Huang, L. ; Hansen, A. S. ; Pauleve, L. ; Unger, M. ; Zechner, C. ; Koeppl, H. (2016)
Reconstructing dynamic molecular states from single-cell time series.
In: Journal of The Royal Society Interface, 13 (122)
doi: 10.1098/rsif.2016.0533
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time evolution of the system in a self-consistent manner. This is a prerequisite for a principled understanding of the inner workings of a system. Owing to the complexity of intracellular processes, experimental techniques that can retrieve a sufficient summary are beyond our reach. For the case of stochastic biomolecular reaction networks, we show how to convert the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximations to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae, where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Huang, L. ; Hansen, A. S. ; Pauleve, L. ; Unger, M. ; Zechner, C. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Reconstructing dynamic molecular states from single-cell time series |
| Sprache: | Englisch |
| Publikationsjahr: | 2016 |
| Verlag: | Royal Society Publishing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of The Royal Society Interface |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 122 |
| DOI: | 10.1098/rsif.2016.0533 |
| URL / URN: | http://rsif.royalsocietypublishing.org/content/13/122/201605... |
| Kurzbeschreibung (Abstract): | The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time evolution of the system in a self-consistent manner. This is a prerequisite for a principled understanding of the inner workings of a system. Owing to the complexity of intracellular processes, experimental techniques that can retrieve a sufficient summary are beyond our reach. For the case of stochastic biomolecular reaction networks, we show how to convert the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximations to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae, where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements. |
| 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: | 15 Sep 2016 06:27 |
| Letzte Änderung: | 20 Nov 2023 13:47 |
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