Statistical inference of single-cell and single-molecule dynamics
42 mins 1 sec,
76.85 MB,
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Description: |
Koeppl, H (Technische Universität Darmstadt)
Friday 24th June 2016 - 09:45 to 10:30 |
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Created: | 2016-07-04 16:40 |
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Collection: | Stochastic Dynamical Systems in Biology: Numerical Methods and Applications |
Publisher: | Isaac Newton Institute |
Copyright: | Koeppl, H |
Language: | eng (English) |
Distribution: | World (downloadable) |
Explicit content: | No |
Aspect Ratio: | 16:9 |
Screencast: | No |
Bumper: | UCS Default |
Trailer: | UCS Default |
Abstract: | Single-cell and single-molecule experimental techniques expose the randomness of cellular processes and invite a stochastic description. In this talk I will present our efforts to solve inverse problems related to stochastic cellular dynamics. First, we provide a inference framework that accounts for extrinsic and intrinsic noise contributions present in single-cell measurements. For that, we show that stochastic components of a cellular process can be marginalised exactly such that the inference remains tractable. Second, we present single-molecule experimental data to study transcriptional kinetics in live yeast cells. A stochastic models for the system is presented and biophysical parameters such elongation speed, termination rate etc are inferred from single transcription-site intensities. Moreover, optimal filtering or state estimation is performed to reconstruct the most likely position of single RNAP molecules on the gene. |
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