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Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

Abhyudai Singh1, Mohammad Soltani2

  • 1Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America ; Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America ; Department of Mathematical Sciences, University of Delaware, Newark, Delaware, United States of America.

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Cellular gene expression shows variability due to intrinsic and extrinsic noise. Fluctuations in transcription burst size or translation rate significantly increase both noise types, impacting gene expression.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Biophysics

Background:

  • Genetically identical cells display significant intercellular variation in protein and mRNA levels.
  • This gene expression variability stems from both intrinsic (stochastic biochemical processes) and extrinsic (cell-specific factors) noise sources.

Purpose of the Study:

  • To mathematically model and decompose gene expression variability into intrinsic and extrinsic noise components.
  • To analyze how fluctuations in transcription and translation rates affect these noise components.

Main Methods:

  • Utilized two-color reporter experiments to distinguish intrinsic and extrinsic noise.
  • Derived analytical formulas for intrinsic and extrinsic noise within stochastic gene expression models.
  • Investigated the impact of variations in transcription burst frequency/size and translation rates.

Main Results:

  • Fluctuations in transcription burst frequency increase extrinsic noise but not intrinsic noise.
  • Variations in transcription burst size or mRNA translation rate significantly elevate both intrinsic and extrinsic noise.
  • Simultaneous fluctuations in transcription and translation rates, due to ATP variability, can reduce intrinsic noise.

Conclusions:

  • The study provides a framework for quantifying noise sources in gene expression.
  • Understanding these noise dynamics is crucial for interpreting single-cell gene expression data.
  • The derived formulas can aid in parameter estimation for gene expression models using experimental data.