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Dynamical quorum-sensing in oscillators coupled through an external medium.

David J Schwab1, Ania Baetica, Pankaj Mehta

  • 1Dept. of Molecular Biology and Lewis-Sigler Institute, Princeton University, Princeton, NJ 08854.

Physica D. Nonlinear Phenomena
|October 23, 2012
PubMed
Summary
This summary is machine-generated.

This study models dynamical quorum sensing in coupled oscillators. It reveals four distinct mechanisms causing loss of synchronized oscillations, including a novel time-delay effect.

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Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Theoretical Biology

Background:

  • Many biological and physical systems synchronize oscillations based on population density.
  • This phenomenon, known as dynamical quorum sensing, often involves chemical signaling through a medium.
  • Understanding the mechanisms driving synchronization and its loss is crucial for various scientific fields.

Purpose of the Study:

  • To investigate a theoretical model of dynamical quorum sensing in a heterogeneous population of coupled oscillators.
  • To identify and characterize the mechanisms leading to a loss of population-level synchronized oscillations.
  • To develop analytic tools for predicting phase boundaries in such systems.

Main Methods:

  • Development of a theoretical model for diffusively coupled limit-cycle oscillators.
  • Analysis of the model's phase diagram to identify distinct synchronization behaviors.
  • Derivation of analytic equations to determine phase boundaries based on population density.

Main Results:

  • The model exhibits a rich phase diagram with four distinct mechanisms causing loss of synchronized oscillations.
  • A novel mechanism involving effective time-delays introduced by the external medium was identified.
  • Analytic equations were derived to predict phase boundaries as a function of population density.

Conclusions:

  • The theoretical model successfully captures key features of dynamical quorum sensing.
  • The findings provide insights into synchronization transitions in systems like BZ catalytic particles and engineered bacteria.
  • The derived analytic framework facilitates the prediction and understanding of collective oscillation dynamics.