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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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A mixed-effects cosinor modelling framework for circadian gene expression.

Michael T Gorczyca1

  • 1MTG Research Consulting, Pittsburgh, Pennsylvania, USA.

Journal of Theoretical Biology
|November 4, 2025
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Summary

This study introduces a new method to accurately analyze gene expression rhythms across populations. It corrects for individual differences in circadian clocks, preventing biased results and improving the detection of gene expression oscillations relevant to health.

Keywords:
Circadian biologyCircular dataDim-light melatonin onsetMeasurement errorMixed-effects models

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Area of Science:

  • Chronobiology
  • Genomics
  • Biostatistics

Background:

  • Circadian rhythms influence molecular and physiological processes, including gene expression.
  • Individual variations in circadian clocks (phase offsets) pose challenges for population-level analysis.
  • Standard cosinor models can produce biased amplitude estimates due to uncorrected phase offsets.

Purpose of the Study:

  • To develop a novel method for estimating population-level cosinor model parameters for gene expression.
  • To mitigate attenuation bias in amplitude estimates caused by individual circadian phase offsets.
  • To provide a cost-effective alternative to laboratory testing for determining individual phase offsets.

Main Methods:

  • Estimating population-level cosinor parameters per gene without individual offsets.
  • Estimating individual-level cosinor parameters per person and gene.
  • Computing data-driven phase offsets for each individual.
  • Re-estimating population-level cosinor parameters incorporating data-driven offsets.

Main Results:

  • The proposed method effectively mitigates attenuation bias in population-level amplitude estimates.
  • Hypothesis testing for gene expression oscillations shows improved accuracy.
  • Simulation studies confirm the method's effectiveness.
  • Real-world data application closely matches results from laboratory-based offset determination.

Conclusions:

  • This novel method accurately estimates population-level circadian gene expression parameters without expensive lab tests.
  • It improves the detection of health-associated gene expression rhythms by correcting for individual phase differences.
  • The approach offers a robust and scalable solution for chronobiological research.