Combined logical and data-driven models for linking signalling pathways to cellular response

In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.Construction of signalling pathways is a major endeavour in biology. Signalling cascades, starting at the receptor level, orchestrate a variety of normal or pathological responses via a complex network of kinases, adaptor molecules, and other signalling proteins [1]. Several gene- and protein-based approaches have emerged for elucidating the complex intracellular signalling activity. Gene-based analysis has the advantage of whole genome exploration [2-4] whereas proteomic approaches are applicable on small pathways but with a more reliable view of pathway function, since proteins are the ultimate reporters of cellular activity [5,6]. Both approaches