Delay-induced stochastic oscillations in gene regulation
Delay-induced stochastic oscillations in gene regulation
复制标题
DOI：
10.1073/pnas.0503858102
复制
发表时间：
2005-10-11
影响因子：
11.1
通讯作者：
Hasty, J
中科院分区：
文献类型：
--
作者：
Bratsun, D;Volfson, D;Hasty, J
参与基因调控的少量反应物分子可导致细胞内mRNA和蛋白质浓度的显著波动，最近有许多研究致力于研究这种噪音在调控水平上的后果。关于随机基因表达的理论和计算工作倾向于关注瞬时转录和翻译事件，而实际延迟时间在这些随机过程中的作用很少受到关注。在这里，我们探讨了时间延迟和本征噪声对基因调控的联合影响。从一组生化反应开始，其中一些是延迟的，我们推导出反应系统的截断主方程，并推导出相关函数和功率谱的解析表达式。我们开发了一个广义的Gillespie算法，该算法解释了随机生物化学事件的非马尔可夫性质，并将我们的分析结果与模拟进行了比较。我们展示了基因表达的时间延迟如何导致系统振荡，即使它的确定性对应物没有振荡。我们展示了这种延迟引起的不稳定性如何损害负反馈回路减少噪声有害影响的能力。鉴于负反馈在基因调控中的普遍存在，我们的发现可能会导致与全基因组尺度上的表达变异性相关的新见解。
The small number of reactant molecules involved in gene regulation can lead to significant fluctuations in intracellular mRNA and protein concentrations, and there have been numerous recent studies devoted to the consequences of such noise at the regulatory level. Theoretical and computational work on stochastic gene expression has tended to focus on instantaneous transcriptional and translational events, whereas the role of realistic delay times in these stochastic processes has received little attention. Here, we explore the combined effects of time delay and intrinsic noise on gene regulation. Beginning with a set of biochemical reactions, some of which are delayed, we deduce a truncated master equation for the reactive system and derive an analytical expression for the correlation function and power spectrum. We develop a generalized Gillespie algorithm that accounts for the non-Markovian properties of random biochemical events with delay and compare our analytical findings with simulations. We show how time delay in gene expression can cause a system to be oscillatory even when its deterministic counterpart exhibits no oscillations. We demonstrate how such delay-induced instabilities can compromise the ability of a negative feedback loop to reduce the deleterious effects of noise. Given the prevalence of negative feedback in gene regulation, our findings may lead to new insights related to expression variability at the whole-genome scale.