Ph.D. University of Washington, Seattle, 2002
Our research focuses on the development of algorithms for solving problems in molecular biology. It builds on techniques from probability theory and machine learning, exploring new frontiers in these individual subjects. We try to understand how genes are regulated, and how the evolution of the regulatory network gives rise to "endless forms, most beautiful and most wonderful" (C. Darwin).
Some of the exciting and challenging problems that we are currently working on include:
- Motif finding - the discovery of transcription factor binding sites by extracting statistically significant patterns in DNA sequences.
- Cis-regulatory modules - motifs (binding sites) are organized in specific combinations into regulatory sequences called "modules", and their computational discovery can provide valuable insight into the "circuitry" behind a gene's function.
- Probabilistic models in Comparative Genomics: We deploy the powerful paradigm of comparing related sequences from multiple species in order to better identify biologically functional subsequences.
- Evolution of Regulation: We study how binding sites and modules evolve across a broad range of evolutionary distances, in an attempt to identify some basic principles of evolution.
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