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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References
Angiolini, J., Plachta, N., Mocskos, E., Levi, V.: Exploring the dynamics of cell processes through simulations of fluorescence microscopy experiments. Biophys. J. 108, 2613–2618 (2015)
Bartol, T., Land, B., Salpeter, E., Salpeter, M.: Monte carlo simulation of miniature endplate current generation in the vertebrate neuromuscular junction. Biophys. J. 59(6), 1290–1307 (1991)
Buyya, R., Broberg, J., Goscinski, A.: Cloud Computing: Principles and Paradigms. Wiley, New York (2011)
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. In: Hernández, G., Barrios Hernández, C.J., Díaz, G., García Garino, C., Nesmachnow, S., Pérez-Acle, T., Storti, M., Vázquez, M. (eds.) CARLA 2014. CCIS, vol. 485, pp. 113–127. Springer, Heidelberg (2014)
Elson, E.L.: Fluorescence correlation spectroscopy: past, present, future. Biophys. J. 101(12), 2855–2870 (2011)
García, S., Iturriaga, S., Nesmachnow, S.: Scientific computing in the Latin America-Europe GISELA grid infrastructure. In: Proceedings of the 4th High Performance Computing Latin America Symposium, pp. 48–62 (2011)
Jakovits, P., Srirama, S.: Adapting scientific applications to cloud by using distributed computing frameworks. In: IEEE International Symposium on Cluster Computing and the Grid, pp. 164–167 (2013)
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. SIAM J. Sci. Comput. 30(6), 3126–3149 (2008)
Li, H.: Introducing Windows Azure. Apress, Berkely (2009)
Richman, R., Zirnhelt, H., Fix, S.: Large-scale building simulation using cloud computing for estimating lifecycle energy consumption. Can. J. Civ. Eng. 41, 252–262 (2014)
Stiles, J.R., Bartol, T.M.: Monte Carlo methods for simulating realistic synaptic microphysiology using MCell, Chap. 4, pp. 87–127. CRC Press (2001)
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. Proc. Natl. Acad. Sci. USA 93(12), 5747–5752 (1996)
Velte, T., Velte, A., Elsenpeter, R.: Cloud Computing, A Practical Approach. McGraw-Hill Education, New York (2009)
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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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