CBI

Computational Modeling of Dynamic Biological Systems

Dr. Joe Song
Assistant Professor
Department of Computer Science
New Mexico State University

2-3pm, Wednesday, May 14, 2008

Room 610, New Life Science Building, Peking University

Modeling dynamical interactions among genes and environment can lead to a quantitative understanding of mechanisms in cellular processes, such as transcription regulation, metabolism, and disease pathways. The computational problem of identifying network models to account for temporal dependencies among interacting genes and environmental stimuli from high‐throughput gene expression data is addressed. The linear discrete dynamic system model was reconstructed for a gene regulatory network in response to 5‐hydroxymethylfurfural, a bioethanol conversion inhibitor for ethanologenic yeast Saccharomyces cerevisiae. A linear discrete dynamic system model consists of a system of difference equations for each variable. The statistically significant discrete dynamic system model of the yeast gene regulatory network derived from time course gene expression measurements by exposure to 5‐hydroxymethylfurfural, revealed several verified transcriptional regulation events. These events implicate several transcription factors consistently known for their regulatory roles by other studies or predicted by independent sequence motif analysis, suggesting their involvement in detoxification of the inhibitor by yeast. Data pre‐processing, model computation, and post‐processing in discrete dynamic system modeling will be discussed, which is applicable to other molecular interactions mechanisms involved in stress tolerance in biomass conversion.

Joe Song received his Ph.D. in 2002 and M.S. in Electrical Engineering in 1999, both from the University of Washington, Seattle. He obtained his B.S. in Electrical Engineering at Beijing University of Posts and Telecommunications in 1992. He has been an Assistant Professor at the Department of Computer Science, New Mexico State University since 2005. He was Assistant Professor of Computer Science with Queens College and the doctoral faculty with Graduate Center, City University of New York from 2002 to 2005. His research areas include statistical computing, quantitative biology, and computer vision. His research has been supported by NSF, NIH, and USDA. He has received Interdisciplinary Research Grant, Graduate Research Enhancement Grant, and Undergraduate Research Initiative Grant from New Mexico State University Office of the Vice President for Research. His current research projects in systems biology involve computational modeling of large dynamic biological networks at molecular levels. His lab currently has two postdoc researchers, two doctoral graduate students, and two Master’s students. He has collaborated with life scientists on campus and around the nation to solve computational modeling problems involved in biofuels, cancer, neuroscience, and microbial communities. His research partners include scientists at Fred Hutchinson Cancer Research Center, USDA, and Lawrence Berkeley National Lab.