A mathematical and computational approach for integrating the major sources of cell population heterogeneity
A mathematical and computational approach for integrating the major sources of cell population heterogeneity
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DOI：
10.1016/j.jtbi.2010.06.002
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发表时间：
2010-09-07
影响因子：
2
通讯作者：
Zygourakis, Kyriacos
中科院分区：
文献类型：
--
作者：
Stamatakis, Michail;Zygourakis, Kyriacos
过去已经使用了几种方法来模拟细菌细胞群体中的异质性，每种方法都侧重于异质性的不同来源。然而，一个整体的方法，将所有的主要来源纳入一个全面的框架适用于细胞populations.In这项工作中，我们提出了一个细胞群体主方程（CPME），描述细胞群体动力学的数学公式，并考虑到异质性的主要来源，即反应，DNA复制和分裂的随机性，以及物种内容物随机分配到两个子细胞中。该制剂还考虑了细胞生长，并尊重分子含量和细胞数量的离散性。我们进一步开发了一个Monte Carlo算法的随机过程的模拟考虑在这里。为了对我们的新框架进行基准测试，我们首先使用它来量化每个异质性来源对Elowitz等人（2002）实验使用的众所周知的双启动子系统的内在和外在表型变异性的影响。最后，我们将我们的框架应用到一个更复杂的系统，并展示了如何在单细胞水平上的噪声基因表达和生长抑制，由于蛋白质积累之间的相互作用，可以导致在细胞群体水平上的复杂行为，我们的框架的通用性，使其适合于研究大量的人工和自然的遗传网络。使用我们的Monte Carlo算法，可以预测感兴趣的遗传结构的细胞群体分布，从而量化细胞内反应的随机性或生理过程（如生长和分裂）速率的变化的影响。这种计算机模拟实验可以深入了解细胞群体的行为，并揭示导致细胞群体异质性的主要来源。(C)2010爱思唯尔有限公司保留所有权利。
Several approaches have been used in the past to model heterogeneity in bacterial cell populations, with each approach focusing on different source(s) of heterogeneity. However, a holistic approach that integrates all the major sources into a comprehensive framework applicable to cell populations is still lacking.In this work we present the mathematical formulation of a cell population master equation (CPME) that describes cell population dynamics and takes into account the major sources of heterogeneity, namely stochasticity in reaction, DNA-duplication, and division, as well as the random partitioning of species contents into the two daughter cells. The formulation also takes into account cell growth and respects the discrete nature of the molecular contents and cell numbers. We further develop a Monte Carlo algorithm for the simulation of the stochastic processes considered here. To benchmark our new framework, we first use it to quantify the effect of each source of heterogeneity on the intrinsic and the extrinsic phenotypic variability for the well-known two-promoter system used experimentally by Elowitz et al. (2002). We finally apply our framework to a more complicated system and demonstrate how the interplay between noisy gene expression and growth inhibition due to protein accumulation at the single cell level can result in complex behavior at the cell population level.The generality of our framework makes it suitable for studying a vast array of artificial and natural genetic networks. Using our Monte Carlo algorithm, cell population distributions can be predicted for the genetic architecture of interest, thereby quantifying the effect of stochasticity in intracellular reactions or the variability in the rate of physiological processes such as growth and division. Such in silico experiments can give insight into the behavior of cell populations and reveal the major sources contributing to cell population heterogeneity. (C) 2010 Elsevier Ltd. All rights reserved.