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A detailed predictive model of the mammalian circadian clock

Open access
Forger, Daniel B., Peskin, Charles S.·2003·Proceedings of the National Academy of Sciences

Abstract

Experimental data on the circadian (≈24-h) clock in mammalian cells are vast, diverse, and detailed. Mathematical models are therefore needed to piece these data together and to study overall clock behavior. Previous models have focused on Neurospora or Drosophila or can be converted to a Drosophila model simply by renaming variables. Those models used Hill-type terms for transcription regulation and Michaelis–Menten type or delay terms for posttranslation regulation. Recent mammalian experimental data call into question some of the assumptions in these approaches. Moreover, gene duplication has led to more proteins in the mammalian system than in lower organisms. Here we develop a detailed distinctly mammalian model by using mass action kinetics. Parameters for our model are found from experimental data by using a coordinate search method. The model accurately predicts the phase of entrainment, amplitude of oscillation, and shape of time profiles of clock mRNAs and proteins and is also robust to parameter changes and mutations.

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Metadata

Authors
Forger, D. B., & Peskin, C. S.
Venue
Proceedings of the National Academy of Sciences
Published
2003-12
Volume
100
Issue
25
Pages
14806-14811
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2112215450
Cited by
353
References
4
FWCI
5.91
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A detailed predictive model of the mammalian circadian clock. https://app.cochat.ai/papers/10.1073/pnas.2036281100
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Forger, D. B., & Peskin, C. S. (2003). A detailed predictive model of the mammalian circadian clock. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2036281100

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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.