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                Department of Mathematical Sciences                       
                   The Johns Hopkins University                           
                                                                        
                              SEMINAR                                
                                                                        
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Raimond L. Winslow                                 December 7, 2000
Whitaker Biomedical Engineering Institute          304 Whitehead Hall
Center for Computational Medicine and Biology      Preseminar: 3:00 p.m.
Burroughs Wellcome Program in Computational Biol.  Refreshments: 3:30 p.m.
The Johns Hopkins University                       Seminar: 4:00 p.m.

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             INTEGRATIVE MODELING OF BIOLOGICAL SYSTEMS:                  
    RELATING ALTERED GENE EXPRESSION TO CELL AND TISSUE FUNCTION

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                             ABSTRACT

There is growing recognition that the tabulation of genetic and molecular
building blocks from which biological systems are composed is not by itself
sufficient for understanding the functional properties of these systems.
Rather, it is becoming clear that dynamic interactions between individual
system components can produce emergent, complex behaviors that are not
readily predicted from knowledge of each system component alone.
Experimentally-based computational models are beginning to provide powerful
new tools for understanding the nature of these interactions.
	
This integrative "systems" approach to the understanding of
structure-function issues in biology will be illustrated by our recent work
on relating changes in gene expression measured in the failing heart to
functional consequences at the cell and tissue level through development and
application of computational models.  We will describe how cDNA microarray
and other technologies are used to measure changes in gene expression in the
failing heart.  We will describe how these data constrain computational
models of the failing myocyte, and how these models are used to understand
the functional importance of changes in gene expression on risk for
arrhythmia, and to suggest new therapeutic targets for reducing this risk.
Finally, we will discuss new emerging areas of biological modeling that may
be of interest to members of the mathematical sciences community.

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