Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.
Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.
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DOI：
10.1016/j.ymeth.2015.06.009
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发表时间：
2015-09-01
期刊：
影响因子：
--
通讯作者：
Neuert G
中科院分区：
文献类型：
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作者：
Munsky B;Fox Z;Neuert G
RNA转录物的产生和降解固有地受到个体细胞中小基因拷贝数产生的生物噪声的影响。因此，细胞RNA水平可以随着时间的推移以及从一个细胞到下一个细胞表现出大的波动。本文介绍了一系列精确的单分子实验技术，基于RNA荧光原位杂交，可用于测量在单细胞水平的RNA的波动。一类模型的基因激活和失活的假设，以捕捉复杂的随机效应的染色质修饰或转录因子的相互作用。一个计算工具，已知的有限状态投影方法，被引入到准确和有效地分析这些模型，以预测RNA的概率分布如何随着时间的推移而变化，以响应不断变化的环境条件。这些单分子实验，离散随机模型，和计算分析系统集成，以确定模型的基因调控动力学。为了说明我们的综合实验和计算方法的能力和通用性，我们探索了包括三种不同RNA类型（sRNA，mRNA和新生RNA），三种不同实验技术和三种不同生物物种（细菌，酵母和人类细胞）的不同模型的情况。
The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known the Finite State Projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).