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SUMMARY:Inference of size dependence of transcription parameters from sing
 le cell data using multi-scale models of gene expression - Vahid Shahrezae
 i ()
DTSTART:20160405T150000Z
DTEND:20160405T154500Z
UID:TALK65306@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Co-authors: Anthony Bowman (Imperial College London)\, X
 i-Ming Sun (MRC CSC)\, Samuel Marguerat (MRC CSC)<br></span><span><br>Gene
  expression is affected by both random timing of reactions (intrinsic nois
 e) and interaction with global stochastic systems in the cells (extrinsic 
 noise). A challenge in inferring parameters of gene expression using model
 s of stochastic gene expression is that these models usually only inlcude 
 intrinsic noise. However\, experimental distributions of transcripts are s
 trongly influenced by extrinsic effects including cell cycle and cell divi
 sion. Here\, we present a multi-scale approach in stochastic gene expressi
 on to deal with this problem. We apply our methodology to data obtained us
 ing single molecule Fish technique in fission yeast. The data suggests cel
 l size influences transcription parameters. We use Approximate Bayesian Co
 mputation (ABC) along with sequential Monte Carlo to infer the dependence 
 of gene expression parameters on cell size. Our analysis reveals a linear 
 increase of transcription burst size during the cell cycle.</span>
LOCATION:Seminar Room 1\, Newton Institute
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