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Robustness of circadian rhythms with respect to molecular noise

Open access
Gonze, Didier, Halloy, José, Goldbeter, Albert·2002·Proceedings of the National Academy of Sciences

Abstract

We use a core molecular model capable of generating circadian rhythms to assess the robustness of circadian oscillations with respect to molecular noise. The model is based on the negative feedback exerted by a regulatory protein on the expression of its gene. Such a negative regulatory mechanism underlies circadian oscillations of the PER protein in Drosophila and of the FRQ protein in Neurospora . The model incorporates gene transcription into mRNA, translation of mRNA into protein, reversible phosphorylation leading to degradation of the regulatory protein, transport of the latter into the nucleus, and repression of gene expression by the nuclear form of the protein. To assess the effect of molecular noise, we perform stochastic simulations after decomposing the deterministic model into elementary reaction steps. The oscillations predicted by the stochastic simulations agree with those obtained with the deterministic version of the model. We show that robust circadian oscillations can occur already with a limited number of mRNA and protein molecules, in the range of tens and hundreds, respectively. Entrainment by light/dark cycles and cooperativity in repression enhance the robustness of circadian oscillations with respect to molecular noise.

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Metadata

Authors
Gonze, D., Halloy, J., & Goldbeter, A.
Venue
Proceedings of the National Academy of Sciences
Published
2002-01-15
Volume
99
Issue
2
Pages
673-678
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2066440682
Cited by
401
References
29
FWCI
6.49
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Robustness of circadian rhythms with respect to molecular noise. https://app.cochat.ai/papers/10.1073/pnas.022628299
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Gonze, D., Halloy, J., & Goldbeter, A. (2002). Robustness of circadian rhythms with respect to molecular noise. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.022628299

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.