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SUMMARY:From live cell fluorescence microscopy to moment-based variational
  inference
DTSTART:20211118T160000
DTSTAMP:20260512T015114Z
UID:afdda30535a45eabc4738cb25a65d5b5a0e7d1dc449d6e393355bb6b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Heinz Köppl\, TU Darmstadt\nCharacterization of biomolecular 
 circuit components is important for the design of novel synthetic biology 
 applications. Reliable models of such components need to account for intri
 nsic and extrinsic noise present in the cellular environment. Stochastic k
 inetic models provide a principled framework for developing quantitatively
  predictive tools in this scenario. Calibration of such models requires an
  experimental setup capable of monitoring a large number of individual cel
 ls over time\, automatic extraction of fluorescence levels for each cell a
 nd a scalable inference approach. In the first part of the talk we will co
 ver our microfluidic setup and a deep-learning based approach to cell segm
 entation and data extraction. The second part will introduce moment-based 
 variational inference as a scalable framework for approximate inference of
  kinetic models based on single cell data.
LOCATION:https://epfl.zoom.us/j/85131999647?pwd=TUpoYWE4MnQ4KzZnbDVhSTRBZH
 h4UT09
STATUS:CONFIRMED
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