Predicting stochastic gene expression dynamics in single cells
Predicting stochastic gene expression dynamics in single cells
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DOI：
10.1073/pnas.0509874103
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发表时间：
2006-05-09
影响因子：
11.1
通讯作者：
van Oudenaarden, Alexander
中科院分区：
文献类型：
--
作者：
Mettetal, Jerome T.;Muzzey, Dale;van Oudenaarden, Alexander
由于单细胞中固有的随机效应，蛋白质数量的波动（噪声）可能对基因调控网络的动态行为产生很大影响。虽然确定性模型可以预测平均网络行为，但它们未能纳入基因表达的随机性特征，从而限制了单细胞行为偏离群体平均值时的相关性。最近，随机模型已被用来预测人口内的稳态蛋白质水平的分布，但不预测的动态，presteadystate分布。在目前的工作中，我们实验研究的系统，其动态的随机效应的影响很大。我们测量人口分布的蛋白质数量作为时间的函数在大肠杆菌乳糖摄取网络（乳糖操纵子）。然后，我们介绍了一个动态随机模型，并表明动态分布的预测只需要几个噪声参数，除了确定性模型的特征率。而确定性模型不能完全捕捉到观察到的行为，我们的随机模型正确地预测实验动力学没有任何合适的参数。我们的研究结果提供了一个原则的可能性，忠实地预测动态人口分布的确定性模型补充随机组件，捕捉主要的噪声源。
Fluctuations in protein numbers (noise) due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the population average. Recently, stochastic models have been used to predict distributions of steady-state protein levels within a population but not to predict the dynamic, presteadystate distributions. In the present work, we experimentally examine a system whose dynamics are heavily influenced by stochastic effects. We measure population distributions of protein numbers as a function of time in the Escherichia coli lactose uptake network (lac operon). We then introduce a dynamic stochastic model and show that prediction of dynamic distributions requires only a few noise parameters in addition to the rates that characterize a deterministic model. Whereas the deterministic model cannot fully capture the observed behavior, our stochastic model correctly predicts the experimental dynamics without any fit parameters. Our results provide a proof of principle for the possibility of faithfully predicting dynamic population distributions from deterministic models supplemented by a stochastic component that captures the major noise sources.