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SUMMARY:Learning Probabilistic Sequence Models for Uncovering Gene Regulat
 ion - Mark Craven\, University of Wisconsin
DTSTART:20061004T131500Z
DTEND:20061004T141500Z
UID:TALK5373@talks.cam.ac.uk
CONTACT:Timothy G. Griffin
DESCRIPTION:A central challenge in computational biology is to uncover the
 \nmechanisms and cellular circuits that govern how the expression of\nvari
 ous genes is controlled in response to a cell's environment.  In\nthis tal
 k\, I will discuss two aspects of my group's work on learning\nprobabilist
 ic grammars to identify gene-regulatory elements in genomic\nsequences.  F
 irst\, I will talk about a method we have developed for\nmodeling and pred
 icting arbitrarily overlapping elements in sequence\ndata.  We have applie
 d this algorithm to the task of analyzing\nbacterial genomes\, in which it
  is common for functional elements in\nthe genomes to overlap one another.
   Second\, I will talk about an\napproach we have developed for learning e
 xpressive models of\ncis-regulatory elements (CRMs).  A CRM is a configura
 tion of sequence\npatterns that controls how a set of genes responds to sp
 ecific\nconditions in a cell.  This talk will not assume any prior knowled
 ge\nof molecular biology\, and for both algorithms\, I will discuss why th
 ey\nare of interest for applications outside of biology.\n
LOCATION:Lecture Theatre 1\, Computer Laboratory
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