The specification of biological decisions by signaling pathways is encoded by the interplay between activation dynamics and network topologies. Although we can describe complex networks, we cannot easily determine which topology the cell actually uses to transduce a specific signal. Experimental testing of all plausible topologies is infeasible because of the combinatorially large number of experiments required to explore the complete hypothesis space. Here, we demonstrate that Bayesian inference-based modeling provides an approach to explore and constrain this hypothesis space,permitting the rational ranking of pathway models. Our approach can use measurements of a limited number of biochemical species when combined with multiple perturbations. As proof of concept, we examined the activation of the extracellular signal-regulated kinase (ERK) pathway by epidermal growth factor. The predicted and experimentally validated model shows that both Raf-1 and, unexpectedly,B-Raf are needed to fully activate ERK in two different cell lines. Thus, our formal methodology rationally infers evidentially supported pathway topologies even when a limited number of biochemical and kinetic measurements are available.

中文翻译：

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从特定生化物种的多重扰动测量推断信号通路拓扑。

信号通路对生物决策的规范是由激活动力学和网络拓扑之间的相互作用编码的。尽管我们可以描述复杂的网络，但我们无法轻易确定单元实际使用哪种拓扑来转换特定信号。由于探索完整的假设空间需要大量的实验组合，因此对所有合理拓扑进行实验测试是不可行的。在这里，我们证明了基于贝叶斯推理的建模提供了一种探索和约束这个假设空间的方法，允许对路径模型进行合理排序。当与多个扰动相结合时，我们的方法可以使用有限数量的生化物种的测量。作为概念证明，我们检查了表皮生长因子对细胞外信号调节激酶 (ERK) 通路的激活。预测和实验验证的模型表明，在两种不同的细胞系中，Raf-1 和 B-Raf 都需要完全激活 ERK。因此，即使在可用的生化和动力学测量数量有限的情况下，我们的正式方法也能合理地推断出有证据支持的通路拓扑。