Two
MUSC teams, composed of system biologists, were recognized in the 4th
Dialogue on Reverse Engineering Assessment and Methods (DREAM) workshop
held in November.
MUSC teams included John H. Schwacke, Ph.D., assistant professor in the
Department of Biochemistry and Molecular Biology; Jim W. Zheng, Ph.D.,
assistant professor, Division of Biostatistics and Epidemiology;
Xinghua Lu, M.D, Ph.D., associate professor, Division of Biostatistics
and Epidemiology; Kellie J. Sims, Ph.D., Lam Tsoi (Alex) and
Tingting Qin, both graduate students in the Division of Biostatistics
and Epidemiology.
Schwacke tied for first place and was designated with a “Best
Performer” award. His team also was invited to present Dec. 4 at the
RECOMB and DREAM4 Reverse Engineering Challenge at the Massachusetts
Institute of Technology’s Broad Institute in Boston. A second team led
by Zheng, placed third after a tie for first among two other teams.
The event is part of an annual international bioinformatics competition
for system biologists and researchers to use computational methods to
solve five biological challenges to make predictions about networks
from provided data. Teams focused on developing and assessing reverse
engineering methods applied to problems such as gene regulatory network
inference, binding domain prediction and signal transduction pathway
prediction.
The annual competition is devised from both simulated and experimental
data donated by leading system biology research groups. It’s organized
and sponsored by Columbia University, IBM’s Computational Biology
Center, the NIH Roadmap Initiative and New York Academy of Sciences.
MUSC’s bioinformatics research group within the Department of
Biochemistry and Molecular Biology has made significant contributions
to the research development at MUSC, particularly to the research
infrastructure including the COBRE in Lipidomics and Biopathology, the
NHLBI Proteomics Center, and SCTR. The faculty members engage in
a variety of bioinformatics and computational biology research efforts
including signal transduction system inference and modeling, proteomic,
lipidomic and transcriptomic data analysis, metabolic pathway modeling,
functional genomics, and text mining.
For information on the group’s research interests and capabilities, contact Lu at lux@musc.edu.
Friday, Dec. 11, 2009
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