Ph.D., Dartmouth College, 2003
Gene regulatory networks; integrated genomic analysis; bioinformatics; RNA biology; small RNAs
Cellular function is dictated by the complex interplay of gene regulatory networks that control gene expression. These networks are incredibly robust to small or temporary fluctuations in the environment, yet retain the ability to change and re-wire themselves in response to large-scale or long-term changes in external stimuli. This plasticity in genetic networks is mediated by multiple types of regulatory factors, such as transcription factors and non-coding RNAs, which respond coordinately to changes in cellular signaling. Thus, gaining a systems level view of how these factors combine to produce a specific cellular outcome requires distilling multiple types of genomic profiling data into an integrated model of genetic signaling pathways.
My group uses computational algorithms to integrate multiple types of genomic and transcriptomic profiling data into models of regulatory re-wiring events in human disease. This includes an emphasis on developing novel tools for the statistical analysis of high-throughput data, developing novel algorithms for modeling the flow of signals through genetic pathways, and importantly, testing these models using the tools of molecular genetics. The ultimate goal is to understand how human diseases like cancer take advantage of the cell’s innate propensity for plasticity to re-wire these regulatory networks into programs that serve the needs of the cancer cells.
Rozhkov, N.V., Hammell, M., and Hannon, G.J. 2013. Multiple roles for Piwi in silencing Drosophila transposons. Genes Dev. 27:400–412.
Li, W., Jin, Y., Prazak, L., Hammell, M., and Dubnau, J. 2012. Transposable Elements in TDP-43-Mediated Neurodegenerative Disorders. PLoS One 7:e44099.
Hammell, M. 2010. Computational methods to identify miRNA targets. Semin. Cell Dev. Biol. 21: 738–744.
Hong X., Hammell, M., Ambros, V., and Cohen, S.M. 2009. Immunopurification of Ago1 miRNPs selects for a distinct class of microRNA targets. PNAS 106: 15085–15090.
Hammell, M., Long, D., Zhang, L., Lee, A., Carmack, C.S., Han, M., Ding, Y., and Ambros, V. 2008. mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts. Nature Methods 5: 813–819.