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Accounting for extrinsic variability in the estimation of stochastic rate constants
Author(s) -
Koeppl Heinz,
Zechner Christoph,
Ganguly Arnab,
Pelet Serge,
Peter Matthias
Publication year - 2012
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.2804
Subject(s) - noise (video) , inference , bayesian probability , stochastic modelling , bayesian inference , biological system , budding yeast , statistical inference , statistical physics , expression (computer science) , biology , mathematics , statistics , computer science , econometrics , yeast , saccharomyces cerevisiae , physics , genetics , artificial intelligence , programming language , image (mathematics)
SUMMARY 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. Copyright © 2012 John Wiley & Sons, Ltd.