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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species

Xu, Tian-Rui and Vyshemirsky, Vladislav and Gormand, Amelie and von Kriegsheim, Alex and Girolami, Mark and Baillie, G.S. and Ketley, Dominic and Dunlop, Allan J. and Milligan, G. and Houslay, Miles D. and Kolch, Walter (2010) Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Science signaling, 3 (113). ra20.

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Abstract

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.