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Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies; however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes.

Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion