A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number

Dan J Woodcock, Keith W Vance, Michal Komorowski, Georgy Koentges, Bärbel Finkenstädt, David A Rand

Research output: Contribution to journalArticle

5 Citations (Scopus)
91 Downloads (Pure)

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.

AVAILABILITY: Supporting information is submitted with the article.

Original languageEnglish
Pages (from-to)1519-25
Number of pages7
JournalBioinformatics
Volume29
Issue number12
DOIs
Publication statusPublished - 15 Jun 2013

Keywords

  • Algorithms
  • Animals
  • Bayes Theorem
  • Cell Line
  • Enhancer Elements, Genetic
  • Gene Dosage
  • MSX1 Transcription Factor
  • Mice
  • Models, Genetic
  • Promoter Regions, Genetic
  • Single-Cell Analysis
  • Transcription, Genetic

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