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Area of Science:

  • Systems Biology
  • Biophysics
  • Molecular Biology

Background:

  • Gene expression exhibits intrinsic stochastic variability, extensively studied.
  • Stochastic reaction delay, arising from heterogeneity and fluctuations, also impacts gene expression but is less understood.
  • This delay affects how reactants like mRNA and proteins explore their cellular environment.

Purpose of the Study:

  • To analyze the impact of stochastic reaction delay on gene expression dynamics.
  • To investigate a non-Markovian model of bursty gene expression with a general delay distribution.
  • To elucidate the role of reaction delay in modulating protein levels and noise.

Main Methods:

  • Analysis of a non-Markovian model for bursty gene expression.
  • Mathematical modeling incorporating general delay distributions.
  • Analytical derivations and numerical simulations.

Main Results:

  • Stochastic reaction delay is analytically shown to be equivalent to negative feedback.
  • The stationary protein distribution depends only on the mean delay, not its specific distribution.
  • Numerical results demonstrate that reaction delay amplifies mean protein levels and significantly reduces protein noise.

Conclusions:

  • Stochastic reaction delay is a crucial factor influencing gene expression.
  • Understanding reaction delay is essential for comprehending gene expression variability and regulation.
  • The findings provide insights into the mechanisms controlling protein noise in cellular systems.