Bronstein, L. ; Koeppl, H. (2016)
Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks.
In: 2016 IEEE 55th Conference on Decision and Control (CDC)
doi: 10.1109/CDC.2016.7798361
Buchkapitel, Bibliographie
Kurzbeschreibung (Abstract)
Inferring quantities of interest from fluorescence microscopy time-lapse measurements of cells is a key step in parameterizing models of biomolecular reaction networks, and also in comparing different models. In this article, we propose a method which performs inference in continuous-time Markov chain models and thus takes into account the discrete nature of molecule counts. It targets the important situation of inference from many measured cells. Our method, a complement to a recently proposed approach, is based on particle Markov chain Monte Carlo and can be argued to have improved scaling behavior as the number of measured cells increases. We numerically demonstrate the performance of our algorithm on simulated data.
| Typ des Eintrags: | Buchkapitel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Bronstein, L. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2016 |
| Ort: | Las Vegas |
| Verlag: | IEEE |
| Buchtitel: | 2016 IEEE 55th Conference on Decision and Control (CDC) |
| DOI: | 10.1109/CDC.2016.7798361 |
| URL / URN: | http://ieeexplore.ieee.org/document/7798361/#full-text-secti... |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Inferring quantities of interest from fluorescence microscopy time-lapse measurements of cells is a key step in parameterizing models of biomolecular reaction networks, and also in comparing different models. In this article, we propose a method which performs inference in continuous-time Markov chain models and thus takes into account the discrete nature of molecule counts. It targets the important situation of inference from many measured cells. Our method, a complement to a recently proposed approach, is based on particle Markov chain Monte Carlo and can be argued to have improved scaling behavior as the number of measured cells increases. We numerically demonstrate the performance of our algorithm on simulated data. |
| Zusätzliche Informationen: | Conference: 12-14 December 2016, Las Vegas, NV, USA |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 02 Sep 2016 06:19 |
| Letzte Änderung: | 29 Mai 2024 07:12 |
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