NF -κB信号网络中噪声驱动响应的统计系综分析
Novel statistical ensemble analysis for simulating extrinsic noise-driven response in NF-κB signaling network
摘要：
由于受外在和内在噪声的强烈影响，单细胞的细胞反应具有高度的异质性和个体性。在这里，我们关心的是如何在实验获得的群体平均响应的约束下，但没有太多详细的动力学信息的外部噪声诱导的单细胞的异质性响应建模。我们提出了一种新的统计系综方案，其中外部噪声被视为动力学参数值的波动，这种波动通过从均匀分布中随机采样动力学速率常数来建模。我们考虑了大量的信号系统的复制，其中每一个具有相同的网络拓扑结构，但一个独特的不同的动力学速率常数集。每个重复的蛋白质动态响应应该代表单个细胞中的动态，并且统计系综平均值应该被视为细胞群体平均的群体水平响应。我们设计了一个优化算法，以找到正确的均匀分布的网络参数，产生正确的统计分布的响应，其总体平均值和分布符合人口水平的实验数据和实验观察到的异质性。我们将这种统计系综分析应用于NF-κB信号系统，并（1）预测异质NF-κB（振荡或非振荡）动态模式和动态特征（例如，（2）预测NF-κB动态反应的分布和统计学总体平均值均敏感地依赖于刺激剂的剂量;（3）从统计学总体平均值和单个重复实验中证实了S形剂量反应。
英文摘要：
Cellular responses in the single cells are known to be highly heterogeneous and individualistic due to the strong influence by extrinsic and intrinsic noise. Here, we are concerned about how to model the extrinsic noise-induced heterogeneous response in the single cells under the constraints of experimentally obtained population-averaged response, but without much detailed kinetic information. We propose a novel statistical ensemble scheme where extrinsic noise is regarded as fluctuations in the values of kinetic parameters and such fluctuations are modeled by randomly sampling the kinetic rate constants from a uniform distribution. We consider a large number of signaling system replicates, each of which has the same network topology, but a uniquely different set of kinetic rate constants. A protein dynamic response from each replicate should represent the dynamics in a single cell and the statistical ensemble average should be regarded as a population-level response averaged over a population of the cells. We devise an optimization algorithm to find the correct uniform distribution of the network parameters, which produces the correct statistical distribution of the response whose ensemble average and distribution agree well with the population-level experimental data and the experimentally observed heterogeneity. We apply this statistical ensemble analysis to a NF-κB signaling system and (1) predict the distributions of the heterogeneous NF-κB (either oscillatory or non-oscillatory) dynamic patterns and of the dynamic features (e.g., period), (2) predict that both the distribution and the statistical ensemble average of the NF-κB dynamic response depends sensitively on the dosage of stimulant, and lastly (3) demonstrate the sigmoidally shaped dose-response from the statistical ensemble average and the individual replicates.
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