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Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
Kaier Wang, Moira L Steyn-Ross, D Alistair Steyn-Ross1
1School of Engineering, The University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand. asr@waikato.ac.nz.
BMC Systems Biology
|April 15, 2014
Summary
Simulink
Area of Science:
- Computational neuroscience
- Nonlinear dynamics
- Reaction-diffusion systems
Background:
- Numerical solutions for reaction-diffusion systems traditionally require complex coding.
- Graphical programming interfaces offer a modern, visual alternative.
- Simulink provides a data-flow diagram approach for solving differential equations.
Purpose of the Study:
- To demonstrate the application of Simulink for solving nonlinear differential equations.
- To compare Simulink's performance against traditional coding methods (Matlab).
- To illustrate spatial dynamics in reaction-diffusion systems using Simulink.
Main Methods:
- Applied Simulink's data-flow technique to the van der Pol oscillator.
- Solved partial differential equations for the Brusselator chemical reactor and a cortical neuron model in 1D and 2D.
- Compared computational performance of Matlab and Simulink implementations.
Main Results:
- Simulink successfully simulated pattern dynamics in reaction-diffusion systems.
- Simulink outputs closely matched theoretical predictions.
- The van der Pol oscillator and reaction-diffusion systems were effectively modeled.
Conclusions:
- Simulink simplifies the implementation of solutions for reaction-diffusion systems.
- The graphical block-diagram approach reduces programming time and complexity.
- Simulink is accessible to users with limited high-level programming experience.
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