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Functional Genomics and Systems Biology Group
Our group investigates integrative approaches to combine functional and systems level knowledge with more traditional genomic code and annotation information. We seek diverse and multi-disciplinary approaches that generally rely on tools such as pattern discovery, statistics, machine learning and simulation. For a quick overview of our work, please see our projects. To carry out these projects, the Functional Genomics and Systems Biology
Group has assembled a diverse set of researchers located mainly at IBM
T.J. Watson Research Center. We also collaborate with other groups within
the Computational Biology Center and IBM Research as well as Healthcare and Life Sciences. In addition, we have a number of academic and industrial collaborators.
Here is quick tour of our group:
Supplemental Materials for:
Rice,
J.J. and Stolovitzky, G. Reconstructing biological networks using conditional
correlation analysis. Bioinformatics.
(2004, in press). (Pubmed) (Supplemental Materials - pdf)
Background
The amount and rate of accumulation of biological information is increasing
rapidly, as is demonstrated by the fact that there are now several hundreds
biological databases accessible from the World Wide Web. Each of these
databases is devoted to a particular category in the hierarchy of biological
organization. For instance, NCBI's Genbank catalogs DNA data, The Swiss Institute for Bioinformatics' SWISS-PROT is a repository of protein sequences, Kyoto University's KEGG is a compilation of biochemical and genetic circuits, etc.
The list of genes and proteins of an organism, however, constitute only
the ground zero in a pyramid of biological complexity, whose top is life
itself. This pyramid is a metaphor for the hierarchy of structures out
of which biological function (such as metabolism or replication) arises.
Elements at each level in this hierarchy interact with each other to produce
a higher level of organization, thus climbing up one step in the pyramid
of bio-complexity. It follows that the information of all the genome and
all the proteome (which is roughly where we stand now) is insufficient
to understand the subtleties of biological function. In order to get a
handle to function we need to understand how the building blocks at a given
level of organization interact with each other: how proteins interact with
both genes and proteins to produce molecular circuits; how these circuits
interact with each other to allow for cellular function; how cells interact
to produce tissues; how tissues form organs; and finally how organs work
together to create a living being.
An understanding of the behavior of biological systems at each level of
their organization can only be achieved by careful study of the complex
dynamical interactions between the components of these systems. For this
understanding to be quantitative it is necessary to develop structurally,
biochemically and biophysically detailed mathematical models. Once developed,
these models can be simulated, analyzed, and visualized through application
of modern engineering and computational approaches, such as the ones pioneered,
e.g., by the groups at Keio University and at the Center for Nonlinear Dynamics in Physiology and Medicine.
Why should IBM Research be interested in simulating life processes? This
question can be answered by analogy with the problem of local weather prediction.
Local weather is the result of large scale fluid motion and cloud microphysics.
In spite of the fact that the equations governing these phenomena have
been known for a long time, weather prediction continues to be a difficult
problem even today. The reason for this is, in part, that weather is governed
by non-linear, partial differential equations, whose solutions depend subtly
on the initial and boundary conditions. These solutions are very often
counterintuitive giving rise, e.g., to the phenomena of convection and
fluid turbulence. IBM has recognized the basic-science importance and the
potential business value of improved weather forecast through funding of
the Deep Thunder project, one of a few selected endeavors undertaken by IBM's Deep Computing
Institute. Along the same lines, biological systems are governed by stochastic,
non-linear, partial differential equations, and as such, their solutions
can only be produced by means of massive computation. As a matter of fact,
scientists do not yet know the precise set of equation nor the boundary
conditions that govern most of the biological processes. The basic science
challenge is thus there for us to conquer. The potential business value
of biological-system modeling can be understood in that biology is becoming
a major factor in medicine and health, which encompasses a market of over
a trillion dollars.
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