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Accounting for extrinsic variability in the estimation of stochastic rate constants


Koeppl, Heinz; Zechner, Christoph; Ganguly, Arnab; Pelet, Serge; Peter, Matthias (2012). Accounting for extrinsic variability in the estimation of stochastic rate constants. International Journal of Robust and Nonlinear Control, 22(10):1103-1119.

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 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.

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 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.

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9 citations in Web of Science®
12 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > YeastX
Special Collections > SystemsX.ch > Research, Technology and Development Projects
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2012
Deposited On:12 Sep 2013 10:24
Last Modified:13 May 2016 10:32
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:1049-8923
Publisher DOI:https://doi.org/10.1002/rnc.2804
Permanent URL: https://doi.org/10.5167/uzh-80946

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