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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2015
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| 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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paper:paper_18650929_v565_n_p34_Marroig |
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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 |
| _version_ |
1768545484158271488 |