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Area of Science:

  • Computational Physics
  • Chemical Kinetics
  • Systems Biology

Background:

  • High-dimensional stochastic systems exhibit complex dynamics.
  • Quantifying the energy landscape is crucial for understanding system behavior.
  • Existing methods may not fully capture the nuances of oscillatory systems.

Purpose of the Study:

  • To present a novel numerical framework for quantifying the energy landscape of high-dimensional stochastic oscillatory systems.
  • To provide a detailed protocol for applying the diffusion decomposition of Gaussian approximation (DDGA) method.
  • To enable researchers to analyze and understand complex oscillatory dynamics.

Main Methods:

  • Development of a numerical framework based on diffusion decomposition of Gaussian approximation (DDGA).
  • Step-by-step procedures for code download and system setup.
  • Calculation of one-dimensional pre-solution and covariance matrices.
  • Quantification of the global energy landscape using DDGA.

Main Results:

  • Successful implementation of the DDGA framework for energy landscape quantification.
  • Detailed protocol facilitates reproducible analysis of stochastic oscillatory systems.
  • The framework allows for a comprehensive understanding of system dynamics.

Conclusions:

  • The presented numerical framework offers a robust method for analyzing high-dimensional stochastic oscillatory systems.
  • DDGA provides a powerful tool for characterizing energy landscapes in complex systems.
  • This work facilitates further research into the dynamics and behavior of such systems.