及一种恒定数量的蒙特卡洛方法来模拟生长细胞种群的时间依赖性统计特性。 为了评估性能，我们将模拟结果与几种情况下的稳态和时间依赖性解析解进行比较，包括稳态和时间依赖性基因表达，以及细胞生长、分裂和DNA复制对种群异质性的影响。 这种比较表明，该算法提供了一种高效且准确的方法来模拟复杂生物特征如何影响基因表达。 我们还使用该算法来模拟在适应环境压力期间“赌注规避”细胞种群内的基因表达动力学。 这些模拟表明，该算法提供了一个适合模拟和分析结合分子水平随机反应动力学、相关生理细节和表型变异性的现实异质种群动态模型的框架。 显示英文摘要

摘要： 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.