论文标题
基因启动子动力学的详细模型揭示了进入生产性伸长的过程是一个高度守时的过程
A detailed model of gene promoter dynamics reveals the entry into productive elongation to be a highly punctual process
论文作者
论文摘要
基因转录是一个随机过程，涉及数千种反应。这些反应发生在基因启动子附近，被认为是随机噪声中最重要的。最常见的转录模型主要与激活剂/阻遏物对总体转录率的影响有关，并将基础转录过程近似为一步事件。根据这种有效的模型，mRNA复制分布的FANO因子总是大于（超级波多州人）或等于1（poissonian），而唯一的方法是通过负反馈来以低于此极限（亚poissonian）。部分原因是该限制是转录的第一阶段是负责mRNA拷贝数中大多数随机噪声的原因。但是，通过考虑建立和推动基础转录机械的所有主要反应，从将一个将一个蛋白质结合到转录复合物（TC）的入口到生产性伸长延伸，这表明转录的前两个阶段，即启动前的构造（PIC）形成（PIC）形成和启动子ppausis（PPP），是一个高度uncutial的过程。换句话说，该过程的第一步和最后一步之间的时间狭窄，这为输入生产性延伸的TC数量引起了次福斯顿的分布。实际上，在使用2000个不同的参数集和4个不同的反应网络拓扑结构通过吉莱斯皮算法模拟了PIC组和PPP后，只有4.4％的FANO因子> 1> 1，上限为1.7，而31％的FANO因子低于0.5，为0.19，为0.19。这些结果使人们对mRNA分布中观察到的大多数随机噪声的概念始终起源于启动子。
Gene transcription is a stochastic process that involves thousands of reactions. The first set of these reactions, which happen near a gene promoter, are considered to be the most important in the context of stochastic noise. The most common models of transcription are primarily concerned with the effect of activators/repressors on the overall transcription rate and approximate the basal transcription processes as a one step event. According to such effective models, the Fano factor of mRNA copy distributions is always greater than (super-Poissonian) or equal to 1 (Poissonian), and the only way to go below this limit (sub-Poissonian) is via a negative feedback. It is partly due to this limit that the first stage of transcription is held responsible for most of the stochastic noise in mRNA copy numbers. However, by considering all major reactions that build and drive the basal transcription machinery, from the first protein that binds a promoter to the entrance of the transcription complex (TC) into productive elongation, it is shown that the first two stages of transcription, namely the pre-initiation complex (PIC) formation and the promoter proximal pausing (PPP), is a highly punctual process. In other words, the time between the first and the last step of this process is narrowly distributed, which gives rise to sub-Poissonian distributions for the number of TCs that have entered productive elongation. In fact, having simulated the PIC formation and the PPP via the Gillespie algorithm using 2000 distinct parameter sets and 4 different reaction network topologies, it is shown that only 4.4% give rise to a Fano factor that is > 1 with the upper bound of 1.7, while for 31% of cases the Fano factor is below 0.5, with 0.19 as the lower bound. These results cast doubt on the notion that most of the stochastic noise observed in mRNA distributions always originates at the promoter.
