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SUMMARY:Uncovering signaling differences between normal and transformed he
 patocytes using cell-specific pathway models - Julio Saez-Rodriguez (EBI)
DTSTART:20100705T150000Z
DTEND:20100705T160000Z
UID:TALK24759@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:Pathway maps are useful abstractions of signaling networks but
  have two key limitations: they are not computable models that can be comp
 ared to functional data\, and they are not cell-specific\, a significant l
 imitation because it is precisely biochemical differences between normal a
 nd diseased cells that are targeted for pharmaceutical intervention. \n\nW
 e have recently developed an efficient method to construct predictive logi
 c models of signaling networks based on generic pathway maps and high-thro
 ughput functional data (Saez-Rodriguez et al.\, Mol. Sys. Biol.\, 5:331\, 
 2009). The method is embedded in the toolbox CellNetOptimizer that works i
 n concert with DataRail (Saez-Rodriguez et al\, Bioinformatics\, 2008)\, a
  complementary toolbox for managing and transforming varied data. \n\nWe a
 pply the method to distinguishing the topologies of immediate early signal
 ing networks in primary human hepatocytes and four hepatocellular carcinom
 a (HCC) cell lines. We show that five distinct models cluster topologicall
 y into normal and diseased sets\, revealing functional differences between
  normal and diseased cells that involve activation of growth factor recept
 ors and intracellular kinase cascades. In a proof-of-principle experiment 
 we also infer a target for an inhibitor developed to treat arthritis and a
 irway inflammation.\n\nHosted by Florian Markowetz.
LOCATION:Cancer Research UK Cambridge Research Institute\, Lecture Theatre
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