The finite state projection approach to analyze dynamics of heterogeneous populations.
The finite state projection approach to analyze dynamics of heterogeneous populations.
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用于分析异质群体动态的有限状态投影方法。
DOI：
10.1088/1478-3975/aa6e5a
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发表时间：
2017
期刊：
影响因子：
2
通讯作者：
Munsky,Brian
中科院分区：
文献类型：
--
作者：
Johnson,Rob;Munsky,Brian
种群建模旨在捕获和预测恒定或波动环境中细胞种群的动态。在初级阶段，人口增长是通过单个细胞的连续分裂进行的。由于随机效应，细胞群体在表型上具有内在的异质性，一些表型变量对分裂或存活率有影响，这可以从部分耐药中看出。因此，当对生长和分裂的控制依赖于表型的群体动力学建模时，相应的模型必须考虑潜在的细胞异质性。有限状态投影（FSP）方法经常被用来分析独立细胞的统计量。在这里，我们将FSP分析扩展到探索群体内细胞动力学和生物分子动力学的耦合。这种扩展允许一个通用框架，用它来模拟一个异质的、等基因的分裂和过期细胞群体的状态职业。该方法是用一个简单的细胞周期进程模型来证明的，我们用它来探索分裂细胞中耐药表型的可能动力学。我们使用这种方法来展示随机单细胞行为如何影响药物治疗的群体水平疗效，并说明对治疗方案的轻微修改如何对药物疗效产生巨大影响。
Population modeling aims to capture and predict the dynamics of cell populations in constant or fluctuating environments. At the elementary level, population growth proceeds through sequential divisions of individual cells. Due to stochastic effects, populations of cells are inherently heterogeneous in phenotype, and some phenotypic variables have an effect on division or survival rates, as can be seen in partial drug resistance. Therefore, when modeling population dynamics where the control of growth and division is phenotype dependent, the corresponding model must take account of the underlying cellular heterogeneity. The finite state projection (FSP) approach has often been used to analyze the statistics of independent cells. Here, we extend the FSP analysis to explore the coupling of cell dynamics and biomolecule dynamics within a population. This extension allows a general framework with which to model the state occupations of a heterogeneous, isogenic population of dividing and expiring cells. The method is demonstrated with a simple model of cell-cycle progression, which we use to explore possible dynamics of drug resistance phenotypes in dividing cells. We use this method to show how stochastic single-cell behaviors affect population level efficacy of drug treatments, and we illustrate how slight modifications to treatment regimens may have dramatic effects on drug efficacy.