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<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff size=2>Elena Nabieva will present her preFPO on Thursday February
27 at 11:15AM in </FONT></SPAN></DIV>
<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff size=2>Room 253, Carl Icahn Lab. The members of her
committee are: Mona Singh, </FONT></SPAN></DIV>
<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff size=2>advisor; Tom Funkhouser, Olga Troyanskaya, readers; Bernard
Chazelle, </FONT></SPAN></DIV>
<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff size=2>Ned Wingreen (MolBio), nonreaders. Everyone is
invited to attend her talk.</FONT></SPAN></DIV>
<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff size=2>Her abstract follows below.</FONT></SPAN></DIV>
<DIV dir=ltr align=left><SPAN class=970213918-22022007><FONT face=Arial
color=#0000ff
size=2>------------------------------</FONT></SPAN></DIV><BR>Abstract:<BR>
<P>Exploring the interplay between topology and function in protein interaction
networks<BR><BR>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. <BR><BR>One type of data
obtained by high-throughput experiments is information about interactions among
proteins, such as physical protein-protein interactions. This
information<SPAN> </SPAN>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.<BR><BR>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.<BR><BR>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.<BR><BR>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.</P></BODY></HTML>