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从结构到动力学:p53-Mdm2 网络中的频率调谐。二、微分和随机方法。

From structure to dynamics: frequency tuning in the p53-Mdm2 network. II Differential and stochastic approaches.

机构信息

Université Libre de Bruxelles (U.L.B.), Faculté des Sciences, Unit of Theoretical and Computational Biology, Campus Plaine C.P. 231, B-1050 Brussels, Belgium.

出版信息

J Theor Biol. 2010 Jun 21;264(4):1177-89. doi: 10.1016/j.jtbi.2010.03.031. Epub 2010 Mar 25.

Abstract

In Part I of this work, we carried out a logical analysis of a simple model describing the interplay between protein p53, its main negative regulator Mdm2 and DNA damage, and briefly discussed the corresponding differential model (Abou-Jaoudé et al., 2009). This analysis allowed us to reproduce several qualitative features of the kinetics of the p53 response to damage and provided an interpretation of the short and long characteristic periods of oscillation reported by Geva-Zatorsky et al. (2006) depending on the irradiation dose. Starting from this analysis, we focus here on more quantitative aspects of the dynamics of our network and combine the differential description of our system with stochastic simulations which take molecular fluctuations into account. We find that the amplitude of the p53 and Mdm2 oscillations is highly variable (to a degree that depends, however, on the bifurcation properties of the system). In contrast, peak width and timing remain more regular, consistent with the experimental data. Our simulations also show that noise can induce repeated pulses of p53 and Mdm2 that, at low damage, resemble the slow irregular fluctuations observed experimentally. Adding the stochastic dimension in our modeling further allowed us to account for an increase of the fraction of cells oscillating with a high frequency when the irradiation dose increases, as observed by Geva-Zatorsky et al. (2006).

摘要

在这项工作的第一部分中,我们对一个简单的模型进行了逻辑分析,该模型描述了蛋白质 p53、其主要负调节剂 Mdm2 和 DNA 损伤之间的相互作用,并简要讨论了相应的微分模型(Abou-Jaoudé 等人,2009 年)。该分析使我们能够再现 p53 对损伤的反应动力学的几个定性特征,并根据 Geva-Zatorsky 等人(2006 年)报告的照射剂量,对短和长特征振荡周期提供了一种解释。从这个分析开始,我们在这里关注我们网络动态的更定量方面,并将我们系统的微分描述与随机模拟相结合,这些模拟考虑了分子波动。我们发现,p53 和 Mdm2 振荡的幅度高度可变(取决于系统的分岔性质)。相比之下,峰宽和定时保持更规则,与实验数据一致。我们的模拟还表明,噪声可以诱导 p53 和 Mdm2 的重复脉冲,在低损伤时,这些脉冲类似于实验中观察到的缓慢不规则波动。在我们的建模中加入随机维度,还使我们能够解释当照射剂量增加时,高频振荡的细胞分数增加,正如 Geva-Zatorsky 等人(2006 年)所观察到的那样。

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