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Related Experiment Video

Updated: Jun 6, 2026

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Published on: November 11, 2016

True or false: all genes are rhythmic.

Andrey A Ptitsyn¹, Jeffrey M Gimble

¹Center for Bioinformatics, Colorado State University, Fort Collins, CO 80525, USA. Andrey.Ptitsyn@colostate.edu

Annals of Medicine

|December 15, 2010

Summary

This summary is machine-generated.

Circadian regulation of gene expression is crucial across species. This review examines the number, drivers, and discrepancies in oscillating genes, exploring implications for circadian molecular biology.

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Area of Science:

Molecular Biology
Chronobiology
Genomics

Background:

Microarray studies confirm circadian regulation of gene expression across species.
Existing reports show significant variability in identified oscillating genes.
Understanding these oscillations is key to circadian molecular biology.

Purpose of the Study:

To explore the number of oscillating genes within tissues and systems.
To investigate the driving forces behind gene expression oscillations.
To identify methodological reasons for discrepancies in oscillating gene estimates.
To examine the physiological and systemic implications of oscillatory gene expression.

Main Methods:

Literature review of multiple microarray studies.
Analysis of existing data on gene expression patterns.
Hypothetical and speculative exploration of proposed paradigms.

Main Results:

Discrepancies exist in the number and identity of oscillating genes across studies.
Methodological differences contribute to variability in findings.
Oscillatory gene expression has significant physiological and systemic implications.

Conclusions:

Circadian gene regulation is complex and influenced by multiple factors.
Further research is needed to reconcile discrepancies and understand underlying mechanisms.
Novel paradigms are emerging, but current data has limitations.