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Circadian Rhythms and Gene Regulation02:19

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The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...
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Updated: May 14, 2026

Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters
10:38

Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters

Published on: September 27, 2012

Deterministic versus stochastic models for circadian rhythms.

D Gonze1, J Halloy, A Goldbeter

  • 1Unité de Chronobiologie Théorique, Faculté des Sciences, Université Libre de Bruxelles, Campus Plaine, C.P. 231, B-1050 Brussels, Belgium.

Journal of Biological Physics
|January 25, 2013
PubMed
Summary
This summary is machine-generated.

Molecular noise impacts circadian rhythms, but robust oscillations persist even with low molecule counts. Stochastic models closely align with deterministic predictions for these biological clocks.

Keywords:
circadian rhythmsmolecular noiserobustnessstochastic simulations

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Area of Science:

  • * Biochemistry and Molecular Biology
  • * Systems Biology
  • * Chronobiology

Background:

  • * Circadian rhythms, essential biological processes with ~24-hour cycles, are driven by gene expression's negative autoregulation.
  • * Deterministic models explain circadian rhythms under constant conditions, entrainment by light, and phase shifts.
  • * Molecular noise becomes significant at low molecule counts, necessitating stochastic simulations.

Purpose of the Study:

  • * To investigate the impact of molecular noise on circadian rhythms using stochastic models.
  • * To compare stochastic model predictions with a deterministic core model for circadian oscillations.

Main Methods:

  • * Two stochastic versions of a core circadian rhythm model (PER protein in Drosophila, FRQ protein in Neurospora) were developed.
  • * A non-developed stochastic model introduced noise without detailed reaction steps.
  • * A developed stochastic model incorporated detailed reaction steps.
  • * The Gillespie method was used for numerical simulations of both stochastic models.

Main Results:

  • * Robust circadian oscillations were observed even with reduced numbers of mRNA and nuclear proteins (tens and hundreds, respectively).
  • * Both stochastic model versions yielded similar results.
  • * Stochastic models showed good agreement with deterministic model predictions for circadian rhythms.
  • * The influence of proximity to a bifurcation point was assessed.

Conclusions:

  • * Stochastic simulations are crucial for understanding molecular noise effects in circadian rhythms at low molecule counts.
  • * The developed and non-developed stochastic models provide reliable insights into circadian rhythm dynamics.
  • * The findings support the robustness of circadian oscillations despite molecular fluctuations.