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Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study
BACKGROUND: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, use...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873313/ https://www.ncbi.nlm.nih.gov/pubmed/20346112 http://dx.doi.org/10.1186/1752-0509-4-34 |
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author | Twycross, Jamie Band, Leah R Bennett, Malcolm J King, John R Krasnogor, Natalio |
author_facet | Twycross, Jamie Band, Leah R Bennett, Malcolm J King, John R Krasnogor, Natalio |
author_sort | Twycross, Jamie |
collection | PubMed |
description | BACKGROUND: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. RESULTS: In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. CONCLUSIONS: Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models. |
format | Text |
id | pubmed-2873313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28733132010-05-20 Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study Twycross, Jamie Band, Leah R Bennett, Malcolm J King, John R Krasnogor, Natalio BMC Syst Biol Research article BACKGROUND: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. RESULTS: In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. CONCLUSIONS: Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models. BioMed Central 2010-03-26 /pmc/articles/PMC2873313/ /pubmed/20346112 http://dx.doi.org/10.1186/1752-0509-4-34 Text en Copyright ©2010 Twycross et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research article Twycross, Jamie Band, Leah R Bennett, Malcolm J King, John R Krasnogor, Natalio Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title | Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title_full | Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title_fullStr | Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title_full_unstemmed | Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title_short | Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
title_sort | stochastic and deterministic multiscale models for systems biology: an auxin-transport case study |
topic | Research article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873313/ https://www.ncbi.nlm.nih.gov/pubmed/20346112 http://dx.doi.org/10.1186/1752-0509-4-34 |
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