Model learning to identify systemic regulators of the peripheral circadian clock.

Area of Science:

• Chronobiology
• Systems Biology
• Personalized Medicine

Background:

• Personalized medicine seeks patient-tailored treatments using diverse data for better outcomes.
• Chronotherapy, adapting drug timing to circadian rhythms, can be enhanced by personalized approaches.
• Individual variability in circadian rhythms necessitates monitoring and predictive modeling for optimal drug timing.

Purpose of the Study:

• To develop a methodology for predicting peripheral circadian clocks and optimal drug timing from circadian biomarkers.
• To investigate the molecular influence of systemic regulators (e.g., temperature, hormones) on peripheral clocks.
• To leverage mouse datasets due to conserved circadian systems between mice and humans.

Main Methods:

• Employed a model-learning approach using systems biology models based on ordinary differential equations.
• Utilized an existing circadian clock model as prior knowledge to approximate systemic regulator actions.
• Fitted time profiles of core-clock gene expression (Bmal1, Per2, Rev-Erbα) using linear regression models.

Main Results:

• Identified temperature or nutrient exposure cycles as likely modulators of Bmal1 or Per2 transcription.
• Confirmed biological knowledge regarding temperature-dependent control of Per2 transcription.
• Observed significant differences in the strength of systemic regulations based on mouse sex and genetic background.

Conclusions:

• Systemic factors, particularly temperature and nutrient cycles, play a crucial role in regulating peripheral circadian clocks at the molecular level.
• The developed modeling approach provides a foundation for predicting individual circadian variations and optimizing drug timing.
• Understanding sex and genetic influences on circadian regulation is vital for advancing personalized chronotherapy.