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Modeling stochastic gene expression under repression.

G C P Innocentini1, J E M Hornos

  • 1Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970, São Carlos, SP, Brazil. innocentini@ursa.ifsc.usp.br

Journal of Mathematical Biology
|May 23, 2007
PubMed
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This study models transcriptional noise using a stochastic approach. Results show that controlling protein synthesis with repression can minimize noise, aligning with experimental findings.

Area of Science:

  • Systems Biology
  • Molecular Biology
  • Biophysics

Background:

  • Gene expression involves intrinsic transcriptional noise from operator fluctuations.
  • Coupling transcription and translation amplifies these fluctuations.
  • Understanding noise is crucial for gene regulation.

Purpose of the Study:

  • Investigate intrinsic transcriptional noise using a stochastic model.
  • Analyze the impact of repression on gene expression noise.
  • Compare model predictions with experimental data.

Main Methods:

  • Developed a simple spin-like stochastic model for gene expression.
  • Incorporated coupling between transcription and translation.
  • Analyzed the effects of varying repression levels and degradation rates.

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Main Results:

  • A new noise term emerges in the presence of repression, dependent on mRNA production rate.
  • Noise is minimized when switch decay time is short relative to mRNA lifetime.
  • Protein production damping by repression is generally linear, but fluctuations can peak at intermediate repression.

Conclusions:

  • The interplay between switch decay time, mRNA, and protein degradation rates is key to effective repressive control with low noise.
  • The model's noise profiles quantitatively match recent experimental observations.
  • This work provides insights into stochastic gene expression and regulatory mechanisms.