Quantifying heterogeneity of stochastic gene expression

Keita Iida, Nobuaki Obata, Yoshitaka Kimura

Research output: Contribution to journalArticlepeer-review

Abstract

The heterogeneity of stochastic gene expression, which refers to the temporal fluctuation in a gene product and its cell-to-cell variation, has attracted considerable interest from biologists, physicists, and mathematicians. The dynamics of protein production and degradation have been modeled as random processes with transition probabilities. However, there is a gap between theory and phenomena, particularly in terms of analytical formulation and parameter estimation. In this study, we propose a theoretical framework in which we present a basic model of a gene regulatory system, derive a steady-state solution, and provide a Bayesian approach for estimating the model parameters from single-cell experimental data. The proposed framework is demonstrated to be applicable for various scales of single-cell experiments at both the mRNA and protein levels and is useful for comparing kinetic parameters across species, genomes, and cell strains.

Original languageEnglish
Pages (from-to)56-62
Number of pages7
JournalJournal of Theoretical Biology
Volume465
DOIs
Publication statusPublished - 2019 Mar 21

Keywords

  • Lac operon
  • Master equation
  • Metropolis algorithm
  • Stochastic process

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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