Delineating dynamics of biological processes from static biological networks
Speaker: Teresa Przytycka , National Center of Biotechnology Information, NLM, NIH
Date: September 18 2006
Time: 11:30AM to 1:00PM
Location: TOC lab 32-G575
Host: P Clote, BC & B Berger, MIT
Contact: Kathleen Dickey, 617 253 3037, kvdickey@mit.edu
Relevant URL: http://www-math.mit.edu/compbiosem/
Mutual dependencies between biological entities are routinely represented in the form of various
types of networks. A major challenge in systems biology is to understand the intricate
relationships present in such network. In the last few years we witnessed a burst of research
directed towards delineating properties of various types of biological networks. These studies
typically focus on statistical properties of networks, like degree distribution, distribution of network
motifs, or identifying densely connected subnetworks.
Most of biological relations are, however, dynamic. For example, only a part of interactions
depicted in a protein-protein interaction network are realized in the cell at any given time.
Furthermore, certain interactions occur in a specific order, for example in the response to external
stimuli. Can we gain some information about such ordering based on the static network?
In this talk I will describe our contribution towards the next step in analysis of biological networks
– recovering temporal relation and overlaps between functional groups from a static network. I will
present two lines of research. In the first part of the talk, I will discuss a graph-theoretical
framework which allows elucidating functional groups in protein interaction network together with
(putatively) temporal relations between them. I will exemplify the utility of this approach using
pheromone signaling pathway recovered from a high throughput protein-protein interaction
network. In the second part of the talk, I will focus on application of this approach directed
towards recovering evolutionary relationships from a network representation of modern taxa.
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