NF-kappaB信号通路振荡的协同控制。

A E C Ihekwaba, D S Broomhead, R Grimley, N Benson, M R H White, D B Kell
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引用次数: 57

摘要

在之前的工作中,我们研究了NF-kappaB信号通路部分模型的行为。随着模型参数的变化,模型振荡的数量、幅度和频率都发生了变化。灵敏度分析表明,当这些参数单独变化时,64个反应参数中只有9个主要负责控制振荡。然而,任何复杂系统的特性控制都是分布的,而且,由于许多这些反应是高度非线性的,我们期望它们的相互作用也是如此。对这9个参数进行两两调制的搜索空间(81对4096)比对所有64个反应进行两两调制所需的搜索空间小了约50倍,这使得他们的研究得以进行(否则的话,这将是非常棘手的)。观察到显著的协同效应,其中一个参数的影响强烈(甚至定性)依赖于另一个参数的值。在参数空间的区域中,振荡的振幅变化,而频率(定时)变化,反之亦然。这种模型将允许设计和执行旨在解开振荡动力学的作用的实验,而不仅仅是它们的振幅,在决定细胞命运。总的来说,这些分析揭示了这些动态模型的复杂性,而仅仅从它们的单个参数的研究中并不明显,并指出了操纵复杂网络的多个元素以实现所需生理效果的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synergistic control of oscillations in the NF-kappaB signalling pathway.

In previous work, we studied the behaviour of a model of part of the NF-kappaB signalling pathway. The model displayed oscillations that varied both in number, amplitude and frequency when its parameters were varied. Sensitivity analysis showed that just nine of the 64 reaction parameters were mainly responsible for the control of the oscillations when these parameters were varied individually. However, the control of the properties of any complex system is distributed, and, as many of these reactions are highly non-linear, we expect that their interactions will be too. Pairwise modulation of these nine parameters gives a search space some 50 times smaller (81 against 4096) than that required for the pairwise modulation of all 64 reactions, and this permitted their study (which would otherwise have been effectively intractable). Strikingly synergistic effects were observed, in which the effect of one of the parameters was strongly (and even qualitatively) dependent on the values of another parameter. Regions of parameter space could be found in which the amplitude, but not the frequency (timing), of oscillations varied, and vice versa. Such modelling will permit the design and performance of experiments aimed at disentangling the role of the dynamics of oscillations, rather than simply their amplitude, in determining cell fate. Overall, the analyses reveal a level of complexity in these dynamic models that is not apparent from study of their individual parameters alone and point to the value of manipulating multiple elements of complex networks to achieve desired physiological effects.

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