From single-cell genetic architecture to cell population dynamics: Quantitatively decomposing the effects of different population heterogeneity sources for a genetic network with positive feedback architecture
From single-cell genetic architecture to cell population dynamics: Quantitatively decomposing the effects of different population heterogeneity sources for a genetic network with positive feedback architecture
复制标题
DOI：
10.1529/biophysj.106.100271
复制
发表时间：
2007-06-01
影响因子：
3.4
通讯作者：
Mantzaris, Nikos V.
中科院分区：
文献类型：
--
作者：
Mantzaris, Nikos V.
细胞间的表型变异或细胞群异质性源于两个根本不同的来源：细胞分裂时细胞物质的不均匀分配以及与细胞内反应相关的随机波动。我们开发了一个数学和计算框架，可以定量分离两种异质性来源，并将其应用于具有正反馈架构的遗传网络。该框架由三个截然不同的数学公式组成：a），连续统模型，完全忽略群体异质性； b) 确定性细胞群体平衡模型，该模型解释了仅源自细胞分裂时不平等分配的群体异质性； c)，一个适应群体异质性的两种来源的完全随机模型。该框架能够定量分解不同群体异质性来源对系统行为的影响。我们的结果表明细胞群体异质性在准确预测平均群体特性方面的重要性。此外，我们发现细胞分裂时的不平等划分和急剧的分裂率缩小了群体表现出双稳态行为的参数空间区域，这是具有正反馈架构的网络的特征。此外，由于操作员波动缓慢和分子数量较少，单细胞水平的固有噪声进一步导致细胞群水平双稳态范围的缩小。最后，发现细胞群水平上的内在噪声的影响与单细胞水平上的明显不同，这强调了模拟整个细胞群而不仅仅是单个细胞的重要性，以了解单细胞遗传结构和细胞群水平上的行为之间复杂的相互作用。
Phenotypic cell-to-cell variability or cell population heterogeneity originates from two fundamentally different sources: unequal partitioning of cellular material at cell division and stochastic fluctuations associated with intracellular reactions. We developed a mathematical and computational framework that can quantitatively isolate both heterogeneity sources and applied it to a genetic network with positive feedback architecture. The framework consists of three vastly different mathematical formulations: a), a continuum model, which completely neglects population heterogeneity; b), a deterministic cell population balance model, which accounts for population heterogeneity originating only from unequal partitioning at cell division; and c), a fully stochastic model accommodating both sources of population heterogeneity. The framework enables the quantitative decomposition of the effects of the different population heterogeneity sources on system behavior. Our results indicate the importance of cell population heterogeneity in accurately predicting even average population properties. Moreover, we find that unequal partitioning at cell division and sharp division rates shrink the region of the parameter space where the population exhibits bistable behavior, a characteristic feature of networks with positive feedback architecture. In addition, intrinsic noise at the single-cell level due to slow operator fluctuations and small numbers of molecules further contributes toward the shrinkage of the bistability regime at the cell population level. Finally, the effect of intrinsic noise at the cell population level was found to be markedly different than at the single-cell level, emphasizing the importance of simulating entire cell populations and not just individual cells to understand the complex interplay between single-cell genetic architecture and behavior at the cell population level.