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SUMMARY:Positional Information\, in Bits
DTSTART:20140526T110000
DTSTAMP:20260406T214449Z
UID:ad2680351cbf872b83fccbe7b8c7e8f28f8c48bdcd18d6449d5e138d
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Gašper Tkačik\, IST Austria\, Klosterneuburg (AT)\nBIO
 ENGINEERING SEMINARAbstract:\nCells in a developing embryo have no direct 
 way of “measuring” their physical position\, but nevertheless need to 
 make cell fate decisions that are appropriate for specific spatial locatio
 ns. The conceptual framework that can account for this observation dates b
 ack to Wolpert\, and decades of research have subsequently uncovered the m
 olecular and cellular basis for establishing and reading out the proposed 
 "positional information\," encoded in spatial gradients of gene expression
 . Despite this progress\, we have until recently lacked a mathematical cou
 nterpart to Wolpert's conceptual framework\, with which we could unambiguo
 usly define and measure the amount of positional information in any set of
  developmental gene expression profiles. Here we show how to measure this 
 information\, in bits\, using the gap genes in the Drosophila embryo as an
  example. Individual genes carry nearly two bits of information\, twice as
  much as would be expected if the expression patterns consisted only of on
 /off domains separated by sharp boundaries. Taken together\, four gap gene
 s carry enough information to define a cell’s location with an error bar
  of ~1% along the anterior/posterior axis of the embryo. This precision is
  nearly enough for each cell to have a unique identity\, which is the maxi
 mum information the system can use\, and is nearly constant along the leng
 th of the embryo. We argue that this constancy is a signature of optimalit
 y in the transmission of information from primary morphogen inputs to the 
 output of the gap gene network.Bio:\nDr. Tkačik joined IST Austria in 201
 1. Previously he was a postdoc with Vijay Balasubramanian and Phil Nelson 
 at University of Pennsylvania\, working on the theory of neural coding and
  specifically exploring population coding and adaptation in the retina. He
  finished his PhD at Princeton with Bill Bialek and Curt Callan in 2007\, 
 studying how biological networks can reliably transmit and process informa
 tion in the presence of intrinsic noise and corrupted signals. He is broad
 ly interested in uncovering general principles that underlie efficient bio
 logical computation.
LOCATION:SV1717A http://map.epfl.ch/?room=sv1717a
STATUS:CONFIRMED
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