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KiRNet: Integrated, kinase-centered network model for investigating kinase-driven phenotypes

ABSTRACT: 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 in basic and translational sciences. To address this need, we have created KiRNet, an kinase-centered method designed to integrate results of functional screens with interactome or other protein-protein interaction data, as well as additional molecular data to generate functional network-level models. These network models can be further 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 driven by Fzd2, a gene known to cause epithelial-mesenchymal transition and cancer metastasis and identified a subnetwork of 166 proteins that regulate this cell state. The KiRNet derived information can be used to formulate high-value predictions for future testing and thus accelerate basic and translational discoveries.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Taran S. Gujral

PROVIDER: MSV000086446 | MassIVE | Thu Nov 12 10:37:00 GMT 2020

REPOSITORIES: MassIVE

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