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SUMMARY:Gene Regulatory Network Inference: A Kernel-Based Learning Approac
 h - Sandy Klemm\, University of Cambridge
DTSTART:20071126T150000Z
DTEND:20071126T160000Z
UID:TALK9311@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:One of the central challenges of modern biology is to understa
 nd the dynamic architecture of gene regulatory networks. To address this p
 roblem\, we introduce a framework for large-scale nonlinear system identif
 ication derived from kernel learning theory. The proposed inference techni
 que is based on a nonparametric differential equation model of mRNA transc
 ription and has been used to successfully reverse engineer a diverse class
  of synthetic gene regulatory networks. For synthetic networks\, we derive
  estimates for both the time scale of mRNA degradation as well as the post
 -transcriptional time delay related to protein synthesis and activation.
LOCATION:Engineering Department\, CBL Room 438
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