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STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python

We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process...

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Detalles Bibliográficos
Autores principales: Wils, Stefan, Schutter, Erik De
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706651/
https://www.ncbi.nlm.nih.gov/pubmed/19623245
http://dx.doi.org/10.3389/neuro.11.015.2009
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author Wils, Stefan
Schutter, Erik De
author_facet Wils, Stefan
Schutter, Erik De
author_sort Wils, Stefan
collection PubMed
description We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code.
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spelling pubmed-27066512009-07-21 STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python Wils, Stefan Schutter, Erik De Front Neuroinformatics Neuroscience We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code. Frontiers Research Foundation 2009-06-29 /pmc/articles/PMC2706651/ /pubmed/19623245 http://dx.doi.org/10.3389/neuro.11.015.2009 Text en Copyright © 2009 Wils and De Schutter. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Wils, Stefan
Schutter, Erik De
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title_full STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title_fullStr STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title_full_unstemmed STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title_short STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python
title_sort steps: modeling and simulating complex reaction-diffusion systems with python
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706651/
https://www.ncbi.nlm.nih.gov/pubmed/19623245
http://dx.doi.org/10.3389/neuro.11.015.2009
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