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Oscillations in probability distributions for stochastic gene expression.

K G Petrosyan1, Chin-Kun Hu1

  • 1Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan.

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Stochastic gene expression models show oscillations in molecule numbers, favoring even counts at low levels. This even-odd symmetry shifts towards a Poisson distribution as molecule counts rise.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Stochasticity is inherent in biological systems, particularly in gene expression.
  • Understanding molecular noise is crucial for predicting cellular behavior.
  • Previous models often assume continuous variables or large molecule numbers.

Purpose of the Study:

  • To investigate oscillations in probability distribution functions for molecular counts in stochastic gene expression.
  • To identify conditions under which even-odd particle number symmetry emerges and disappears.
  • To assess the experimental observability of these oscillations.

Main Methods:

  • Development of a stochastic model for gene expression.
  • Analysis of probability distribution functions for molecule numbers.
  • Simulation of systems with low molecule counts and high intracellular noise.

Main Results:

  • Observed oscillations in probability distributions, distinguishing between even and odd numbers of molecules.
  • Demonstrated that oscillations occur at low molecule counts and high intracellular noise.
  • Showed restoration of even-odd symmetry as molecule numbers increase, with distributions approaching Poisson.

Conclusions:

  • The study reveals a novel even-odd oscillation phenomenon in stochastic gene expression.
  • This phenomenon provides a potential marker for low molecule counts and high noise environments.
  • Suggests avenues for experimental validation in gene, protein, and mRNA expression studies.