Tata Institute of Fundamental Research

Computational Challenges in Network Biology: From Brain Tissues to Single Cells

STCS Seminar
Speaker: Manikandan Narayanan (National Institute of Health United States of America)
Organiser: Vinod M. Prabhakaran
Date: Thursday, 5 Feb 2015, 11:00 to 12:00
Venue: AG-66 (Lecture Theatre)

(Scan to add to calendar)
Abstract:  Abstract: Interactions among proteins, regulatory DNA regions surrounding genes, and other biomolecules are central to cellular function and health. Constructing a network representation of these interactions makes it possible to dissect complex cellular behaviors using computational and statistical methods. I would like to talk about the design and application of a graph-theoretic algorithm that aligns gene networks derived from more than 600 human postmortem brain tissues to extract insights about dysregulation in two neurodegenerative diseases. Given the recent advances and huge interest in single-cell measurement technologies, I would like to conclude the talk with challenges in extending similar network inference ideas from tissue-level measurements to those obtained from individual cells (this work was supported by the Intramural Research Program of NIAID, NIH).

Bio: Manikandan Narayanan is a Staff Scientist in the Laboratory of Systems Biology at the National Institutes of Health (NIH). He is interested in the design and application of computational methods to study cellular behaviors. Complex cellular behaviors arise from interactions among various molecules, and Narayanan's specific interest involves analysis of network (graph) representation of such interactions. He obtained his Ph.D. in Computer Science (with an emphasis in computational and genomic biology) from the University of California at Berkeley under the mentorship of Prof. Richard Karp, and held a Sr. Research Scientist position at Merck Research Labs first in Seattle and then in Boston before coming to the NIH. He is a Siebel Scholar Class of 2003, and has authored widely-cited publications in top-ranking computational biology journals.