Elena Nabieva will present her preFPO on Thursday February 27 at 11:15AM in
Room 253, Carl Icahn Lab. The members of her committee are: Mona Singh,
advisor; Tom Funkhouser, Olga Troyanskaya, readers; Bernard Chazelle,
Ned Wingreen (MolBio), nonreaders. Everyone is invited to attend her talk.
Her abstract follows below.
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Abstract:
Exploring the interplay between topology and function in protein interaction networks
The emergence in recent years of numerous high-througphut experimental techniques in
biology has lead to a new, genome-scale approach towards biological research. This
high-throughput biology faces two complementary tasks: obtaining data on genomic scale and
making sense of this data. It is the second task where computer scientists working in
computational biology can make great contribution.
One type of data obtained by high-throughput experiments is information about interactions
among proteins, such as physical protein-protein interactions. This information can bring
scientists closer to a solution to one of the most important problems in biology:
understanding the role that different proteins play in the cell and the interplay among
them.
In my work, I look at the relationship between protein function and the protein's context
in the interaction network from two angles: using interaction networks and information
about other proteins to predict a protein's cellular role, and finding schemas, or
recurring patterns of interaction among different types of proteins.
In the first part of the talk, I explore the use of physical protein interaction networks
for predicting the function of proteins. First, using as illustration some of the
existing approaches to this problem, I discuss which topological properties of interaction
networks should be taken into account by algorithms for predicting protein function based
on physical interaction networks. Using these desiderata as guidelines, I introduce an
original network-flow based algorithm called FunctionalFlow that exploits the underlying
structure of protein interaction maps in order to predict protein function. In
cross-validation testing on the yeast proteome, I show that FunctionalFlow has improved
performance over previous methods in predicting the function of proteins with few (or no)
annotated protein neighbors. I demonstrate that FunctionalFlow performs well because it
takes advantage of both network topology and some measure of locality. Finally, I show
that performance can be improved substantially as we consider multiple data sources and
use them to create weighted interaction networks.
In the second part of the talk, I take a different view at the topology-function
relationship and use known information about protein molecular function and the physical
interaction network to attempt to uncover organizational principles of the network. In
this bottom-up view, I examine the networks from the perspective of ``pathway schemas,''
or recurring patterns of interaction among different types of proteins. Proteins in these
schemas tend to act as functional units within diverse biological processes. I discuss
computational methods for automatically uncovering statistically over-represented pathway
schemas in protein-protein interaction maps, and touch upon the comparative-interactomics
aspects of this problem. Coming back to the task of improving our understanding of
protein function, I conclude by demonstrating how overrepresented schemas can be used to
gain new insights about the biological function of proteins.
