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Related Experiment Video

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Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH
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A stochastic transcriptional switch model for single cell imaging data.

Kirsty L Hey1, Hiroshi Momiji2, Karen Featherstone3

  • 1Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.

Biostatistics (Oxford, England)
|March 31, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new stochastic switch model to better understand gene expression variability in single cells. The model uses advanced computational methods to analyze gene transcription dynamics, improving our understanding of cellular processes.

Keywords:
Bayesian hierarchical modelBirth and death processesGene expressionLinear noise approximationParticle GibbsReversible jump MCMCState-space modelsStochastic reaction networks

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Systems Biology

Background:

  • Gene expression exhibits inherent stochasticity, crucial for understanding cellular function.
  • Stochastic Reaction Networks (SRNs) model intracellular biochemical processes and intrinsic variability.
  • Current models require enhancement to capture complex dynamic features of gene expression.

Purpose of the Study:

  • To extend existing SRN models by incorporating a random step or switch function for transcriptional processes.
  • To develop a generic framework for capturing diverse dynamic features in single-cell gene expression.
  • To address the challenges in inferring SRNs due to intractable transition densities.

Main Methods:

  • Developed a stochastic switch model for gene expression within SRNs.
  • Employed reversible jump Markov chain Monte Carlo (MCMC) for model estimation.
  • Derived and compared a model-specific birth-death approximation with the linear noise approximation within a state-space model framework.

Main Results:

  • The stochastic switch model provides a flexible framework for analyzing single-cell gene expression dynamics.
  • The birth-death approximation offers a viable method for inference in complex SRNs.
  • The methodology was successfully applied to both synthetic and experimental single-cell imaging data.

Conclusions:

  • The proposed stochastic switch model enhances the understanding of gene expression variability.
  • The developed inference methods facilitate the analysis of complex stochastic gene expression models.
  • This approach is applicable to real-world biological data, such as human prolactin gene expression in pituitary cells.