AI 中文总结
研究随机基因表达中，转录后调控如何影响基因表达自然变异。利用泊松到达分区特性映射模型，得出蛋白质分布矩精确结果，纳入转录爆发扩展框架，形成通用统一分析框架用于分析转录后调控。
AI 中文摘要
基因表达是一个随机过程，会导致蛋白质水平波动，进而在基因相同的细胞群体中产生表型异质性。因此，人们非常关注量化基因表达中的自然变异（噪声）如何受到细胞控制机制的影响，比如与转录后调控相关的各种机制。尽管之前的研究已经开发出一个通用分析框架来计算任何基于启动子的调控基序的mRNA分布的精确矩，以及在某些情况下精确的mRNA分布本身，但目前还缺乏一个类似的蛋白质波动框架。在此，我们利用泊松到达的分区特性，将一类通用的转录后调控随机模型映射到类似于基于启动子调控的模型上。这种方法利用已知的任意基于启动子调控的mRNA分布的精确结果，得出蛋白质分布矩的精确分析结果，在某些情况下还能得出完整分布本身。我们进一步扩展该框架以纳入转录爆发，从而得到一个通用的、统一的分析框架，用于分析随机基因表达中的转录后调控。
英文摘要
Gene expression is a stochastic process that allows for fluctuations in protein levels that can give rise to phenotypic heterogeneity within a population of genetically identical cells. Thus, there is great interest in quantifying how natural variation (noise) in gene expression is impacted by cellular control mechanisms, such as the various mechanisms pertaining to post-transcriptional regulation. Although previous research has developed a general analytical framework to compute the exact moments of mRNA distributions for any promoter-based regulatory motif, and the exact mRNA distribution itself in some cases, a similar framework for protein fluctuations is currently lacking. Here, we invoke the partitioning property of Poisson arrivals to map a general class of stochastic models of post-transcriptional regulation onto models that resemble promoter-based regulation. This approach leads to exact analytical results for the moments of protein distributions, and in certain cases the full distribution itself, using known exact results for mRNA distributions undergoing arbitrary promoter-based regulation. We further extend the framework to incorporate transcriptional bursting, leading to a versatile, unifying analytical framework for analyzing post-transcriptional regulation in stochastic gene expression.
Comments13 pages, 6 figures, submitted to Physical Review E
