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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Autor principal: Marroig, L.
Otros Autores: Riverón, C., Nesmachnow, S., Mocskos, E., Navaux P.O.A, Osthoff C., Dias P.L.S, Hernandez C.J.B
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: Springer Verlag 2015
Acceso en línea:Registro en Scopus
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100 1 |a Marroig, L. 
245 1 0 |a Cloud computing for fluorescence correlation spectroscopy simulations 
260 |b Springer Verlag  |c 2015 
270 1 0 |m Mocskos, E.; Departamento de Computación, Universidad de Buenos AiresArgentina; email: emocskos@dc.uba.ar 
506 |2 openaire  |e Política editorial 
504 |a Angiolini, J., Plachta, N., Mocskos, E., Levi, V., Exploring the dynamics of cell processes through simulations of fluorescence microscopy experiments (2015) Biophys. J, 108, pp. 2613-2618 
504 |a Bartol, T., Land, B., Salpeter, E., Salpeter, M., Monte carlo simulation of miniature endplate current generation in the vertebrate neuromuscular junction (1991) Biophys. J, 59 (6), pp. 1290-1307 
504 |a Buyya, R., Broberg, J., Goscinski, A., (2011) Cloud Computing: Principles and Paradigms, , Wiley, New York 
504 |a Da Silva, M., Nesmachnow, S., Geier, M., Mocskos, E., Angiolini, J., Levi, V., Cristobal, A., Efficient fluorescence microscopy analysis over a volunteer grid/cloud infrastructure (2014) CARLA 2014. CCIS, 485, pp. 113-127. , Hernández, G., Barrios Hernández, C.J., Díaz, G., García Garino, C., Nesmachnow, S., Pérez-Acle, T., Storti, M., Vázquez, M. (eds.), Springer, Heidelberg 
504 |a Elson, E.L., Fluorescence correlation spectroscopy: Past, present, future (2011) Biophys. J, 101 (12), pp. 2855-2870 
504 |a García, S., Iturriaga, S., Nesmachnow, S., Scientific computing in the Latin America-Europe GISELA grid infrastructure (2011) Proceedings of the 4th High Performance Computing Latin America Symposium, pp. 48-62 
504 |a Jakovits, P., Srirama, S., Adapting scientific applications to cloud by using distributed computing frameworks (2013) IEEE International Symposium on Cluster Computing and the Grid, pp. 164-167 
504 |a Kerr, R., Bartol, T., Kaminsky, B., Dittrich, M., Chang, J., Baden, S., Sejnowski, T., Stiles, J., Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces (2008) SIAM J. Sci. Comput, 30 (6), pp. 3126-3149 
504 |a Li, H., (2009) Introducing Windows Azure, , Apress, Berkely 
504 |a Richman, R., Zirnhelt, H., Fix, S., Large-scale building simulation using cloud computing for estimating lifecycle energy consumption (2014) Can. J. Civ. Eng, 41, pp. 252-262 
504 |a Stiles, J.R., Bartol, T.M., (2001) Monte Carlo methods for simulating realistic synaptic microphysiology using MCell, Chap 4, pp. 87-127. , CRC Press 
504 |a Stiles, J.R., Van Helden, D., Bartol, T.M., Salpeter, E.E., Salpeter, M.M., Miniature endplate current rise times less than 100 microseconds from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle (1996) Proc. Natl. Acad. Sci. USA, 93 (12), pp. 5747-5752 
504 |a Velte, T., Velte, A., Elsenpeter, R., (2009) Cloud Computing, A Practical Approach, , McGraw-Hill Education, New YorkA4 - 
520 3 |a 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.  |l eng 
593 |a Universidad de la República, Montevideo, Uruguay 
593 |a Departamento de Computación, Universidad de Buenos Aires, Buenos Aires, Argentina 
593 |a Centro de Simulación Computacional p/Aplic. Tecnológicas/CSC-CONICET, Godoy Cruz 2390, Buenos Aires, C1425FQD, Argentina 
690 1 0 |a CLOUD 
690 1 0 |a FLUORESCENCE ANALYSIS 
690 1 0 |a SCIENTIFIC COMPUTING 
690 1 0 |a CLOUDS 
690 1 0 |a FLUORESCENCE 
690 1 0 |a FLUORESCENCE MICROSCOPY 
690 1 0 |a FLUORESCENCE SPECTROSCOPY 
690 1 0 |a NATURAL SCIENCES COMPUTING 
690 1 0 |a SPECTROSCOPIC ANALYSIS 
690 1 0 |a STOCHASTIC MODELS 
690 1 0 |a STOCHASTIC SYSTEMS 
690 1 0 |a WINDOWS OPERATING SYSTEM 
690 1 0 |a CLOUD INFRASTRUCTURES 
690 1 0 |a DESIGN AND IMPLEMENTATIONS 
690 1 0 |a EXPERIMENTAL ANALYSIS 
690 1 0 |a FLUORESCENCE ANALYSIS 
690 1 0 |a FLUORESCENCE CORRELATION SPECTROSCOPY 
690 1 0 |a PARALLEL EXECUTIONS 
690 1 0 |a SCALABLE ARCHITECTURES 
690 1 0 |a STOCHASTIC SIMULATIONS 
690 1 0 |a DISTRIBUTED COMPUTER SYSTEMS 
700 1 |a Riverón, C. 
700 1 |a Nesmachnow, S. 
700 1 |a Mocskos, E. 
700 1 |a Navaux P.O.A. 
700 1 |a Osthoff C. 
700 1 |a Dias P.L.S. 
700 1 |a Hernandez C.J.B. 
711 2 |d 26 August 2015 through 28 August 2015  |g Código de la conferencia: 159279 
773 0 |d Springer Verlag, 2015  |g v. 565  |h pp. 34-49  |p Commun. Comput. Info. Sci.  |n Communications in Computer and Information Science  |x 18650929  |z 9783319269276  |t 2nd Latin American Conference on High Performance Computing, CARLA 2015 
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