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Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models
BACKGROUND: Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computatio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483253/ https://www.ncbi.nlm.nih.gov/pubmed/22818032 http://dx.doi.org/10.1186/1752-0509-6-88 |
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author | Tøndel, Kristin Indahl, Ulf G Gjuvsland, Arne B Omholt, Stig W Martens, Harald |
author_facet | Tøndel, Kristin Indahl, Ulf G Gjuvsland, Arne B Omholt, Stig W Martens, Harald |
author_sort | Tøndel, Kristin |
collection | PubMed |
description | BACKGROUND: Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation. RESULTS: By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported. CONCLUSIONS: Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods. |
format | Online Article Text |
id | pubmed-3483253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34832532012-11-05 Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models Tøndel, Kristin Indahl, Ulf G Gjuvsland, Arne B Omholt, Stig W Martens, Harald BMC Syst Biol Research Article BACKGROUND: Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation. RESULTS: By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported. CONCLUSIONS: Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods. BioMed Central 2012-07-20 /pmc/articles/PMC3483253/ /pubmed/22818032 http://dx.doi.org/10.1186/1752-0509-6-88 Text en Copyright ©2012 Tøndel 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 Tøndel, Kristin Indahl, Ulf G Gjuvsland, Arne B Omholt, Stig W Martens, Harald Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title | Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title_full | Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title_fullStr | Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title_full_unstemmed | Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title_short | Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
title_sort | multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483253/ https://www.ncbi.nlm.nih.gov/pubmed/22818032 http://dx.doi.org/10.1186/1752-0509-6-88 |
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