CIS Colloquium, Jul 29, 2014, 11:00AM - 12:00PM, Wachman 1015D

CIS Colloquium, Jul 29, 2014, 11:00AM - 12:00PM, Wachman 1015D


Mining real-world networks: from biology to economics


Natasa Przulj , Imperial College London

Abstract:
We are faced with a flood of molecular network data. The challenge is how to use them to answer fundamental questions. Just as sequence-based computational approaches have revolutionized biological understanding, the expectation is that analysis of biological networks will have a similar ground-breaking impact. However, dealing with network data is nontrivial, since many methods for analyzing large networks fall into the category of computationally intractable problems. We develop efficient methods for extracting biological information from the topology of large systems biology molecular network data sets. For example, we introduce a family of topology-based network alignment algorithms, that we call GRAph ALigner (GRAAL) algorithms, which produce by far the most complete alignments of biological networks to date. Also, we develop approaches that link network topology with biological function and translate the information hidden in the topology into everyday language, bringing network analysis closer to biologists. For instance, we demonstrate that topology around cancer and non-cancer genes is different and when integrated with functional genomics data, it successfully predicts new members of melanogenesis-related pathways. Also, we find that aging, cancer, pathogen-interacting, drug-target and genes involved in signaling pathways are topologically "central" in the network, occupying dense network regions and "dominating" other genes in the network. Furthermore, we develop new methods for fusing heterogeneous biological network data to find new relationships between diseases, as well as methods for tracking network dynamics. We conclude that network topology is a valuable source of biological information that can suggest novel drug targets and impact therapeutics. Also, we gain new economic insights by tracking the dynamics of the World Trade Network.

Bio:
Dr. Natasa Przulj is an Associate Professor in the Department of Computing, Imperial College London. At Imperial, she is also a member of the Institute of Systems and Synthetic Biology, the Centre for Bioinformatics, and the Centre for Integrative Systems Biology (CISBIC). She was an Assistant Professor in the Department of Computer Science at University of California Irvine from 2005 to 2009. She obtained a PhD in Computer Science from University of Toronto, Canada, in 2005. Dr. Przulj is a Fellow of the British Computer Society. In 2014, she was awarded the British Computer Society Roger Needham Award for a distinguished research contribution in computer science by a UK based researcher within ten years of their PhD. In 2013, she was elected into the Young Academy of Europe. She received a prestigious European Research Council (ERC) Starting Independent Researcher Grant for 2012-2017 for her project titled "Biological Network Topology Complements Genome as a Source of Biological Information". She held a USA analogue to an ERC Starting Grant, a prestigious NSF CAREER Award, for the project titled "Tools for Analyzing, Modeling, and Comparing Protein-Protein Interaction Networks" in 2007-2011 at University of California Irvine. Her research has also been supported by other large governmental and industrial grants including those from GlaxoSmithKline, IBM and Google. Dr Przulj is renowned for initiating extraction of biological knowledge purely from topology of real-world networks. That is, she views large and complex biological networks as a new source of biological information that needs to be mined, and looks for links between network topology in protein-protein interaction networks and biological function and involvement of proteins in disease. Her recent work includes integration and dynamics of heterogeneous network data, applied to many areas of systems biology and medicine, as well as to economics.

© 2001-2013 Center for Data Analytics and Biomedical Informatics, Temple University