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SUMMARY:Statistical inference of single-cell and single-molecule dynamics 
 - Heinz Koeppl (Technische Universität Darmstadt)
DTSTART:20160624T084500Z
DTEND:20160624T093000Z
UID:TALK66569@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Single-cell and single-molecule experimental techniques expose
  the randomness of cellular processes and invite a&nbsp\;stochastic&nbsp\;
 description. In this talk I will present our efforts to solve inverse prob
 lems related to stochastic cellular dynamics. First\, we provide a inferen
 ce framework that accounts for extrinsic and intrinsic noise contributions
  present in single-cell measurements. For that\, we show that stochastic c
 omponents of a cellular process can be marginalised exactly such that the 
 inference remains tractable. Second\, we present single-molecule experimen
 tal data to study transcriptional kinetics in live yeast cells. A stochast
 ic models for the system is&nbsp\;presented and biophysical parameters suc
 h elongation speed\, termination rate etc are inferred from single transcr
 iption-site intensities. Moreover\, optimal filtering or state estimation 
 is performed to reconstruct the most likely position of single RNAP molecu
 les on the gene.&nbsp\;&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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