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Updated: May 8, 2026
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Published on: September 27, 2018
Synchronization of stochastic oscillators in biochemical systems
Joseph D Challenger1, Alan J McKane
1Theoretical Physics Division, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, United Kingdom. joseph.challenger@postgrad.manchester.ac.uk
Summary
This study explores how biochemical oscillations synchronize using a novel analytical method. Results show how cell variations impact synchronization, validated by numerical simulations.
Area of Science:
- Biochemistry
- Systems Biology
- Theoretical Biology
Background:
- Stochastic oscillations are crucial in biological systems.
- Understanding synchronization mechanisms is key to deciphering cellular behavior.
- Previous models often lack analytical approaches for complex systems.
Purpose of the Study:
- To develop and apply an analytical method for investigating stochastic oscillation synchronization in biochemical models.
- To analyze the impact of cellular parameter variations on synchronization.
- To quantify phase lag in synchronized oscillations.
Main Methods:
- Utilizing the complex coherence function within the linear noise approximation.
- Applying the analytical method to a simple biochemical model.
- Extending the analysis to model chemical concentration synchronization in social amoeba.
Main Results:
- The complex coherence function effectively quantifies synchronization in stochastic biochemical systems.
- Variations in rate constants and cell volumes significantly affect oscillation synchronization.
- Calculated phase lags provide insights into the temporal dynamics of synchronization.
- Analytical predictions closely matched numerical simulation outcomes.
Conclusions:
- The linear noise approximation and complex coherence function offer a powerful analytical tool for studying biochemical synchronization.
- Cellular heterogeneity plays a critical role in the emergent synchronization patterns observed in populations.
- The findings contribute to a deeper understanding of collective behaviors in biological systems, such as social amoeba.
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