Advertisement

FM-Sim: A Hybrid Protocol Simulator of Fluorescence Microscopy Neuroscience Assays with Integrated Bayesian Inference

  • Donal StewartEmail author
  • Stephen Gilmore
  • Michael A. Cousin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7699)

Abstract

We present FM-Sim, a domain-specific simulator for defining and simulating fluorescence microscopy assays. Experimental protocols as performed in vitro may be defined in the simulator. The defined protocols then interact with a computational model of presynaptic behaviour in rodent central nervous system neurons, allowing simulation of fluorescent responses to varying stimuli. Rate parameters of the model may be obtained using Bayesian inference functions integrated into the simulator, given experimental fluorescence observations of the protocol performed in vitro as training data. These trained protocols allow for predictive in silico modelling of potential experimental outcomes prior to time-consuming and expensive in vitro studies.

Keywords

Synaptic Vesicle Nerve Terminal Extracellular Medium Marginal Likelihood Sequential Monte Carlo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Thanks to the members of the Cousin group, in particular Sarah Gordon, for helpful discussions, and provision of experimental data. Thanks also to the reviewers of this paper for their helpful comments and suggestions.

This work was supported in part by grants EP/F500385/1 and BB/F529254/1 for the University of Edinburgh School of Informatics Doctoral Training Centre in Neuroinformatics and Computational Neuroscience (www.anc.ac.uk/dtc) from the UK Engineering and Physical Sciences Research Council (EPSRC), UK Biotechnology and Biological Sciences Research Council (BBSRC), and the UK Medical Research Council (MRC). The work has made use of resources provided by the Edinburgh Compute and Data Facility (ECDF; www.ecdf.ed.ac.uk), which has support from the eDIKT initiative (www.edikt.org.uk).

Stephen Gilmore is supported by the BBSRC SysMIC grant, BB/I014713/1.

References

  1. 1.
    Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo methods. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 72(3), 269–342 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Aravanis, A., Pyle, J., Tsien, R.: Single synaptic vesicles fusing transiently and successively without loss of identity. Nature 423(6940), 643–647 (2003)CrossRefGoogle Scholar
  3. 3.
    Atluri, P.P., Ryan, T.A.: The kinetics of synaptic vesicle reacidification at hippocampal nerve terminals. J. Neurosci. 26(8), 2313–2320 (2006)CrossRefGoogle Scholar
  4. 4.
    Barrio, M., Burrage, K., Leier, A., Tian, T.: Oscillatory regulation of Hes1: discrete stochastic delay modelling and simulation. PLoS Comput. Biol. 2(9), e117 (2006)CrossRefGoogle Scholar
  5. 5.
    Cheung, G., Jupp, O., Cousin, M.: Activity-dependent bulk endocytosis and clathrin-dependent endocytosis replenish specific synaptic vesicle pools in central nerve terminals. J. Neurosci. 30(24), 8151–8161 (2010)CrossRefGoogle Scholar
  6. 6.
    Cheung, G., Cousin, M.A.: Adaptor protein complexes 1 and 3 are essential for generation of synaptic vesicles from activity-dependent bulk endosome. J. Neurosci. 32(17), 6014–6023 (2012)CrossRefGoogle Scholar
  7. 7.
    Clayton, E., Cousin, M.: Differential labelling of bulk endocytosis in nerve terminals by FM dyes. Neurochem. Int. 53(3), 51–55 (2008)CrossRefGoogle Scholar
  8. 8.
    Cousin, M.: Use of FM1-43 and other derivatives to investigate neuronal function. Curr. Protoc. Neurosci. 43(2.6), 2.6.1–2.6.12 (2008)Google Scholar
  9. 9.
    Cousin, M.: Activity-dependent bulk synaptic vesicle endocytosis - a fast, high capacity membrane retrieval mechanism. Mol. Neurobiol. 39(3), 185–189 (2009)CrossRefGoogle Scholar
  10. 10.
    Fernandez-Alfonso, T., Ryan, T.A.: A heterogeneous “resting” pool of synaptic vesicles that is dynamically interchanged across boutons in mammalian cns synapses. Brain Cell Biol. 36(1), 87–100 (2008)CrossRefGoogle Scholar
  11. 11.
    Gabriel, T., García-Pérez, E., Mahfooz, K., Goñi, J., Martínez-Turrillas, R., Pérez-Otaño, I., Lo, D., Wesseling, J.: A new kinetic framework for synaptic vesicle trafficking tested in synapsin knock-outs. J. Neurosci. 31(32), 11563–11577 (2011)CrossRefGoogle Scholar
  12. 12.
    Gillespie, D.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  13. 13.
    Golightly, A., Wilkinson, D.: Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo. Interface Focus 1(6), 807–820 (2011)CrossRefGoogle Scholar
  14. 14.
    Granseth, B., Lagnado, L.: The role of endocytosis in regulating the strength of hippocampal synapses. J. Physiol. 586(24), 5969–5982 (2008)CrossRefGoogle Scholar
  15. 15.
    Granseth, B., Odermatt, B., Royle, S.J., Lagnado, L.: Clathrin-mediated endocytosis is the dominant mechanism of vesicle retrieval at hippocampal synapses. Neuron 51(6), 773–786 (2006)CrossRefGoogle Scholar
  16. 16.
    McMahon, H., Boucrot, E.: Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nat. Rev. Mol. Cell Biol. 12(8), 517–533 (2011)CrossRefGoogle Scholar
  17. 17.
    Miesenböck, G., De Angelis, D.A., Rothman, J.E.: Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins. Nature 394(6689), 192–195 (1998)CrossRefGoogle Scholar
  18. 18.
    Royle, S., Granseth, B., Odermatt, B., Derevier, A., Lagnado, L.: Imaging pHluorin-based probes at hippocampal synapses. Meth. Mol. Biol. 457, 293–303 (2008)CrossRefGoogle Scholar
  19. 19.
    Sankaranarayanan, S., De Angelis, D., Rothman, J., Ryan, T.: The use of pHluorins for optical measurements of presynaptic activity. Biophys. J. 79(4), 2199–2208 (2000)CrossRefGoogle Scholar
  20. 20.
    Wu, Y., Yeh, F., Mao, F., Chapman, E.: Biophysical characterization of styryl dye-membrane interactions. Biophys. J. 97(1), 101–109 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Donal Stewart
    • 1
    Email author
  • Stephen Gilmore
    • 2
  • Michael A. Cousin
    • 3
  1. 1.Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Laboratory for Foundations of Computer Science, School of InformaticsUniversity of EdinburghEdinburghUK
  3. 3.Centre for Integrative Physiology, School of Biomedical SciencesUniversity of EdinburghEdinburghUK

Personalised recommendations