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Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK

BACKGROUND: Investigation of the nonlinear pattern dynamics of a reaction-diffusion system almost always requires numerical solution of the system’s set of defining differential equations. Traditionally, this would be done by selecting an appropriate differential equation solver from a library of su...

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Autores principales: Wang, Kaier, Steyn-Ross, Moira L, Steyn-Ross, D Alistair, Wilson, Marcus T, Sleigh, Jamie W, Shiraishi, Yoichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4006638/
https://www.ncbi.nlm.nih.gov/pubmed/24725437
http://dx.doi.org/10.1186/1752-0509-8-45
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author Wang, Kaier
Steyn-Ross, Moira L
Steyn-Ross, D Alistair
Wilson, Marcus T
Sleigh, Jamie W
Shiraishi, Yoichi
author_facet Wang, Kaier
Steyn-Ross, Moira L
Steyn-Ross, D Alistair
Wilson, Marcus T
Sleigh, Jamie W
Shiraishi, Yoichi
author_sort Wang, Kaier
collection PubMed
description BACKGROUND: Investigation of the nonlinear pattern dynamics of a reaction-diffusion system almost always requires numerical solution of the system’s set of defining differential equations. Traditionally, this would be done by selecting an appropriate differential equation solver from a library of such solvers, then writing computer codes (in a programming language such as C or Matlab) to access the selected solver and display the integrated results as a function of space and time. This “code-based” approach is flexible and powerful, but requires a certain level of programming sophistication. A modern alternative is to use a graphical programming interface such as Simulink to construct a data-flow diagram by assembling and linking appropriate code blocks drawn from a library. The result is a visual representation of the inter-relationships between the state variables whose output can be made completely equivalent to the code-based solution. RESULTS: As a tutorial introduction, we first demonstrate application of the Simulink data-flow technique to the classical van der Pol nonlinear oscillator, and compare Matlab and Simulink coding approaches to solving the van der Pol ordinary differential equations. We then show how to introduce space (in one and two dimensions) by solving numerically the partial differential equations for two different reaction-diffusion systems: the well-known Brusselator chemical reactor, and a continuum model for a two-dimensional sheet of human cortex whose neurons are linked by both chemical and electrical (diffusive) synapses. We compare the relative performances of the Matlab and Simulink implementations. CONCLUSIONS: The pattern simulations by Simulink are in good agreement with theoretical predictions. Compared with traditional coding approaches, the Simulink block-diagram paradigm reduces the time and programming burden required to implement a solution for reaction-diffusion systems of equations. Construction of the block-diagram does not require high-level programming skills, and the graphical interface lends itself to easy modification and use by non-experts.
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spelling pubmed-40066382014-05-19 Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK Wang, Kaier Steyn-Ross, Moira L Steyn-Ross, D Alistair Wilson, Marcus T Sleigh, Jamie W Shiraishi, Yoichi BMC Syst Biol Methodology Article BACKGROUND: Investigation of the nonlinear pattern dynamics of a reaction-diffusion system almost always requires numerical solution of the system’s set of defining differential equations. Traditionally, this would be done by selecting an appropriate differential equation solver from a library of such solvers, then writing computer codes (in a programming language such as C or Matlab) to access the selected solver and display the integrated results as a function of space and time. This “code-based” approach is flexible and powerful, but requires a certain level of programming sophistication. A modern alternative is to use a graphical programming interface such as Simulink to construct a data-flow diagram by assembling and linking appropriate code blocks drawn from a library. The result is a visual representation of the inter-relationships between the state variables whose output can be made completely equivalent to the code-based solution. RESULTS: As a tutorial introduction, we first demonstrate application of the Simulink data-flow technique to the classical van der Pol nonlinear oscillator, and compare Matlab and Simulink coding approaches to solving the van der Pol ordinary differential equations. We then show how to introduce space (in one and two dimensions) by solving numerically the partial differential equations for two different reaction-diffusion systems: the well-known Brusselator chemical reactor, and a continuum model for a two-dimensional sheet of human cortex whose neurons are linked by both chemical and electrical (diffusive) synapses. We compare the relative performances of the Matlab and Simulink implementations. CONCLUSIONS: The pattern simulations by Simulink are in good agreement with theoretical predictions. Compared with traditional coding approaches, the Simulink block-diagram paradigm reduces the time and programming burden required to implement a solution for reaction-diffusion systems of equations. Construction of the block-diagram does not require high-level programming skills, and the graphical interface lends itself to easy modification and use by non-experts. BioMed Central 2014-04-11 /pmc/articles/PMC4006638/ /pubmed/24725437 http://dx.doi.org/10.1186/1752-0509-8-45 Text en Copyright © 2014 Wang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Wang, Kaier
Steyn-Ross, Moira L
Steyn-Ross, D Alistair
Wilson, Marcus T
Sleigh, Jamie W
Shiraishi, Yoichi
Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title_full Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title_fullStr Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title_full_unstemmed Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title_short Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK
title_sort simulations of pattern dynamics for reaction-diffusion systems via simulink
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4006638/
https://www.ncbi.nlm.nih.gov/pubmed/24725437
http://dx.doi.org/10.1186/1752-0509-8-45
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