On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore

ABSTRACT For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater...

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Autores principales:	Benoit Delahaye, Damien Eveillard, Nicholas Bouskill
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2017
Materias:	modeling simulation uncertainty Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/7c67fdfbc1024f71a9a2fc0e34643386
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id	oai:doaj.org-article:7c67fdfbc1024f71a9a2fc0e34643386
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spelling	oai:doaj.org-article:7c67fdfbc1024f71a9a2fc0e346433862021-12-02T18:15:43ZOn the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore10.1128/mSystems.00169-172379-5077https://doaj.org/article/7c67fdfbc1024f71a9a2fc0e346433862017-12-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00169-17https://doaj.org/toc/2379-5077ABSTRACT For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater experimental variability than expected and emphasized uncertainties not as a weakness but as a necessary feature of complex microbial systems. We therefore advocate that biological uncertainties need to be considered foundational facets that must be incorporated in models. Not only will understanding these uncertainties improve our understanding and identification of microbial traits, it will also provide fundamental insights on microbial systems as a whole. Taking into account uncertainties within microbial models calls for new validation techniques. Formal verification already overcomes this shortcoming by proposing modeling frameworks and validation techniques dedicated to probabilistic models. However, further work remains to extract the full potential of such techniques in the context of microbial models. Herein, we demonstrate how statistical model checking can enhance the development of microbial models by building confidence in the estimation of critical parameters and through improved sensitivity analyses.Benoit DelahayeDamien EveillardNicholas BouskillAmerican Society for MicrobiologyarticlemodelingsimulationuncertaintyMicrobiologyQR1-502ENmSystems, Vol 2, Iss 6 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	modeling simulation uncertainty Microbiology QR1-502
spellingShingle	modeling simulation uncertainty Microbiology QR1-502 Benoit Delahaye Damien Eveillard Nicholas Bouskill On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
description	ABSTRACT For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater experimental variability than expected and emphasized uncertainties not as a weakness but as a necessary feature of complex microbial systems. We therefore advocate that biological uncertainties need to be considered foundational facets that must be incorporated in models. Not only will understanding these uncertainties improve our understanding and identification of microbial traits, it will also provide fundamental insights on microbial systems as a whole. Taking into account uncertainties within microbial models calls for new validation techniques. Formal verification already overcomes this shortcoming by proposing modeling frameworks and validation techniques dedicated to probabilistic models. However, further work remains to extract the full potential of such techniques in the context of microbial models. Herein, we demonstrate how statistical model checking can enhance the development of microbial models by building confidence in the estimation of critical parameters and through improved sensitivity analyses.
format	article
author	Benoit Delahaye Damien Eveillard Nicholas Bouskill
author_facet	Benoit Delahaye Damien Eveillard Nicholas Bouskill
author_sort	Benoit Delahaye
title	On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
title_short	On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
title_full	On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
title_fullStr	On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
title_full_unstemmed	On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
title_sort	on the power of uncertainties in microbial system modeling: no need to hide them anymore
publisher	American Society for Microbiology
publishDate	2017
url	https://doaj.org/article/7c67fdfbc1024f71a9a2fc0e34643386
work_keys_str_mv	AT benoitdelahaye onthepowerofuncertaintiesinmicrobialsystemmodelingnoneedtohidethemanymore AT damieneveillard onthepowerofuncertaintiesinmicrobialsystemmodelingnoneedtohidethemanymore AT nicholasbouskill onthepowerofuncertaintiesinmicrobialsystemmodelingnoneedtohidethemanymore
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On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore

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