Dr. Kimberly Glass
Assistant Professor of Medicine
Brigham and Women’s Hospital & Harvard Medical School
Multi-Omic Network Analysis in Complex Disease
Abstract:
Rapidly evolving Omics technologies are providing unprecedented amounts of data that are yielding new insights into complex disease. Networks provide a powerful approach for integrating multiple types of Omics information. Our group has developed a suite of methods that support: (1) effective integration of multi-omic data to reconstruct gene regulatory networks; (2) analysis of these networks to identify changes in disease state; and (3) modeling of patient-specific networks in order to link regulatory alterations with heterogeneous phenotypes. In this talk, I will review several specific applications in which we have used these approaches to discover new features of disease and to understand the complex regulatory processes at work across patients.
Key references:
Kuijjer ML*, Tung M*, Quackenbush J, Yuan GC, Glass K. Estimating Sample-Specific Regulatory Networks. iScience. 2019 Apr 26;14:226-240. doi: 10.1016/j.isci.2019.03.021. Epub 2019 Mar 28. Pubmed Cell Press arXiv
Lopes-Ramos CM, Kuijjer ML, Ogino S, Fuchs CS, DeMeo DL, Glass K, Quackenbush J. Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism processes. Cancer Research. 2018 Oct 1;78(19):5538-5547. Pubmed bioRxiv
Biography:
Kimberly Glass is an expert in complex networks and genomic data analysis. She obtained her PhD in Physics in 2010 from the University of Maryland. From 2010-2014, Dr. Glass was a postdoctoral fellow at Dana-Farber Cancer Institute and the Harvard T.H. Chan School of Public Health where she received training in computational biology. During her post-doc she developed several computational and data-integration methods for inferring and analyzing gene regulatory networks. In 2014 Kimberly joined the faculty of the Channing Division of Network Medicine (CDNM) at Brigham and Women’s Hospital where she is continuing her research in systems medicine and network methods. Her current research focuses on how to integrate and interpret multiple biological data-types in the regulatory network context and on how to understand the biological mechanisms represented in these networks. She is also investigating potential applications of networks in precision medicine, using network approaches to understand susceptibility to, severity, and treatment of complex diseases.