Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells

Autores
Delmont, Ignacio; Buena Maizon, Héctor; Mosqueira, Alejo; Barrantes, Francisco José
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Delmont, Ignacio. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Delmont, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Buena Maizon, Héctor. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Buena Maizon, Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mosqueira, Alejo. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Mosqueira, Alejo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Abstract: Storm (stochastical optical reconstruction microscopy), a form of single-molecule nanoscopy, calls for a variety of statistical and mathematical operations to reconstruct the original objects from their noisy wide-field point spread functions [1]. We are interested in understanding the dynamics of the nicotinic acetylcholine receptor (nAChR) protein, a cell-surface neurotransmitter receptor. Analyzing the translational motion of nAChR molecules by single-particle tracking in living cells is a complex task. In order to understand how nAChR molecules associate/dissociate into/from nanometer-sized clusters over time, and to characterize their trajectories according to different mathematical models, we are developing analytical procedures based on artificial intelligence. Due to their speed of calculation and accuracy, deep learning models are clearly an improvement on classical models in biological image analysis and biomedical science.
Fuente
Microscopy and Microanalysis. 2020, 26 (sup. 1)
Materia
INTELIGENCIA ARTIFICIAL
PROTEINAS
NEUROTRANSMISORES
NANOSCOPIA
BIOMEDICINA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/14612
Acceder
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oai_identifier_str oai:ucacris:123456789/14612
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cellsDelmont, IgnacioBuena Maizon, HéctorMosqueira, AlejoBarrantes, Francisco JoséINTELIGENCIA ARTIFICIALPROTEINASNEUROTRANSMISORESNANOSCOPIABIOMEDICINAFil: Delmont, Ignacio. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; ArgentinaFil: Delmont, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Buena Maizon, Héctor. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; ArgentinaFil: Buena Maizon, Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mosqueira, Alejo. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; ArgentinaFil: Mosqueira, Alejo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; ArgentinaFil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAbstract: Storm (stochastical optical reconstruction microscopy), a form of single-molecule nanoscopy, calls for a variety of statistical and mathematical operations to reconstruct the original objects from their noisy wide-field point spread functions [1]. We are interested in understanding the dynamics of the nicotinic acetylcholine receptor (nAChR) protein, a cell-surface neurotransmitter receptor. Analyzing the translational motion of nAChR molecules by single-particle tracking in living cells is a complex task. In order to understand how nAChR molecules associate/dissociate into/from nanometer-sized clusters over time, and to characterize their trajectories according to different mathematical models, we are developing analytical procedures based on artificial intelligence. Due to their speed of calculation and accuracy, deep learning models are clearly an improvement on classical models in biological image analysis and biomedical science.Cambridge University Press2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/146121431-927610.1017/S143192762000032XDelmont, I. et al. Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells [en línea]. Microscopy and Microanalysis. 2020, 26 (sup. 1). doi: 10.1017/S143192762000032X. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14612Microscopy and Microanalysis. 2020, 26 (sup. 1)reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica Argentinaenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2025-07-03T10:58:43Zoai:ucacris:123456789/14612instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852025-07-03 10:58:43.817Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
title Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
spellingShingle Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
Delmont, Ignacio
INTELIGENCIA ARTIFICIAL
PROTEINAS
NEUROTRANSMISORES
NANOSCOPIA
BIOMEDICINA
title_short Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
title_full Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
title_fullStr Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
title_full_unstemmed Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
title_sort Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
dc.creator.none.fl_str_mv Delmont, Ignacio
Buena Maizon, Héctor
Mosqueira, Alejo
Barrantes, Francisco José
author Delmont, Ignacio
author_facet Delmont, Ignacio
Buena Maizon, Héctor
Mosqueira, Alejo
Barrantes, Francisco José
author_role author
author2 Buena Maizon, Héctor
Mosqueira, Alejo
Barrantes, Francisco José
author2_role author
author
author
dc.subject.none.fl_str_mv INTELIGENCIA ARTIFICIAL
PROTEINAS
NEUROTRANSMISORES
NANOSCOPIA
BIOMEDICINA
topic INTELIGENCIA ARTIFICIAL
PROTEINAS
NEUROTRANSMISORES
NANOSCOPIA
BIOMEDICINA
dc.description.none.fl_txt_mv Fil: Delmont, Ignacio. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Delmont, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Buena Maizon, Héctor. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Buena Maizon, Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mosqueira, Alejo. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Mosqueira, Alejo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
Fil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Abstract: Storm (stochastical optical reconstruction microscopy), a form of single-molecule nanoscopy, calls for a variety of statistical and mathematical operations to reconstruct the original objects from their noisy wide-field point spread functions [1]. We are interested in understanding the dynamics of the nicotinic acetylcholine receptor (nAChR) protein, a cell-surface neurotransmitter receptor. Analyzing the translational motion of nAChR molecules by single-particle tracking in living cells is a complex task. In order to understand how nAChR molecules associate/dissociate into/from nanometer-sized clusters over time, and to characterize their trajectories according to different mathematical models, we are developing analytical procedures based on artificial intelligence. Due to their speed of calculation and accuracy, deep learning models are clearly an improvement on classical models in biological image analysis and biomedical science.
description Fil: Delmont, Ignacio. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Biología Celular y Molecular; Argentina
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/14612
1431-9276
10.1017/S143192762000032X
Delmont, I. et al. Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells [en línea]. Microscopy and Microanalysis. 2020, 26 (sup. 1). doi: 10.1017/S143192762000032X. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14612
url https://repositorio.uca.edu.ar/handle/123456789/14612
identifier_str_mv 1431-9276
10.1017/S143192762000032X
Delmont, I. et al. Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells [en línea]. Microscopy and Microanalysis. 2020, 26 (sup. 1). doi: 10.1017/S143192762000032X. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14612
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
dc.source.none.fl_str_mv Microscopy and Microanalysis. 2020, 26 (sup. 1)
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 13.070432

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