Hello, my name is Trey Ideker,
I'm a professor in
the Department of Bioengineering at the University of California, San Diego.
The title of my talk today is protein networks and analysis of global gene expression.
Much of the recent excitement in network biology runs in parallel to and by analogy
to many of the successful developments in
protein sequence biology over the past 30 years.
Over here at left is shown what's by far
the most powerful paradigm in all of bioinformatics and perhaps, in all of genomics.
The idea is that if you have a critical mass of
DNA and protein sequence in a public database,
then you can do a very simple yet powerful things
like query that database with a short tidbit like
the nucleic acid sequence shown here to find all of the sequence similar matches
based on what's known about the function and structure of some of those matches.
Then you can infer that the structure and function
of your sequence query may, in fact, be the same.
What's shown over here now at right is
the analogy at the level of the protein interaction network.
What my group and several groups of other labs are trying to do,
is to develop an analogous set of
bioinformatic tools that would allow you to query not just the protein sequence database,
but databases of protein-protein and other kinds of interactions.
In this case, the goal is somewhat less clear,
but generally would be to query this database with
a global data set that's complimentary to the interaction data,
in order to find particular patterns of
interactions and regions of the interaction network that correspond to
different signaling and gene regulatory pathways of interest.