Associate Professor, Biochemistry and Molecular Genetics
- BS, Microwave Engineering, University of California, Los Angeles
- PhD, Statistical Physics, University of California, Santa Barbara
- Postdoc, Statistical Physics, University of Maryland
- Postdoc, Computational Biology, The Rockefeller University
Bioinformatics and Genomics, Biophysics, Biophysics & Structural Biology, Cancer Biology, Computational Biology, Epigenetics, Molecular Biology, Translational Science
Physical Modeling of Microarray Hybridization; Analysis of Genomic Tiling Array Data; Bioinformatics; Computational Biology; Regulatory Networks
My laboratory develops and applies computational statistics methods to functional genomic data with a focus on epigenetic data. Epigenetics is the study of heritable phenotypic traits not coded in the primary DNA sequence, which can be modified by environmental factors and comprise a complex regulatory network that controls a number of processes on DNA including transcription, replication and repair. A central element of this control network is chemical groups (e.g., acetyl or methyl) that are added or removed from DNA and histones, which package DNA in a cell's nucleus. Control of processes such as transcription is achieved by making DNA sequences available or unavailable for factors including General Transcription Factors and Pol II that initiate transcription as well as recruitment of factors that facilitate each stage of transcription (i.e., promotion, elongation, etc.). Similarly, accessibility of DNA regulates the binding of the Origin Recognition Complex and subsequent initiation of DNA replication. Epigenetic factors have been shown to play a fundamental role in regulating cell differentiation, and the dysregulation of epigenetic processes have been implicated in a number of diseases including cancer, diabetes, and neurological disorders.
We are interested in characterizing the complex epigenetic regulatory network of histone modifications and DNA methylation, which exhibits extensive cross-talk. We apply machine-learning methods including Multivariate Regression Splines (MARS) and Bayesian Networks (BN) to epigenomic data in order to uncover this network and understand how it regulates transcription and DNA replication. We analyze both publicly available data sets (e.g., epigenomic data generated by K. Zhao's lab and the ENCODE, modENCODE and Epigenomics Mapping Consortia) as well as data generated by colleagues here at UVa. In our collaborations, we are studying (1) preinitiation complex (PIC) and transcription factor dynamics and their role in regulation of transcription level and precision (2) epigenomic regulation of the epithelial to mesenchymal transition -- a model of how cells are reprogrammed during metastasis (3) origins of DNA replication in the human genome and the epigenetic factors that drive them (4) the role of histone deacetylaces including Sir2 in aging and (5) the impact of chemicals in our environment (e.g., Bisphenol A) on our epigenome and subsequent phenotypic outcomes. An exciting aspect of the collaborative environment at UVa is that we are able to experimentally test our predicted epigenetic regulatory network models at the biochemical and phenotypic level.