Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.
Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.
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DOI：
10.1371/journal.pcbi.1000256
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发表时间：
2009-01
影响因子：
4.3
通讯作者：
Sengupta AM
中科院分区：
文献类型：
--
作者：
Dayarian A;Chaves M;Sontag ED;Sengupta AM
在过去十年中，监管网络稳健性的概念受到了广泛关注。鲁棒性的一种度量与可行区域的体积有关，即系统在参数空间中的功能区域。在本文中，我们表明，除了体积之外，该区域的几何形状对网络的鲁棒性和脆弱性也有重要影响。我们发展了一个近似，在这个近似内我们可以用代数方法指定可行域。我们分析了片段极性基因网络来说明我们的方法。对参数空间中的随机游走及其如何退出可行区域的研究，为我们提供了一个丰富的视角来研究该网络模型的不同失效模式。特别是，我们发现，在两种激活无翼的方法中，一种比另一种更坚固。我们的方法提供了对参数变化的更完整的鲁棒性度量。作为一种通用的建模策略，我们的方法是生化网络布尔表示的一种有趣的替代方法。开发具有大量参数的模型来描述生化网络的动态是当今常见的练习。这种网络模型的预测依赖于参数的选择，理解这一点很重要，原因有二。为了拟合生物数据并进行预测，我们需要知道哪些参数组合受到观测结果的强烈约束，以及哪些组合严重影响特定的预测。此外，我们期望自然进化的网络对参数变化具有一定的鲁棒性。如果网络的功能需要对许多参数进行微调，那么引起调节相互作用变化的突变可能很快使网络功能失调。对于涉及基因产物是ON或OFF的预测，我们发现了一种促进研究参数依赖的方法。以果蝇为例，分析了几种相互竞争的片段极性网络模型。我们在参数空间中明确描述了关键基因的野生型表达模式对每个模型可行的区域。我们还研究了参数空间中的随机漫步如何从网络模型的可行区域退出，使我们能够比较替代模型的相对鲁棒性。
The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks. Developing models with a large number of parameters for describing the dynamics of a biochemical network is a common exercise today. The dependence of predictions of such a network model on the choice of parameters is important to understand for two reasons. For the purpose of fitting biological data and making predictions, we need to know which combinations of parameters are strongly constrained by observations and also which combinations seriously affect a particular prediction. In addition, we expect naturally evolved networks to be somewhat robust to parameter changes. If the functioning of the network requires fine-tuning in many parameters, then mutations causing changes in regulatory interactions could quickly make the network dysfunctional. For predictions involving gene products being ON or OFF, we found a method that facilitates the study parameter dependence. As an example, we analyzed several competing models of the segment polarity network in Drosophila. We explicitly describe the region in the parameter space where the wild-type expression pattern of key genes becomes feasible for each model. We also study how random walks in the parameter space exit from the feasible region of a network model, allowing us to compare the relative robustness of the alternative models.
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影响因子：
64.8
作者：
Alon, U;Surette, MG;Leibler, S
通讯作者：
Leibler, S
影响因子：
10.5
作者：
CADIGAN, KM;GROSSNIKLAUS, U;GEHRING, WJ
通讯作者：
GEHRING, WJ
DOI：
10.1049/ip-syb:20050079
发表时间：
2006-07-01
期刊：
IEE PROCEEDINGS SYSTEMS BIOLOGY
影响因子：
--
作者：
Chaves, M.;Sontag, E. D.;Albert, R.
通讯作者：
Albert, R.
DOI：
10.1073/pnas.022388499
发表时间：
2002-02-19
影响因子：
11.1
作者：
Sengupta, AM;Djordjevic, M;Shraiman, BI
通讯作者：
Shraiman, BI
影响因子：
2
作者：
Brown, KS;Hill, CC;Cerione, RA
通讯作者：
Cerione, RA