Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

<h4>Background</h4>Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze...

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Autores principales:	Jorge Gago, Lourdes Martínez-Núñez, Mariana Landín, Jaume Flexas, Pedro P Gallego
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e5344b23ed0f4c3f93b6297610f6d918
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spelling	oai:doaj.org-article:e5344b23ed0f4c3f93b6297610f6d9182021-11-18T08:37:13ZModeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.1932-620310.1371/journal.pone.0085989https://doaj.org/article/e5344b23ed0f4c3f93b6297610f6d9182014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24465829/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.<h4>Methodology and principal findings</h4>In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).<h4>Conclusions</h4>Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.Jorge GagoLourdes Martínez-NúñezMariana LandínJaume FlexasPedro P GallegoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e85989 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jorge Gago Lourdes Martínez-Núñez Mariana Landín Jaume Flexas Pedro P Gallego Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
description	<h4>Background</h4>Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.<h4>Methodology and principal findings</h4>In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).<h4>Conclusions</h4>Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.
format	article
author	Jorge Gago Lourdes Martínez-Núñez Mariana Landín Jaume Flexas Pedro P Gallego
author_facet	Jorge Gago Lourdes Martínez-Núñez Mariana Landín Jaume Flexas Pedro P Gallego
author_sort	Jorge Gago
title	Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
title_short	Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
title_full	Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
title_fullStr	Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
title_full_unstemmed	Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
title_sort	modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/e5344b23ed0f4c3f93b6297610f6d918
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Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

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