Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.

<h4>Background</h4>A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitabi...

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Autores principales:	Treenut Saithong, Kevin J Painter, Andrew J Millar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d185fb74ac1f41f589415ac584e945e6
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spelling	oai:doaj.org-article:d185fb74ac1f41f589415ac584e945e62021-11-18T07:01:37ZConsistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.1932-620310.1371/journal.pone.0015589https://doaj.org/article/d185fb74ac1f41f589415ac584e945e62010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21179566/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain.<h4>Results</h4>Here, we propose a novel robustness analysis that aims to determine the "common robustness" of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network.<h4>Conclusions</h4>Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.Treenut SaithongKevin J PainterAndrew J MillarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 12, p e15589 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Treenut Saithong Kevin J Painter Andrew J Millar Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
description	<h4>Background</h4>A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain.<h4>Results</h4>Here, we propose a novel robustness analysis that aims to determine the "common robustness" of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network.<h4>Conclusions</h4>Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.
format	article
author	Treenut Saithong Kevin J Painter Andrew J Millar
author_facet	Treenut Saithong Kevin J Painter Andrew J Millar
author_sort	Treenut Saithong
title	Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
title_short	Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
title_full	Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
title_fullStr	Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
title_full_unstemmed	Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
title_sort	consistent robustness analysis (cra) identifies biologically relevant properties of regulatory network models.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/d185fb74ac1f41f589415ac584e945e6
work_keys_str_mv	AT treenutsaithong consistentrobustnessanalysiscraidentifiesbiologicallyrelevantpropertiesofregulatorynetworkmodels AT kevinjpainter consistentrobustnessanalysiscraidentifiesbiologicallyrelevantpropertiesofregulatorynetworkmodels AT andrewjmillar consistentrobustnessanalysiscraidentifiesbiologicallyrelevantpropertiesofregulatorynetworkmodels
_version_	1718424071808483328

Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.

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