Statistical inference of single-cell and single-molecule dynamics
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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)
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| 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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