Conceptualizing a tool to optimize therapy based on dynamic heterogeneity
Conceptualizing a tool to optimize therapy based on dynamic heterogeneity
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DOI：
10.1088/1478-3975/9/6/065005
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发表时间：
2012-12-01
期刊：
影响因子：
2
通讯作者：
Tlsty, Thea D.
中科院分区：
文献类型：
--
作者：
Liao, David;Estevez-Salmeron, Luis;Tlsty, Thea D.
复杂的生物系统在基础物理研究过程中往往表现出一种平行的随机性。这种简单的随机性是由于系统的复杂性而出现的，并且是生物学中称为表型随机性的一个基本方面的基础。在单单位水平上，表型的持续随机波动可能导致两种突发性种群表型。表型随机性不仅在细胞群体内产生异质性，而且允许多种状态之间的可逆转换。这种表型间转换倾向于在特定成员耗尽后将种群恢复到以前的组成。我们称这种趋势为稳态异质性。这些动态异质性的概念可以应用于由分子、细胞、个体等组成的群体。在这里，我们讨论了表型随机性这一概念，即表型随机性是细胞群体异质性产生的基础，也可用于控制群体组成，尤其有助于持续出现的耐药性和消耗耐药细胞的机会。使用“大”和“小”数量的生物分子成分的概念，我们合理化了我们使用马尔可夫过程来模拟耐药细胞的产生和根除。利用这些见解，我们开发了一种图形工具，称为节拍图，我们建议它将允许我们优化给药频率和总疗程持续时间，以获得临床效益。
Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both 'large' and 'small' numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit.