Abstract
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.
This project has been partially supported by the Microsoft Azure for Research Award.
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Marroig, L., Riverón, C., Nesmachnow, S., Mocskos, E. (2015). Cloud Computing for Fluorescence Correlation Spectroscopy Simulations. In: Osthoff, C., Navaux, P., Barrios Hernandez, C., Silva Dias, P. (eds) High Performance Computing. CARLA 2015. Communications in Computer and Information Science, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-26928-3_3
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DOI: https://doi.org/10.1007/978-3-319-26928-3_3
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