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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Publicado: 2015
Materias:
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18650929_v565_n_p34_Marroig
http://hdl.handle.net/20.500.12110/paper_18650929_v565_n_p34_Marroig
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spelling paper:paper_18650929_v565_n_p34_Marroig2023-06-08T16:29:38Z Cloud computing for fluorescence correlation spectroscopy simulations 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. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18650929_v565_n_p34_Marroig 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
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.
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
publishDate 2015
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18650929_v565_n_p34_Marroig
http://hdl.handle.net/20.500.12110/paper_18650929_v565_n_p34_Marroig
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