Intracellular signalling networks are robust due to feedback mechanisms and cross talk between pathways. Rewiring or short-circuiting this network within the insulin signaling pathway can lead to insulin resistance. This is characterized by a reduced cellular response to insulin, a hallmark of of type 2 diabetes. Although some important nodes of the insulin signaling network are known, they do not explain insulin resistance. Thus, we wish to better understand how insulin mediates its effects upon the cell by building up the insulin signalling network a priori.  
To address this, we have previously performed a phosphoproteomic screen investigating insulin action over time in adipocytes (fat cells). Previous analysis has included clustering and machine learning to predict novel substrates of the three major kinases (protein signalling hubs) Akt, mTOR and PKA. Here, we extend this study, using statistical, mathematical and computational techniques to build up the insulin signalling network a priori. This is done by combining the phosphorylation time-course data with: (1) experimentally-validated kinase-substrate interactions, from databases and the literature; and (2) predicted interactions (e.g., from protein-protein interaction studies, consensus sequences). This will enable us to perform a more statistically rigorous analysis with improved predictive power. Our preliminary results suggest involvement of kinases (e.g. G protein coupled receptor kinase) that have not been associated with the insulin signalling pathway before. Furthermore, our data shows that proteins are phosphorylated at different time points depending on their kinase and intrinsic properties such as location, sequence motif and abundance.
We aim to develop new mathematical and computational techniques to assign kinases to phosphorylation events in the insulin signalling network. This will lead to a better understanding of the mechanisms driving kinase action and discovery of potential therapeutic targets for overcoming type 2 diabetes.