Combining phospho- (and other) proteomics with biological knowledge into computational models to understand signal transduction

Proteomics in its various forms – from phosphor-proteomics to thermal proteome profiling - provides rich information about cellular processes and their deregulation in disease. Given the complexity of the data, we need computational methods to effectively extract mechanistic information from it. We believe that including biological knowledge in the methods can be instrumental to move from pure correlation to causation in large data sets, and thereby identify the molecular processes that underlie signaling events, that can be ultimately used as therapeutic targets and biomarkers. In this talk I will describe various approaches that follow this strategy, from kinase-activity estimation to signaling network reconstruction, and illustrate them in examples from various contexts.