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SUMMARY:Model Based Target Identification from Expression Data - Neil Lawr
 ence (University of Sheffield)
DTSTART:20120206T160000Z
DTEND:20120206T170000Z
UID:TALK34517@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:A simple approach to target identification through gene expres
 sion studies has been to cluster the expression profiles and look for core
 gulated genes within clusters. Within systems biology mechanistic models o
 f gene expression are typically constructed through differential equations
 . mRNA's production is taken to be proportional to transcription factor ac
 tivity (with the proportionality given by the sensitivity) and the mRNA is
  assumed to decay at a particular rate. The assumption that coregulated ge
 nes have similar profiles is equivalent to assuming both the decay and the
  sensitivity are high.\n\nTypically researchers either use a data driven a
 pproach (such as clustering) or a model based approach (such as differenti
 al equations). In this talk we advocate hybrid techniques which have aspec
 ts of the mechanistic and data driven models. We combine simple differenti
 al equation models with Gaussian process priors to make probabilistic mode
 ls with mechanistic underpinnings. We show applications in target identifi
 cation from mRNA measurements.
LOCATION:Cancer Research UK Cambridge Research Institute\, Lecture Theatre
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