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Effect of promoter architecture on the cell-to-cell variability in gene expression.

According to recent experimental evidence, promoter architecture, defined by the number, strength and regulatory role of the operators that control transcription, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect variability in gene expression in a systematic rather than case-by-case fashion. In this article we make such a systematic investigation, based on a microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcriptional output from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can be used to test kinetic models of gene regulation. The emphasis of the discussion is on prokaryotic gene regulation, but our analysis can be extended to eukaryotic cells as well.

Pubmed ID: 21390269

Authors

  • Sanchez A
  • Garcia HG
  • Jones D
  • Phillips R
  • Kondev J

Journal

PLoS computational biology

Publication Data

March 10, 2011

Associated Grants

  • Agency: NIH HHS, Id: DP1 OD000217
  • Agency: NIH HHS, Id: DP1 OD000217
  • Agency: NIH HHS, Id: DP1 OD000217-05
  • Agency: NIGMS NIH HHS, Id: GM43369
  • Agency: NIGMS NIH HHS, Id: GM81648
  • Agency: NIGMS NIH HHS, Id: R01 GM085286
  • Agency: NIGMS NIH HHS, Id: R01 GM085286
  • Agency: NIGMS NIH HHS, Id: R01 GM085286-01S
  • Agency: NIGMS NIH HHS, Id: R01 GM085286-01S1
  • Agency: NIGMS NIH HHS, Id: R01 GM085286-04

Mesh Terms

  • Algorithms
  • Binding Sites
  • Computational Biology
  • Escherichia coli
  • Gene Expression Regulation, Bacterial
  • Kinetics
  • Models, Genetic
  • Molecular Dynamics Simulation
  • Poisson Distribution
  • Promoter Regions, Genetic
  • Repressor Proteins
  • Stochastic Processes