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Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics
BACKGROUND: Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have disco...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374650/ https://www.ncbi.nlm.nih.gov/pubmed/28361679 http://dx.doi.org/10.1186/s12918-017-0401-9 |
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author | Chen, Minghan Li, Fei Wang, Shuo Cao, Young |
author_facet | Chen, Minghan Li, Fei Wang, Shuo Cao, Young |
author_sort | Chen, Minghan |
collection | PubMed |
description | BACKGROUND: Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. RESULTS: In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. CONCLUSION: Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system. |
format | Online Article Text |
id | pubmed-5374650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53746502017-04-03 Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics Chen, Minghan Li, Fei Wang, Shuo Cao, Young BMC Syst Biol Research BACKGROUND: Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. RESULTS: In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. CONCLUSION: Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system. BioMed Central 2017-03-14 /pmc/articles/PMC5374650/ /pubmed/28361679 http://dx.doi.org/10.1186/s12918-017-0401-9 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 | Research Chen, Minghan Li, Fei Wang, Shuo Cao, Young Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title | Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title_full | Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title_fullStr | Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title_full_unstemmed | Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title_short | Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics |
title_sort | stochastic modeling and simulation of reaction-diffusion system with hill function dynamics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374650/ https://www.ncbi.nlm.nih.gov/pubmed/28361679 http://dx.doi.org/10.1186/s12918-017-0401-9 |
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