Abstract
Precision medicine needs to be data driven and corresponding analyses comprehensive and systematic. We use models of biological systems to integrate diverse types of information. This ranges from multiple high-throughput datasets, functional annotations and orthology data to expert knowledge about biochemical reactions and biological pathways. Such integrative systems are used to develop new hypotheses and answer complex questions such as what factors cause disease; which patients are at high risk; will patients respond to a given treatment; how to rationally select a combination therapy to individual patient, etc.
Semantic biological pathway modeling has been studied for some time, but it is still at an early stage of development. Specifically, we discuss challenges and experiences in the design and construction of pathway representation models, as well as tools and strategies for using these models for visualization, data integration, and hypotheses generation. Such models of integrated signaling cascades may enable characterizing and in turn treating cancer successfully. Using a systematic graph theoretic analysis of relevant networks we predict and validate drug combinations for "repairing" cancer network.