Accounting for extrinsic variability in the estimation of stochastic rate constants

Détails

ID Serval
serval:BIB_9173830ECF8F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Accounting for extrinsic variability in the estimation of stochastic rate constants
Périodique
International Journal of Robust Nonlinear Control
Auteur(s)
Koeppl H., Zechner C., Ganguly A., Pelet S., Peter M.
ISSN
1049-8923
Statut éditorial
Publié
Date de publication
2012
Volume
221
Numéro
10
Pages
1103-1119
Langue
anglais
Résumé
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 stageto 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 because of molecular noise. Here, we present a Bayesian inference scheme that deconvolutes 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 osmostress-induced transcriptional activation in budding yeast.
Mots-clé
cell-to-cell variability, stochastic chemical kinetics, mass conservation, Bayesian estimation, MAPK Hog1 signaling pathway
Web of science
Création de la notice
22/10/2012 13:17
Dernière modification de la notice
03/03/2018 19:28
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