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致癌突变与细胞凋亡途径模型参数敏感性之间的相关性。

Correlation between oncogenic mutations and parameter sensitivity of the apoptosis pathway model.

作者信息

Chen Jia, Yue Haicen, Ouyang Qi

机构信息

Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences at Peking University, Beijing, China.

Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences at Peking University, Beijing, China ; School of Physics and the State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, Peking University, Beijing, China.

出版信息

PLoS Comput Biol. 2014 Jan;10(1):e1003451. doi: 10.1371/journal.pcbi.1003451. Epub 2014 Jan 23.

DOI:10.1371/journal.pcbi.1003451
PMID:24465201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3900373/
Abstract

One of the major breakthroughs in oncogenesis research in recent years is the discovery that, in most patients, oncogenic mutations are concentrated in a few core biological functional pathways. This discovery indicates that oncogenic mechanisms are highly related to the dynamics of biologic regulatory networks, which govern the behaviour of functional pathways. Here, we propose that oncogenic mutations found in different biological functional pathways are closely related to parameter sensitivity of the corresponding networks. To test this hypothesis, we focus on the DNA damage-induced apoptotic pathway--the most important safeguard against oncogenesis. We first built the regulatory network that governs the apoptosis pathway, and then translated the network into dynamics equations. Using sensitivity analysis of the network parameters and comparing the results with cancer gene mutation spectra, we found that parameters that significantly affect the bifurcation point correspond to high-frequency oncogenic mutations. This result shows that the position of the bifurcation point is a better measure of the functionality of a biological network than gene expression levels of certain key proteins. It further demonstrates the suitability of applying systems-level analysis to biological networks as opposed to studying genes or proteins in isolation.

摘要

近年来肿瘤发生研究的重大突破之一是发现,在大多数患者中,致癌突变集中在少数几个核心生物功能通路中。这一发现表明致癌机制与生物调控网络的动态变化高度相关,而生物调控网络控制着功能通路的行为。在此,我们提出在不同生物功能通路中发现的致癌突变与相应网络的参数敏感性密切相关。为了验证这一假设,我们聚焦于DNA损伤诱导的凋亡通路——预防肿瘤发生的最重要保障。我们首先构建了调控凋亡通路的网络,然后将该网络转化为动力学方程。通过对网络参数进行敏感性分析并将结果与癌症基因突变谱进行比较,我们发现显著影响分歧点的参数对应于高频致癌突变。这一结果表明,与某些关键蛋白的基因表达水平相比,分歧点的位置是衡量生物网络功能的更好指标。它进一步证明了将系统水平分析应用于生物网络的适用性,而不是孤立地研究基因或蛋白质。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccd8/3900373/c9b0fbce7915/pcbi.1003451.g008.jpg
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2
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Cell Death Differ. 2012 Apr;19(4):661-70. doi: 10.1038/cdd.2011.138. Epub 2011 Oct 21.
3
Decision making of the p53 network: death by integration.p53网络的决策:整合导致死亡
线粒体凋亡途径随机模型的参数敏感性分析。
PLoS One. 2018 Jun 18;13(6):e0198579. doi: 10.1371/journal.pone.0198579. eCollection 2018.
4
Revealing determinants of two-phase dynamics of P53 network under gamma irradiation based on a reduced 2D relaxation oscillator model.基于简化二维弛豫振荡器模型揭示γ射线辐照下P53网络两相动力学的决定因素。
IET Syst Biol. 2018 Feb;12(1):26-38. doi: 10.1049/iet-syb.2017.0041.
5
Synonymous mutations in oncogenesis and apoptosis versus survival unveiled by network modeling.通过网络建模揭示的肿瘤发生、凋亡与生存中的同义突变
Oncotarget. 2016 Jun 7;7(23):34599-616. doi: 10.18632/oncotarget.8963.
6
Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis.突变诱导的蛋白质相互作用动力学变化影响凋亡网络动态特性并促进肿瘤发生。
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7
Cooperation between Noncanonical Ras Network Mutations.非经典Ras网络突变之间的合作
Cell Rep. 2015 Jan 20;10(3):307-316. doi: 10.1016/j.celrep.2014.12.035. Epub 2015 Jan 15.
J Theor Biol. 2011 Feb 21;271(1):205-11. doi: 10.1016/j.jtbi.2010.11.041. Epub 2010 Dec 3.
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5
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8
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