Cloud computing for fluorescence correlation spectroscopy simulations

Fluorescence microscopy techniques and protein labeling set an inflection point in the way cells are studied. The fluorescence correlation spectroscopy is extremely useful for quantitatively measuring the movement of molecules in living cells. This article presents the design and implementation of a...

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Autores principales: Marroig, L., Riverón, C., Nesmachnow, S., Mocskos, E., Navaux P.O.A., Osthoff C., Dias P.L.S., Hernandez C.J.B.
Formato: SER
Materias:
Cloud
Fluorescence analysis
Scientific computing
Clouds
Fluorescence
Fluorescence microscopy
Fluorescence spectroscopy
Natural sciences computing
Spectroscopic analysis
Stochastic models
Stochastic systems
Windows operating system
Cloud infrastructures
Design and implementations
Experimental analysis
Fluorescence Correlation Spectroscopy
Parallel executions
Scalable architectures
Stochastic simulations
Distributed computer systems
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_18650929_v565_n_p34_Marroig
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_18650929_v565_n_p34_Marroig
record_format dspace
spelling todo:paper_18650929_v565_n_p34_Marroig2023-10-03T16:33:34Z Cloud computing for fluorescence correlation spectroscopy simulations Marroig, L. Riverón, C. Nesmachnow, S. Mocskos, E. Navaux P.O.A. Osthoff C. Dias P.L.S. Hernandez C.J.B. Cloud Fluorescence analysis Scientific computing Clouds Fluorescence Fluorescence microscopy Fluorescence spectroscopy Natural sciences computing Spectroscopic analysis Stochastic models Stochastic systems Windows operating system Cloud infrastructures Design and implementations Experimental analysis Fluorescence analysis Fluorescence Correlation Spectroscopy Parallel executions Scalable architectures Stochastic simulations Distributed computer systems Fluorescence microscopy techniques and protein labeling set an inflection point in the way cells are studied. The fluorescence correlation spectroscopy is extremely useful for quantitatively measuring the movement of molecules in living cells. This article presents the design and implementation of a system for fluorescence analysis through stochastic simulations using distributed computing techniques over a cloud infrastructure. A highly scalable architecture, accessible to many users, is proposed for studying complex cellular biological processes. A MapReduce algorithm that allows the parallel execution of multiple simulations is developed over a distributed Hadoop cluster using the Microsoft Azure cloud platform. The experimental analysis shows the correctness of the implementation developed and its utility as a tool for scientific computing in the cloud. © Springer International Publishing Switzerland 2015. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_18650929_v565_n_p34_Marroig
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Cloud
Fluorescence analysis
Scientific computing
Clouds
Fluorescence
Fluorescence microscopy
Fluorescence spectroscopy
Natural sciences computing
Spectroscopic analysis
Stochastic models
Stochastic systems
Windows operating system
Cloud infrastructures
Design and implementations
Experimental analysis
Fluorescence analysis
Fluorescence Correlation Spectroscopy
Parallel executions
Scalable architectures
Stochastic simulations
Distributed computer systems
spellingShingle Cloud
Fluorescence analysis
Scientific computing
Clouds
Fluorescence
Fluorescence microscopy
Fluorescence spectroscopy
Natural sciences computing
Spectroscopic analysis
Stochastic models
Stochastic systems
Windows operating system
Cloud infrastructures
Design and implementations
Experimental analysis
Fluorescence analysis
Fluorescence Correlation Spectroscopy
Parallel executions
Scalable architectures
Stochastic simulations
Distributed computer systems
Marroig, L.
Riverón, C.
Nesmachnow, S.
Mocskos, E.
Navaux P.O.A.
Osthoff C.
Dias P.L.S.
Hernandez C.J.B.
Cloud computing for fluorescence correlation spectroscopy simulations
topic_facet Cloud
Fluorescence analysis
Scientific computing
Clouds
Fluorescence
Fluorescence microscopy
Fluorescence spectroscopy
Natural sciences computing
Spectroscopic analysis
Stochastic models
Stochastic systems
Windows operating system
Cloud infrastructures
Design and implementations
Experimental analysis
Fluorescence analysis
Fluorescence Correlation Spectroscopy
Parallel executions
Scalable architectures
Stochastic simulations
Distributed computer systems
description Fluorescence microscopy techniques and protein labeling set an inflection point in the way cells are studied. The fluorescence correlation spectroscopy is extremely useful for quantitatively measuring the movement of molecules in living cells. This article presents the design and implementation of a system for fluorescence analysis through stochastic simulations using distributed computing techniques over a cloud infrastructure. A highly scalable architecture, accessible to many users, is proposed for studying complex cellular biological processes. A MapReduce algorithm that allows the parallel execution of multiple simulations is developed over a distributed Hadoop cluster using the Microsoft Azure cloud platform. The experimental analysis shows the correctness of the implementation developed and its utility as a tool for scientific computing in the cloud. © Springer International Publishing Switzerland 2015.
format SER
author Marroig, L.
Riverón, C.
Nesmachnow, S.
Mocskos, E.
Navaux P.O.A.
Osthoff C.
Dias P.L.S.
Hernandez C.J.B.
author_facet Marroig, L.
Riverón, C.
Nesmachnow, S.
Mocskos, E.
Navaux P.O.A.
Osthoff C.
Dias P.L.S.
Hernandez C.J.B.
author_sort Marroig, L.
title Cloud computing for fluorescence correlation spectroscopy simulations
title_short Cloud computing for fluorescence correlation spectroscopy simulations
title_full Cloud computing for fluorescence correlation spectroscopy simulations
title_fullStr Cloud computing for fluorescence correlation spectroscopy simulations
title_full_unstemmed Cloud computing for fluorescence correlation spectroscopy simulations
title_sort cloud computing for fluorescence correlation spectroscopy simulations
url http://hdl.handle.net/20.500.12110/paper_18650929_v565_n_p34_Marroig
work_keys_str_mv AT marroigl cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT riveronc cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT nesmachnows cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT mocskose cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT navauxpoa cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT osthoffc cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT diaspls cloudcomputingforfluorescencecorrelationspectroscopysimulations
AT hernandezcjb cloudcomputingforfluorescencecorrelationspectroscopysimulations
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