Lee Newberg, the proposed candidate, received his Ph.D. from the Department of Computer Science at Berkeley, for his work in quantitative biology, but his career has been outside of biomedical research since that time. This proposal aims to bring Newberg back to biomedical research - to molecular medicine and, specifically, the field of transcription regulation. Newberg left his academic career in computational biology to follow his wife's academic career in astrophysics. In his first position after achieving his Ph.D., he wrote educational software for The University of Chicago Biological Sciences Division and was ultimately promoted to be acting Director of the division's Office of Academic Computing. Subsequently, Newberg worked in quantitative finance as the lead for the quantitative aspects of a two billion-dollar global event-driven arbitrage portfolio. While his career has kept his quantitative and organizational skills honed, to return to biomedical research he needs training in molecular biology and biochemistry, and he needs research supervision from suitable mentors. Charles """"""""Chip"""""""" Lawrence and Randall Morse, the proposed mentors, are key players in the field of quantitative transcription-regulation analysis, and advisor Lee Ann McCue has made significant contributions to the field as well. With guidance from these able mentors, Newberg proposes to attack the task of locating transcription factor binding sites (TFBSs), by rigorously modeling and analyzing the interplay between transcription regulation and evolution. They propose to use these models to extend the reach of existing approaches for TFBSs to increased sensitivity. The transcription regulatory network is arguably the most important foundation of cellular function, since it exerts the most fundamental control over the abundance of virtually all of a cell's functional macromolecules. Genomic-level analysis of this process is an area of intense research interest, both experimentally and computationally. Comparative genomics has proven to be a powerful bioinformatics method with which to study transcription regulation. A K25 award will enable Newberg to become a high-quality independent investigator and will move us towards a greater understanding of cellular function.
| Palumbo, Michael J; Newberg, Lee A (2010) Phyloscan: locating transcription-regulating binding sites in mixed aligned and unaligned sequence data. Nucleic Acids Res 38:W268-74 |
| Newberg, Lee A; Lawrence, Charles E (2009) Exact calculation of distributions on integers, with application to sequence alignment. J Comput Biol 16:1-18 |
| Newberg, Lee A (2008) Memory-efficient dynamic programming backtrace and pairwise local sequence alignment. Bioinformatics 24:1772-8 |
| Newberg, Lee A (2008) Significance of gapped sequence alignments. J Comput Biol 15:1187-94 |
| Newberg, Lee A; Thompson, William A; Conlan, Sean et al. (2007) A phylogenetic Gibbs sampler that yields centroid solutions for cis-regulatory site prediction. Bioinformatics 23:1718-27 |
| Thompson, William A; Newberg, Lee A; Conlan, Sean et al. (2007) The Gibbs Centroid Sampler. Nucleic Acids Res 35:W232-7 |
