Koeppl, H. ; Zechner, C. ; Ganguly, A. ; Pelet, S. ; Peter, M. (2012)
Accounting for extrinsic variability in the estimation of stochastic rate constants.
In: International Journal of Robust and Nonlinear Control, 22 (10)
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Single-cell recordings of transcriptional and post-transcriptional processes reveal the inherent stochasticity of cellular events. However, to a large extent the observed variability in isogenic cell populations is due to extrinsic factors, such as difference in expression capacity, cell volume and cell cycle stage - to name a few. Thus, such experimental data represents a convolution of effects from stochastic kinetics and extrinsic noise sources. Recent parameter inference schemes for single-cell data just account for variability due to molecular noise. Here we present a Bayesian inference scheme which de-convolutes the two sources of variability and enables us to obtain optimal estimates of stochastic rate constants of low copy-number events and extract statistical information about cell-to-cell variability. In contrast to previous attempts, we model extrinsic noise by a variability in the abundance of mass-conserved species, rather than a variability in kinetic parameters. We apply the scheme to a simple model of the osmo-stress induced transcriptional activation in budding yeast.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Koeppl, H. ; Zechner, C. ; Ganguly, A. ; Pelet, S. ; Peter, M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Accounting for extrinsic variability in the estimation of stochastic rate constants |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2012 |
| Verlag: | Wiley-Blackwell |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Robust and Nonlinear Control |
| Jahrgang/Volume einer Zeitschrift: | 22 |
| (Heft-)Nummer: | 10 |
| URL / URN: | http://doi.wiley.com/10.1002/rnc.2804 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Single-cell recordings of transcriptional and post-transcriptional processes reveal the inherent stochasticity of cellular events. However, to a large extent the observed variability in isogenic cell populations is due to extrinsic factors, such as difference in expression capacity, cell volume and cell cycle stage - to name a few. Thus, such experimental data represents a convolution of effects from stochastic kinetics and extrinsic noise sources. Recent parameter inference schemes for single-cell data just account for variability due to molecular noise. Here we present a Bayesian inference scheme which de-convolutes the two sources of variability and enables us to obtain optimal estimates of stochastic rate constants of low copy-number events and extract statistical information about cell-to-cell variability. In contrast to previous attempts, we model extrinsic noise by a variability in the abundance of mass-conserved species, rather than a variability in kinetic parameters. We apply the scheme to a simple model of the osmo-stress induced transcriptional activation in budding yeast. |
| 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 12:25 |
| Letzte Änderung: | 23 Sep 2021 14:31 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum