Combinatorial complexity and dynamical restriction of network flows in signal transduction.

J R Faeder, M L Blinov, B Goldstein, W S Hlavacek
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引用次数: 68

Abstract

The activities and interactions of proteins that govern the cellular response to a signal generate a multitude of protein phosphorylation states and heterogeneous protein complexes. Here, using a computational model that accounts for 307 molecular species implied by specified interactions of four proteins involved in signalling by the immunoreceptor FcepsilonRI, we determine the relative importance of molecular species that can be generated during signalling, chemical transitions among these species, and reaction paths that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of these measures and over two- and ten-fold ranges of model parameters--rate constants and initial concentrations--only a small portion of the biochemical network is active. The spectrum of active complexes, however, can be shifted dramatically, even by a change in the concentration of a single protein, which suggests that the network can produce qualitatively different responses under different cellular conditions and in response to different inputs. Reduced models that reproduce predictions of the full model for a particular set of parameters lose their predictive capacity when parameters are varied over two-fold ranges.

信号转导中网络流的组合复杂性和动态约束。
控制细胞对信号反应的蛋白质的活性和相互作用产生多种蛋白质磷酸化状态和异质蛋白质复合物。在这里,我们使用了一个计算模型,计算了免疫受体FcepsilonRI参与信号传导的四种蛋白质的特定相互作用所隐含的307种分子物种,确定了信号传导过程中可能产生的分子物种的相对重要性,这些物种之间的化学转变,以及导致蛋白酪氨酸激酶(PTK) Syk激活的反应途径。通过所有这些测量和超过两倍和十倍的模型参数范围——速率常数和初始浓度——只有一小部分生化网络是活跃的。然而,活性复合物的光谱甚至可以通过单个蛋白质浓度的变化而发生显著变化,这表明该网络可以在不同的细胞条件下和对不同输入的响应中产生定性不同的反应。对一组特定参数再现完整模型预测的简化模型在参数变化超过两倍范围时失去了预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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