Computer Science/Center for Computational Molecular Biology Seminar
"Systems Macro-Biology: From Parts and Genomes to Network Signatures of Disease"
Simon Kasif, Boston University and Children's Hospital, Boston
Wednesday, October 22, 2008 at 4:00 P.M.
Room 241 Swig Boardroom (2nd Floor CIT)
Computer systems specification, analysis and diagnosis are arguably one of the most practically important and essential topics in computer science and engineering. This research resulted in tools and environments that allow engineers to specify and analyze hardware and networks as well as recognize and correct unexpected behaviors, network intrusion or anomalies.
Medicine and biology, on surface present similar problems. Our cells are provided with basic instructions that when executed properly enable living organisms to function properly. As a result of genetic, epigenetic or environmental perturbations our cells exhibit aberrations in their functional behaviors leading to major diseases such as cancer or diabetes, causing inordinate suffering for patients and their families.
We focus on insulin signaling and related processes such as inflammation and glucose metabolism. We describe our on-going projects aimed towards identification of the full dictionary of parts and their cellular interactions that are involved in these important biological functions using both network and evolutionary approaches. This work leads to better genomic annotation of newly sequenced genes and a number of novel predictions. Manipulations of several genes in these pathways have been shown to extend life in model organisms or have predicted associations with diabetes in the human population.
Biology is very complex and it is often difficult to formalize the entire repertoire of "normal" or "abnormal" clinical phenotypes of age associated diseases such as Diabetes or Alzheimer's. We describe the new paradigm of network signatures of disease that allow us to recognize anomalies leading to disease at the molecular network level. Specifically, we introduce several concepts including Gene Network Enrichment Analysis (GNEA) and show how it enables biomedical researchers to identify and confirm disregulated molecular processes in diabetes and insulin resistance that elude recognition by standard methods. This work has the potential to lead to new diagnostic or prognostic biomarkers as well as new drug targets.
This work describes joint research performed at Boston University, Harvard Medical School, Joslin Diabetes Center, Harvard School of Public Health and the National Center for Biomedical Computing (I2B2) at Harvard Partners.
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