title: A stochastic transcriptional switch model for single cell imaging data.
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.
authors.affiliation: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Kirsty L
authors.initials: KL
authors.lastname: Hey
authors.affiliation: Warwick Systems Biology, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Momiji
authors.affiliation: Centre for Endocrinology and Diabetes, University of Manchester, Manchester M13 9PT, UK.
authors.forename: Karen
authors.initials: K
authors.lastname: Featherstone
authors.affiliation: Centre for Endocrinology and Diabetes, University of Manchester, Manchester M13 9PT, UK.
authors.forename: Julian R E
authors.initials: JR
authors.lastname: Davis
authors.affiliation: Systems Biology Centre, University of Manchester, Manchester M13 9PL, UK.
authors.forename: Michael R H
authors.initials: MR
authors.lastname: White
authors.affiliation: Warwick Systems Biology, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: David A
authors.initials: DA
authors.lastname: Rand
authors.affiliation: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK B.F.Finkenstadt@warwick.ac.uk.
authors.forename: Bärbel
authors.initials: B
authors.lastname: Finkenstädt
mesh_terms: D000818:Animals; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D061848:Optical Imaging; D051381:Rats; D059010:Single-Cell Analysis; D013269:Stochastic Processes; D014158:Transcription, Genetic
keywords: Bayesian hierarchical model; Birth and death processes; Gene expression; Linear noise approximation; Particle Gibbs; Reversible jump MCMC; State-space models; Stochastic reaction networks
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