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• W1893911863 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. Copyright © 2012 John Wiley & Sons, Ltd." @default.
• W1893911863 created "2016-06-24" @default.
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• W1893911863 date "2012-04-04" @default.
• W1893911863 modified "2023-09-26" @default.
• W1893911863 title "Accounting for extrinsic variability in the estimation of stochastic rate constants" @default.
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• W1893911863 doi "https://doi.org/10.1002/rnc.2804" @default.
• W1893911863 hasPublicationYear "2012" @default.
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