KiRNet: Integrated, Kinase-Centered Network Modeling Predicts Mechanisms Behind Phenotypic Associations

The ever-increasing size and scale of biological information have created a need for systems-level tools that synthesize large quantities of dispersed and distinct data and inform decision- and hypothesis-making processes. To address this need, we have created KiRNet, a kinase-centered method designed to integrate results of functional screens with protein-protein interaction data, and additional molecular data. KiRNet produces functional, network-level models that are optimized and refined to identify small, differentially regulated subnetworks, even in the absence of large-scale datasets. As a proof of concept, we applied KiRNet to liver cancer cells overexpressing FZD2, a gene known to drive the epithelial-mesenchymal transition and cancer metastasis and identified a subnetwork of 166 proteins that regulate this cell state. We demonstrate that KiRNet can formulate high-value predictions for future testing and thus accelerate basic and translational discoveries.