Stochastic effects of multiple regulators on expression profiles in eukaryotes

Pawel Paszek, Tomasz Lipniacki, Allan R. Brasier, Bing Tian, David E. Nowak, Marek Kimmel

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

The stochastic nature of gene regulation still remains not fully understood. In eukaryotes, the stochastic effects are primarily attributable to the binary nature of genes, which are considered either switched "on" or "off" due to the action of the transcription factors binding to the promoter. In the time period when the gene is activated, bursts of mRNA transcript are produced. In the present paper, we investigate regulation of gene expression at the single cell level. We propose a mechanism of gene regulation, which is able to explain the observed distinct transcription profiles assuming the number of co-regulatory activities, without attempting to identify the specific proteins involved. The model is motivated by our experiments on NF-κB-dependent genes in HeLa cells. Our experimental data shows that NF-κB-dependent genes can be stratified into three characteristic groups according to their expression profiles: early, intermediate and late having maximum of expression at about 1, 3 and 6 h, respectively, from the beging of TNF stimulation. We provide a tractable analytical approach, not only in the terms of expected expression profiles and their moments, which corresponds to the measurements on the cell population, but also in the terms of single cell behavior. Comparison between these two modes of description reveals that single cells behave qualitatively different from the cell population. This analysis provides insights useful for understanding of microarray experiments.

Original languageEnglish (US)
Pages (from-to)423-433
Number of pages11
JournalJournal of Theoretical Biology
Volume233
Issue number3
DOIs
StatePublished - Apr 7 2005

Keywords

  • Expression profiles
  • NF-κB
  • Single cell simulations
  • Stochastic gene regulation

ASJC Scopus subject areas

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

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