Generalized analysis of experimental data for interrelated biological measurements

Important biological mechanisms, such as signal transduction and gene expression, are mediated by numerous interacting multifunctional molecules, whose expression and activation are tightly regulated in space and time in response to stimuli. In order to describe the network of functional inter-relationships that govern such mechanisms, we use simple algorithms to interpret multiple variable measurements, identify the prominent participants, evaluate their interactions and obtain a 'functional fingerprint' of cell behaviour. Dynamic measurements of responses yield hierarchical information about causal relations in the underlying pathway. As a proof of principles we apply this approach to phosphorylation assays in protein gels, probing hormone and insulin signalling.