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A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number
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Woodcock, Dan J., Vance, Keith W., Komorowski, Michal, Koentges, Georgy, Finkenstädt, Bärbel and Rand, D. A. (David A.) (2013) A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number. Bioinformatics, Volume 29 (Number 12). pp. 1519-1525. doi:10.1093/bioinformatics/btt201 ISSN 1367-4803.
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WRAP_Woodock_Bioinformatics-2013-Woodcock-1519-25.pdf - Published Version Available under License Creative Commons Attribution. Download (721Kb) | Preview |
Official URL: http://dx.doi.org/10.1093/bioinformatics/btt201
Abstract
Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown.
Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QH Natural history > QH301 Biology Q Science > QH Natural history > QH426 Genetics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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Library of Congress Subject Headings (LCSH): | Cytology, Cytology -- Research, Genetics -- Research , Genomics , Genetics , Microbiolgy, Microbial genetics, Bayesian statistical decision theory | ||||
Journal or Publication Title: | Bioinformatics | ||||
Publisher: | Oxford University Press | ||||
ISSN: | 1367-4803 | ||||
Official Date: | 15 June 2013 | ||||
Dates: |
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Volume: | Volume 29 | ||||
Number: | Number 12 | ||||
Page Range: | pp. 1519-1525 | ||||
DOI: | 10.1093/bioinformatics/btt201 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 24 December 2015 | ||||
Date of first compliant Open Access: | 24 December 2015 | ||||
Funder: | Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Engineering and Physical Sciences Research Council (EPSRC), BioSim, University of Warwick, Foundation for Polish Science [Fundacja na rzecz Nauki Polskiej] (FNP), Wellcome Trust (London, England), Human Frontiers Science Program (HFSP) | ||||
Grant number: | BB/F005814/1 (BBSRC) ; GR/S29256/01, EP/C5445871/1 (EPSRC) ; 005147 (BS) ; 066790/E/02/Z, 066745/2/01/Z (WT) ; RGP0029/2007C |
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