Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species : A model-based approach

The specification of biological decisions by signaling pathways is encoded by the interplay between activation dynamics and network topologies. While we can describe complex networks, we cannot easily determine which topology is actually realized to transduce a specific signal. Experimental testing of all plausible topologies is infeasible due to the combinatorially large number of experiments required to explore the complete hypothesis space. Here, it is demonstrated that Bayesian inference-based modeling provides a formal and systematic approach to explore and constrain this hypothesis space permitting the rational ranking of pathway models. Importantly, this approach can use measurements of a limited number of biochemical species when combined with multiple perturbations. As proof-of-concept the activation of the Extracellular signal regulated Kinase (ERK) pathway by Epidermal Growth Factor (EGF) was examined. The predicted and experimentally validated model shows that both Raf-1 and, unexpectedly, B-Raf are needed to fully activate ERK. Thus, this formal methodology rationally infers evidentially supported pathway topologies even when a limited amount of measurements is available.