Model selection in systems biology depends on experimental design.

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this appr...

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Autores principales:	Daniel Silk, Paul D W Kirk, Chris P Barnes, Tina Toni, Michael P H Stumpf
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/bb5622cefb92482b910eaff474022ea3
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spelling	oai:doaj.org-article:bb5622cefb92482b910eaff474022ea32021-11-11T05:52:06ZModel selection in systems biology depends on experimental design.1553-734X1553-735810.1371/journal.pcbi.1003650https://doaj.org/article/bb5622cefb92482b910eaff474022ea32014-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24922483/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.Daniel SilkPaul D W KirkChris P BarnesTina ToniMichael P H StumpfPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 6, p e1003650 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daniel Silk Paul D W Kirk Chris P Barnes Tina Toni Michael P H Stumpf Model selection in systems biology depends on experimental design.
description	Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.
format	article
author	Daniel Silk Paul D W Kirk Chris P Barnes Tina Toni Michael P H Stumpf
author_facet	Daniel Silk Paul D W Kirk Chris P Barnes Tina Toni Michael P H Stumpf
author_sort	Daniel Silk
title	Model selection in systems biology depends on experimental design.
title_short	Model selection in systems biology depends on experimental design.
title_full	Model selection in systems biology depends on experimental design.
title_fullStr	Model selection in systems biology depends on experimental design.
title_full_unstemmed	Model selection in systems biology depends on experimental design.
title_sort	model selection in systems biology depends on experimental design.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/bb5622cefb92482b910eaff474022ea3
work_keys_str_mv	AT danielsilk modelselectioninsystemsbiologydependsonexperimentaldesign AT pauldwkirk modelselectioninsystemsbiologydependsonexperimentaldesign AT chrispbarnes modelselectioninsystemsbiologydependsonexperimentaldesign AT tinatoni modelselectioninsystemsbiologydependsonexperimentaldesign AT michaelphstumpf modelselectioninsystemsbiologydependsonexperimentaldesign
_version_	1718439533058457600

Model selection in systems biology depends on experimental design.

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