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Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks

Bronstein, L. ; Koeppl, H. (2016)
Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks.
55th IEEE Conference on Decision and Control. Las Vegas (December 2016)
Konferenzveröffentlichung, 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: Konferenzveröffentlichung
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
Veranstaltungstitel: 55th IEEE Conference on Decision and Control
Veranstaltungsort: Las Vegas
Veranstaltungsdatum: December 2016
URL / URN: http://ieeexplore.ieee.org/document/7798361/#full-text-secti...
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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.

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: 23 Sep 2021 14:31
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