[bioinfo] CS 591 BIO: REMINDER: Talk TOMORROW by Tsvi Achler (Computer Science, UIUC)

Sinha, Saurabh sinhas at illinois.edu
Thu Apr 23 20:50:53 CDT 2009


Hello,
   our speaker this week is Dr. Tsvi Achler of the Computer Science
department (UIUC), who will be presenting his work on Genetic Networks
analysis. Details of the talk appear below.

   DATE: Friday, April 24
   TIME: 1100 AM - 1200 noon
   PLACE: 4403 Siebel Center.

   thanks,
   Saurabh

-------------------------------------------------------------
Title:
Gene Product Homeostasis Model of Genetic Networks

Speaker: Tsvi Achler
Post-doctoral researcher, Computer Science, UIUC.
http://reason.cs.uiuc.edu/tsvi/

Abstract:
An implicit problem in studying genetic networks is that we have
information about gene expression, but we don¹t have information about
the final products (ie. proteins, enzymes etc.), their concentrations,
and their effect on expression.
Thus for modeling, the effects of the end-products are abstracted away
to what we can measure: namely the degree of how one gene effects the
expression of another.  In modeling genetic networks such interactions
are commonly reduced to a network with an optimized set of parameters
designating types and strengths of Odirect¹ Olinear¹ interactions
between genes.
However the real genetic networks are more complex, and the expression
of each gene is likely auto-regulated based on the concentrations of
the end-products.  Likely expression of each gene maintains a
homeostasis based on the end product.  Thus for example the cells
produce only the right amount of enzymes ie for peroxide-genesis that
in over produced concentrations may be toxic to cells.
Products that require multiple enzymes, in complex production pathways
likely regulate the many genes involved in the pathway.  From the
perspective of gene expression this regulation is not linear.
Thus I model gene to gene interactions though the gene-product
regulation.  The genes interact in a nonlinear fashion modeling
important properties of regulation which cannot be observed directly.
This gene-product model of regulation gives a deeper insight towards
genetic networks.





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