Zechner, C. ; Unger, M. ; Pelet, S. ; Peter, M. ; Koeppl, H. (2014)
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.
In: Nature methods, 11 (2)
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Zechner, C. ; Unger, M. ; Pelet, S. ; Peter, M. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings |
| Sprache: | Englisch |
| Publikationsjahr: | Februar 2014 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Nature methods |
| Jahrgang/Volume einer Zeitschrift: | 11 |
| (Heft-)Nummer: | 2 |
| URL / URN: | http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2794.h... |
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| Kurzbeschreibung (Abstract): | Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy 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: | 04 Apr 2014 11:41 |
| Letzte Änderung: | 24 Jul 2023 12:47 |
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