BowTieBuilder: modeling signal transduction pathways

Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins. Given protein-protein interaction (PPI) data signaling pathways are assembled without knowledge of the intermediate signaling proteins while maximizing the overall probability of the pathway. To assess the inference quality, BowTieBuilder and three alternative heuristics are applied to several pathways, and the resulting pathways are compared to reference pathways taken from KEGG. In addition, BowTieBuilder is used to infer a signaling pathway of the innate immune response in humans and a signaling pathway that potentially regulates an underlying gene regulatory network.We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.Most signal transduction events are initialized by cell-surface proteins that respond to specific environmental stimuli. When activated these proteins emanate a signaling cascade which involves a series of (de)-phosphorylation events. In many cases such signaling events transduce the signal to transcription factors (TFs), which in turn regulate the expression level of downstream genes. Understanding this cellular processing of information, from the source proteins (e.g., cell-surface proteins) to the target proteins (e.g., TFs), is important when generating comprehensive models of regulatory networks. For several biological processes the signaling pathway has been derived experimentally [1,2]. However, a large number of complex signaling pathways are yet to be discovered. To unravel these, computational inference methods are a valuable tool.The basis for the computational inference of novel signaling pathways are protein-protein interaction (PPI) datasets. These datasets are derived from biological studies on individual PPIs, but recently also by large-scale genomic, proteomic, and bioinformatic analyses. The