Mark B Gerstein, Ph.D

Professor of Biomedical Informatics
Molecular Biophysics & Biochemistry
and Computer Science
Yale University

Title: Analysis of Molecular Networks

Abstract:
My talk will be concerned with understanding protein function on a genomic scale. My lab approaches this through the prediction and analysis of biological networks, focusing on protein-protein interaction and transcription-factor-target ones. I will describe how these networks can be determined through integration of many genomic features and how they can be analyzed in terms of various topological statistics. In particular, I will discuss a number of recent analyses: (1) Improving the prediction of molecular networks through systematic training-set expansion; (2) Showing how the analysis of pathways across environments potentially allows them to act as biosensors; (3a) Analyzing the structure of the regulatory network indicates that it has a hierarchical layout with the "middle-managers" acting as information bottlenecks; (3b) Showing these middle managers tend be arranged in various "partnership" structures giving the hierarchy a "democratic character" ; (4) Showing that most human variation occurs at the periphery of the protein interaction network; (5) Comparing the topology and variation of the regulatory network to the call graph of a computer operating system; and (5) Developing useful web-based tools for the analysis of networks (TopNet and tYNA).
http://networks.gersteinlab.org
http://topnet.gersteinlab.org

The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006) Bioinformatics 22: 2968-70.

Analysis of Diverse Regulatory Networks in a Hierarchical Context: Consistent Tendencies for Collaboration in the Middle Levels N Bhardwaj et al. PNAS (2010)

Positive selection at the protein network periphery: evaluation in terms of structural constraints and cellular context. PM Kim, JO Korbel, MB Gerstein (2007) Proc Natl Acad Sci U S A 104: 20274-9.

Training Set Expansion: An Approach to Improving the Reconstruction of Biological Networks from Limited and Uneven Reliable Interactions. KY Yip, M Gerstein (2008) Bioinformatics

Quantifying environmental adaptation of metabolic pathways in metagenomics T Gianoulis, J Raes, P Patel, R Bjornson, J Korbel, I Letunic, T Yamada, A Paccanaro, L Jensen, M Snyder, P Bork, M Gerstein (2009) PNAS

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks. KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010) Proc Natl Acad Sci U S A

Bob Cottingham

Group Leader
Computational Biology and Bioinformatics
Oak Bridge National Laboratory

Title: Bioinformatics - Transition from algorithmic to data intensive science

Short Biography:
Bob Cottingham is one of the pioneers of bioinformatics. In the 1970s he began his career as a software developer on some of the first genetic linkage analysis programs applied to mapping human disease traits. In 1989 he became Directeur Informatique at CEPH in Paris France overseeing the database of CEPH family genotypes, a resource ultimately used by more than 1000 labs in an international consortium to construct the first genetic maps of the human genome. He then joined the US Human Genome Project, first as the Co-Director of the Informatics Core in the Baylor College of Medicine Human Genome Center, then as Operations Director of the Genome Database at the Johns Hopkins University School of Medicine. Subsequently, Bob became Vice President of Computing at Celltech Chiroscience, a UK biopharmaceutical company developing drugs based on gene targets. In 2000 he co-founded Vizx Labs, a bioinformatics company that developed GeneSifter, the first web based gene expression microarray analysis service now used worldwide by hundreds of labs. In 2008 Bob moved to Oak Ridge National Lab where he leads the Computational Biology and Bioinformatics group that currently has projects in the DOE BioEnergy Science Center and Genomic Science programs.