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A stochastic transcriptional switch model for single cell imaging data

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Hey, Kirsty L., Momiji, Hiroshi, Featherstone, Karen, Davis, Julian R.E., White, Michael R.H., Rand, D. A. (David A.) and Finkenstädt, Bärbel (2015) A stochastic transcriptional switch model for single cell imaging data. Biostatistics, 16 (4). pp. 655-669. doi:10.1093/biostatistics/kxv010

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Official URL: http://dx.doi.org/10.1093/biostatistics/kxv010

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Abstract

Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic variability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth-death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells.

Item Type: Journal Article
Subjects: Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science > Statistics
Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH): Biometry, Biometry -- Statistical methods, Gene expression, Bayesian statistical theory
Journal or Publication Title: Biostatistics
Publisher: Oxford University Press
ISSN: 1465-4644
Official Date: October 2015
Dates:
DateEvent
October 2015Published
25 March 2015Available
26 March 2015Accepted
Volume: 16
Number: 4
Page Range: pp. 655-669
DOI: 10.1093/biostatistics/kxv010
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), Wellcome Trust (London, England)
Grant number: RSMAA.3020.SRA, 67252

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