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Spontaneous synchronization of coupled circadian oscillators
Didier Gonze1, Samuel Bernard, Christian Waltermann
1Institute for Theoretical Biology, Humboldt Universität zu Berlin, Berlin, Germany.
Biophysical Journal
|April 26, 2005
Summary
This study models how neurotransmitters synchronize circadian rhythms in the suprachiasmatic nucleus (SCN). Global neurotransmitter oscillations effectively synchronize thousands of SCN neurons, enabling entrainment to daily light-dark cycles.
Area of Science:
- Neuroscience
- Computational Biology
- Chronobiology
Background:
- The mammalian circadian pacemaker resides in the suprachiasmatic nucleus (SCN).
- Individual SCN neurons possess molecular clocks but exhibit tissue-level synchrony.
- Intercellular coupling via neurotransmitters is hypothesized to drive SCN synchrony.
Purpose of the Study:
- To develop a dynamical model of coupled circadian oscillators in the SCN.
- To investigate the role of global neurotransmitter concentration in synchronizing SCN neuronal populations.
- To explore how synchronized SCN networks respond to light-dark cycles and inter-regional coupling.
Main Methods:
- A three-variable model representing the core negative feedback loop of a cellular circadian oscillator.
- Incorporation of a global coupling mechanism based on neurotransmitter concentration.
- Simulations of a large population (10,000 cells) and interactions between two SCN sub-populations.
Main Results:
- Global coupling efficiently synchronizes a large population of circadian oscillators.
- Synchronized SCN cell populations can be entrained by a 24-hour light-dark cycle.
- Simulations predict phase-leading in driven SCN populations and provide testable predictions regarding intrinsic periods and neurotransmitter dampening.
Conclusions:
- A global neurotransmitter-based coupling mechanism can robustly synchronize individual SCN circadian oscillators.
- This synchronization facilitates entrainment to external environmental cues like the light-dark cycle.
- The model offers experimentally verifiable predictions about SCN cellular and network dynamics.