ArticleBiostatistics (Oxford, England)2015
A stochastic transcriptional switch model for single cell imaging data.
Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 32 citations in OpenAlex.
- Transcription Factor Pit-1 Affects Transcriptional Timing in the Dual-Promoter Human Prolactin Gene.Endocrinology · 2021Article
- A Mechanochemical Model of Transcriptional Bursting.Biophysical journal · 2020Article
- Multimodal transcriptional control of pattern formation in embryonic development.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Disentangling juxtacrine from paracrine signalling in dynamic tissue.PLoS computational biology · 2019Article
- Filtering and inference for stochastic oscillators with distributed delays.Bioinformatics (Oxford, England) · 2019Article
- Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.The journal of physical chemistry. B · 2019Article
- Bayesian inference on stochastic gene transcription from flow cytometry data.Bioinformatics (Oxford, England) · 2018Article
- Article
- ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data.BMC bioinformatics · 2017Article
- Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes.PLoS computational biology · 2017Article
- Article
- Role of Estrogen Response Element in the Human Prolactin Gene: Transcriptional Response and Timing.Molecular endocrinology (Baltimore, Md.) · 2016Article
- Single-cell transcriptome sequencing: recent advances and remaining challenges.F1000Research · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.