New methods for analyzing biological networks
We create new network analysis techniques that are specifically designed to answer important questions in biology and medicine. ALPACA compares regulatory network structure between cases and controls to identify modules that are specific to disease. Inspired by differential expression (mRNA) analysis, we developed CRANE to estimate the statistical significance of differential modules. Our methods are open-source and available on our lab Github page.
Merkel cell carcinoma
Neuroendocrine tumors are an aggressive type of cancer that can occur in many organs throughout the body. Merkel cell carcinoma is the only neuroendocrine cancer that can be caused by a virus. Using the virus, we can generate "cancer in a test tube" and observe how neuroendocrine cancer develops in the lab. Using genomic profiling, we are creating dynamic regulatory network models to understand how normal skin cells are reprogrammed into a precancerous neuroendocrine state - and to identify drugs that can reverse this process.
Early-onset colorectal cancer
Colorectal cancer rates in individuals under the age of 50 have been rising over the past two decades. We are identifying changes in the genome, transcriptome, and methylome that are associated with early-onset colorectal cancer. In particular, we are modeling how cryptic alterations to the regulatory network can induce cancer in younger individuals who have not had time to accumulate mutations in known cancer genes.