A stochastic model of gene transcription: An application to L1 retrotransposition events

Grzegorz A. Rempala, Kenneth S. Ramos, Ted Kalbfleisch

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


A simplified mathematical model of gene transcription is presented based on a system of coupled chemical reactions and a corresponding set of stochastic equations similar to those used in enzyme kinetics theory. The quasi-stationary distribution for the model is derived and its usefulness illustrated with an example of model parameters estimation using sparse time course data on L1 retrotransposon expression kinetics. The issue of model validation is also discussed and a simple validation procedure for the estimated model is devised. The procedure compares model predicted values with the laboratory data via the standard Bayesian techniques with the help of modern Markov-Chain Monte-Carlo methodology.

Original languageEnglish
Pages (from-to)101-116
Number of pages16
JournalJournal of Theoretical Biology
Issue number1
StatePublished - Sep 7 2006

Bibliographical note

Funding Information:
The first author would like to acknowledge the generous research support for this work from the Center for Genetics and Molecular Medicine at the University of Louisville. All authors would like to acknowledge the anonymous referee whose thoughtful comments and suggestions helped improve the original manuscript.


  • Bayesian inference
  • Chemical reaction kinetics
  • Gene transcription model
  • L-retrotransposon
  • Northern blot
  • Reaction constants estimation
  • Stochastic intracellular network

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


Dive into the research topics of 'A stochastic model of gene transcription: An application to L1 retrotransposition events'. Together they form a unique fingerprint.

Cite this