Suppr超能文献

具有爆发、细胞周期和复制动态的随机基因表达模型的精确解。

Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics.

机构信息

Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.

Department of Mathematics, University College London, London WC1H 0AY, United Kingdom.

出版信息

Phys Rev E. 2020 Mar;101(3-1):032403. doi: 10.1103/PhysRevE.101.032403.

Abstract

The bulk of stochastic gene expression models in the literature do not have an explicit description of the age of a cell within a generation and hence they cannot capture events such as cell division and DNA replication. Instead, many models incorporate the cell cycle implicitly by assuming that dilution due to cell division can be described by an effective decay reaction with first-order kinetics. If it is further assumed that protein production occurs in bursts, then the stationary protein distribution is a negative binomial. Here we seek to understand how accurate these implicit models are when compared with more detailed models of stochastic gene expression. We derive the exact stationary solution of the chemical master equation describing bursty protein dynamics, binomial partitioning at mitosis, age-dependent transcription dynamics including replication, and random interdivision times sampled from Erlang or more general distributions; the solution is different for single lineage and population snapshot settings. We show that protein distributions are well approximated by the solution of implicit models (a negative binomial) when the mean number of mRNAs produced per cycle is low and the cell cycle length variability is large. When these conditions are not met, the distributions are either almost bimodal or else display very flat regions near the mode and cannot be described by implicit models. We also show that for genes with low transcription rates, the size of protein noise has a strong dependence on the replication time, it is almost independent of cell cycle variability for lineage measurements, and increases with cell cycle variability for population snapshot measurements. In contrast for large transcription rates, the size of protein noise is independent of replication time and increases with cell cycle variability for both lineage and population measurements.

摘要

文献中的大多数随机基因表达模型都没有明确描述细胞在一个世代内的年龄,因此它们无法捕捉到细胞分裂和 DNA 复制等事件。相反,许多模型通过假设细胞分裂导致的稀释可以用一阶动力学的有效衰减反应来描述,从而隐含地包含了细胞周期。如果进一步假设蛋白质的产生是爆发式的,那么静止态的蛋白质分布就是负二项式。在这里,我们试图了解与随机基因表达的更详细模型相比,这些隐含模型的准确性如何。我们推导出了描述爆发式蛋白质动力学、有丝分裂时的二项式划分、包括复制在内的依赖年龄的转录动力学以及从爱尔朗或更一般的分布中采样的随机分裂时间的化学主方程的精确静止态解;对于单谱系和群体快照设置,解是不同的。我们表明,当每个周期产生的 mRNA 平均数量较低且细胞周期长度变化较大时,隐含模型(负二项式)可以很好地近似蛋白质分布。当这些条件不满足时,分布要么几乎是双峰的,要么在模式附近显示非常平坦的区域,无法用隐含模型来描述。我们还表明,对于转录率较低的基因,蛋白质噪声的大小强烈依赖于复制时间,对于谱系测量,它几乎独立于细胞周期变化,而对于群体快照测量,它随细胞周期变化而增加。相比之下,对于转录率较大的基因,蛋白质噪声的大小独立于复制时间,并且对于谱系和群体测量,它随细胞周期变化而增加。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验