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Area of Science:

  • Biophysics
  • Systems Biology
  • Molecular Biology

Background:

  • Gene expression is inherently noisy due to low molecule numbers (mRNA, protein).
  • Stochastic processes can be Markovian or non-Markovian, influencing reaction event timing.
  • Mechanistic models are crucial for predicting fluctuations in gene expression.

Purpose of the Study:

  • To analyze commonalities and extensions of stochastic gene expression models.
  • To focus on contributions of noise sources to protein levels.
  • To derive a formula for quantifying protein noise.

Main Methods:

  • Review and comparison of existing mechanistic models for stochastic gene expression.
  • Extension of models from Markov to non-Markov processes.
  • Derivation of a formula for protein noise (variance/mean^2).

Main Results:

  • A formula for protein noise is derived, expressed via probabilistic event frequencies.
  • The formula allows rapid evaluation of protein abundance fluctuations.
  • Protein noise is decomposed into spontaneous (birth/death) and forced (promoter switching) components.

Conclusions:

  • The derived formula provides insights into the origins of protein noise.
  • Understanding noise sources is key for predicting gene expression variability.
  • The framework aids in analyzing stochasticity in biological systems.