Time: 3:00-4:30 p.m., April 24, 2013

Venue: Medical Science Building, B323

Reporter: Zhang Bin, Associate Professor, Department of Genetics & Genomic Sciences, Member, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA

Introduction

Dr. Bin Zhang is an associate professor of the Department of Genetics and Genomic Sciences and a member of the Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA. Prior to his appointment at Mount Sinai, he was a Principal Scientist and Group Leader of Sage Bionetworks, a non-profit research organization started in 2009 that grew out of a decade of intense well-funded work at Rosetta Inpharmatics, a wholly owned subsidiary of Merck & Co. Before he joined Sage, he worked at Merck & Co. first as a senior research scientist from and then as a Research Fellow. Prior to joining Merck & Co., he was a post-doctoral fellow and then a Research Faculty and Senior Biostatistician at David Geffen Medical School of University of California at Los Angeles. He holds a Ph.D. and a master degree in Computer Science from the State University of New York at Buffalo, a master degree in electronic engineering from Tsinghua University, Beijing, China, and a bachelor‘s degree in electrical engineering from Tongji University, Shanghai, China.

His expertise lies in bioinformatics and computational biology, image processing, pattern recognition and data mining. He has developed and significantly contributed to several influential gene network inference algorithms which have been extensively used to identify pathways and gene targets involved in a variety of diseases such as cancer, atherosclerosis, Alzheimer‘s, obesity and diabetes etc. As a prolific researcher, he has published 70 peer-reviewed journal and conference papers including 8 papers in Nature, Nature Genetics, Cell and PNAS. As of December 2011, his publications have been cited 2750 times, according to Google Scholar.

Abstract:

Despite decades of intensive research, the causal chains of mechanisms underlying Late-Onset Alzheimer’s Disease (LOAD) remains elusive. New approaches need be developed to identify the causal genes and pathways in LOAD. In this study, we conducted an integrative multiscale network-based analysis of DNA and mRNA data in 1647 post-mortem tissues from multiple brain regions of subjects diagnosed with LOAD and normal disease-free controls. A massive remodeling of network structures in LOAD was observed and quantified to objectively rank order subnetworks for relevance to LOAD pathology. Gene causal networks were further constructed to identify key drivers and develop mechanisms. This multiscale network-based analysis reveals a subnetwork involved in immune response as the top ranking with respect to LOAD pathology. We further experimentally validated a predicted key driver of this immune response subnetwork by demonstrating its involvement in amyloid-β turnover and neuronal damage as well as its capability of regulating its downstream targets.