Choubey Sandeep
FAS Center for Systems Biology and Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
Phys Rev E. 2018 Feb;97(2-1):022402. doi: 10.1103/PhysRevE.97.022402.
Regulation of transcription is a vital process in cells, but mechanistic details of this regulation still remain elusive. The dominant approach to unravel the dynamics of transcriptional regulation is to first develop mathematical models of transcription and then experimentally test the predictions these models make for the distribution of mRNA and protein molecules at the individual cell level. However, these measurements are affected by a multitude of downstream processes which make it difficult to interpret the measurements. Recent experimental advancements allow for counting the nascent mRNA number of a gene as a function of time at the single-cell level. These measurements closely reflect the dynamics of transcription. In this paper, we consider a general mechanism of transcription with stochastic initiation and deterministic elongation and probe its impact on the temporal behavior of nascent RNA levels. Using techniques from queueing theory, we derive exact analytical expressions for the mean and variance of the nascent RNA distribution as functions of time. We apply these analytical results to obtain the mean and variance of nascent RNA distribution for specific models of transcription. These models of initiation exhibit qualitatively distinct transient behaviors for both the mean and variance which further allows us to discriminate between them. Stochastic simulations confirm these results. Overall the analytical results presented here provide the necessary tools to connect mechanisms of transcription initiation to single-cell measurements of nascent RNA.
转录调控是细胞中的一个重要过程,但其调控的机制细节仍然难以捉摸。揭示转录调控动态的主要方法是首先建立转录的数学模型,然后在单个细胞水平上通过实验检验这些模型对mRNA和蛋白质分子分布所做的预测。然而,这些测量受到众多下游过程的影响,这使得对测量结果的解释变得困难。最近的实验进展使得能够在单细胞水平上统计一个基因的新生mRNA数量随时间的变化。这些测量结果密切反映了转录的动态过程。在本文中,我们考虑一种具有随机起始和确定性延伸的转录通用机制,并探究其对新生RNA水平时间行为的影响。利用排队论技术,我们推导出了新生RNA分布的均值和方差作为时间函数的精确解析表达式。我们应用这些解析结果来获得特定转录模型的新生RNA分布的均值和方差。这些起始模型在均值和方差方面都表现出定性上截然不同的瞬态行为,这进一步使我们能够区分它们。随机模拟证实了这些结果。总体而言,本文给出的解析结果提供了必要的工具,将转录起始机制与新生RNA的单细胞测量联系起来。