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快速切换条件下自调节基因的动力学相图。

Dynamical phase diagram of an auto-regulating gene in fast switching conditions.

机构信息

Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, China.

School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.

出版信息

J Chem Phys. 2020 May 7;152(17):174110. doi: 10.1063/5.0007221.

Abstract

While the steady-state behavior of stochastic gene expression with auto-regulation has been extensively studied, its time-dependent behavior has received much less attention. Here, under the assumption of fast promoter switching, we derive and solve a reduced chemical master equation for an auto-regulatory gene circuit with translational bursting and cooperative protein-gene interactions. The analytical expression for the time-dependent probability distribution of protein numbers enables a fast exploration of large swaths of the parameter space. For a unimodal initial distribution, we identify three distinct types of stochastic dynamics: (i) the protein distribution remains unimodal at all times; (ii) the protein distribution becomes bimodal at intermediate times and then reverts back to being unimodal at long times (transient bimodality); and (iii) the protein distribution switches to being bimodal at long times. For each of these, the deterministic model predicts either monostable or bistable behavior, and hence, there exist six dynamical phases in total. We investigate the relationship of the six phases to the transcription rates, the protein binding and unbinding rates, the mean protein burst size, the degree of cooperativity, the relaxation time to the steady state, the protein mean, and the type of feedback loop (positive or negative). We show that transient bimodality is a noise-induced phenomenon that occurs when the protein expression is sufficiently bursty, and we use a theory to estimate the observation time window when it is manifested.

摘要

虽然具有自动调节的随机基因表达的稳态行为已经得到了广泛研究,但它的时变行为却受到了较少关注。在这里,我们假设快速启动子转换,推导出并求解了具有翻译突发和协同蛋白-基因相互作用的自动调节基因回路的简化化学主方程。蛋白数量的时变概率分布的解析表达式能够快速探索参数空间的大部分区域。对于单峰初始分布,我们确定了三种不同类型的随机动力学:(i)蛋白分布在所有时间都保持单峰;(ii)蛋白分布在中间时间变为双峰,然后在长时间后恢复为单峰(瞬时双峰);(iii)蛋白分布在长时间后变为双峰。对于每一种情况,确定性模型预测要么是单稳态要么是双稳态行为,因此,总共存在六种动力学相。我们研究了这六种相与转录率、蛋白结合和解离率、平均蛋白爆发大小、协同度、向稳态的弛豫时间、蛋白平均值以及反馈环类型(正或负)之间的关系。我们表明,瞬时双峰是一种由噪声引起的现象,当蛋白表达足够突发时就会发生,并且我们使用一种理论来估计它表现出来的观测时间窗口。

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