AI 中文总结
研究转录后调控对随机基因表达中罕见事件的影响，采用泊松到达划分框架，将转录后调控一般模型映射到类似转录调控模型，得到通用框架，为分析相关罕见事件开辟新途径。
AI 中文摘要
基因表达是一个随机过程，会导致蛋白质水平的大幅波动，从而在克隆细胞群体中产生表型异质性；转录后调控在控制群体内表型变异性水平方面起着关键作用，这与细胞命运决定直接相关。因此，大量工作致力于定量建模各种转录后机制对蛋白质水平（噪声）波动强度的影响。然而，转录后调控对与大偏差相对应的罕见事件发生的相应影响却很少被探索，且仅在一个特殊模型中被考虑过。在此，我们采用转录后调控的一般模型，并应用泊松到达划分（PPA）框架将其映射到一个类似于基于启动子的转录调控模型上，从而得到一个通用框架，可直接从基于启动子的模型的先前结果中，为转录后调控模型获取大偏差中的感兴趣对象（即用于量化观察到罕见蛋白质产生速率可能性的大偏差率函数以及表征罕见事件条件下系统动态的相应驱动过程）。所得结果为分析与各种不同生物学环境相关的转录后调控一般模型中的罕见事件开辟了新途径。
英文摘要
Gene expression is a stochastic process that gives rise to large fluctuations in protein levels leading to phenotypic heterogeneity in clonal cell populations; post-transcriptional regulation plays a crucial role in controlling the level of phenotypic variability within a population, which is directly tied to cell-fate decisions. As such, substantial efforts have been directed towards quantitatively modeling the effects of various post-transcriptional mechanisms on the strength of fluctuations in protein levels (noise). However, the corresponding effects of post-transcriptional regulation on the occurrence of rare events corresponding to large deviations are far less explored and have only been considered for a special model. Here, we take a general model of post-transcriptional regulation and apply the partitioning of Poisson arrivals (PPA) framework to map it onto a model that resembles promoter-based regulation of transcription, leading to a general framework to obtain objects of interest in large deviations (i.e. large deviation rate function for quantifying the likelihood of observing rare protein production rates and the corresponding driven process that characterizes the system dynamics conditional on the rare event) for models of post-transcriptional regulation directly from prior results for promoter-based models. The results derived create new avenues to analyze rare events in general models of post-transcriptional regulation pertaining to various different biological settings.
Comments7 pages, 5 figures, submitted to Physical Biology
