Cloud Computing for Fluorescence Correlation Spectroscopy Simulations

  • Lucía Marroig
  • Camila Riverón
  • Sergio Nesmachnow
  • Esteban Mocskos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 565)


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.


Scientific computing Cloud Fluorescence analysis 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Buyya, R., Broberg, J., Goscinski, A.: Cloud Computing: Principles and Paradigms. Wiley, New York (2011)CrossRefGoogle Scholar
  4. 4.
    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) Google Scholar
  5. 5.
    Elson, E.L.: Fluorescence correlation spectroscopy: past, present, future. Biophys. J. 101(12), 2855–2870 (2011)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Li, H.: Introducing Windows Azure. Apress, Berkely (2009) CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Stiles, J.R., Bartol, T.M.: Monte Carlo methods for simulating realistic synaptic microphysiology using MCell, Chap. 4, pp. 87–127. CRC Press (2001)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Velte, T., Velte, A., Elsenpeter, R.: Cloud Computing, A Practical Approach. McGraw-Hill Education, New York (2009) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lucía Marroig
    • 1
  • Camila Riverón
    • 1
  • Sergio Nesmachnow
    • 1
  • Esteban Mocskos
    • 2
    • 3
  1. 1.Universidad de la RepúblicaMontevideoUruguay
  2. 2.Departamento de Computación, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresBuenos AiresArgentina
  3. 3.Centro de Simulación Computacional p/Aplic. Tecnológicas/CSC-CONICETBuenos AiresArgentina

Personalised recommendations