Meet The Speaker
“Statistical models for dynamic networks”
Joel S. Bader, Ph.D., (joel.bader@jhu.edu, www.baderzone.org) is an Associate Professor at Johns Hopkins University in the Department of Biomedical Engineering and is a member of the High Throughput Biology Center at the School of Medicine, with secondary appointments in Computer Science and Human Genetics. Prior to joining Johns Hopkins, Dr. Bader was employed by CuraGen Corporation (1995-2003) and is co-inventor of the Roche/454 Genome Sequencer. Dr. Bader has a Ph.D. in Theoretical Chemistry from U.C. Berkeley (1991), where he was an NSF Predoctoral Fellow, and performed post-doctoral research at Columbia University (1992-1995). Dr. Bader has a B.S. in Biochemistry from Lehigh University (1986, Phi Beta Kappa, Tau Beta Pi).
Research in the Bader lab focuses on systems and synthetic biology: mapping and analyzing biological pathways; connecting genes and pathways to disease; and designing and building genomes. The Bader lab has received funding from NIH, NSF CAREER, DOE, Microsoft, the Kleberg Foundation, and the Simons Foundation.
Seminar Abstract
“Statistical models for dynamic networks”
Biological networks are dynamic, driven by processes such as development and disease that change the expressed genes and proteins and modify interactions. Understanding how networks remodel could reveal the etiology of developmental disorders and suggest new drug targets for infectious disease. We present new methods that predict how networks change over time through joint analysis of time-domain and static data. For each single network snapshot, we infer a hierarchical stochastic block model that describes patterns of heterogeneous edges connecting inferred groups of vertices. Across snapshots we introduce a time-evolution operator similar to a path integral or hidden Markov model for group membership. The method is fully Bayesian, with no adjustable parameters. Our methods provide superior performance for predicting new interactions and protein complex co-membership. Applied to dynamic data, the method reveals new regulatory mechanisms for controlling the activity of multi-protein complexes. These methods may have broad applicability to social networks and other dynamic networks. For preliminary work, see Park & Bader BMC Bioinformatics 2011 12:S44; Park & Bader Bioinformatics 2012 28:i40-8.
Note: Light lunch will be served starting at 11:30am.
