FM-Sim: Protocol Definition, Simulation and Rate Inference for Neuroscience Assays

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8859)


Synaptic vesicle recycling at the presynaptic terminal of neurons is essential for the maintenance of neurotransmission at central synapses. Among the tools used to visualise the mechanics of this process is time-series fluorescence microscopy. Fluorescent dyes such as FM1-43, or engineered fluorescent versions of synaptic vesicle proteins such as pHluorins, have been employed to reveal different steps of this key process [3,7]. Predictive in silico modelling of potential experimental outcomes would be highly informative for these time consuming and expensive studies.

We present FM-Sim [9], user-friendly software for defining and simulating fluorescence microscopy experimental assays, with the following features: intuitive user definition of experimental protocols; automatic conversion of protocol definitions into time series rate value changes; domain-specific simulation model of a synaptic terminal; experimental data used for model parameter value inference; automatic Bayesian inference of parameter values [1,5] and reduction of inferred parameter set size for Bayesian inference.


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  1. 1.
    Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(3), 269–342 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barrio, M., Burrage, K., Leier, A., Tian, T.: Oscillatory regulation of Hes1: discrete stochastic delay modelling and simulation. PLoS Computational Biology 2(9), e117 (2006)Google Scholar
  3. 3.
    Cousin, M.: Use of FM1-43 and Other Derivatives to Investigate Neuronal Function. In: Current Protocols in Neuroscience, pp. 2–6 (2008)Google Scholar
  4. 4.
    Cousin, M.: Activity-dependent bulk synaptic vesicle endocytosis - a fast, high capacity membrane retrieval mechanism. Molecular Neurobiology 39(3), 185–189 (2009)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    McMahon, H., Boucrot, E.: Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nature Reviews Molecular Cell Biology 12(8), 517–533 (2011)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Moraru, I.I., Schaff, J.C., Slepchenko, B.M., Blinov, M., Morgan, F., Lakshminarayana, A., Gao, F., Li, Y., Loew, L.M.: Virtual cell modelling and simulation software environment. IET Systems Biology 2(5), 352–362 (2008)CrossRefGoogle Scholar
  9. 9.
    Stewart, D., Gilmore, S., Cousin, M.A.: FM-Sim : A hybrid protocol simulator of fluorescence microscopy neuroscience assays with integrated Bayesian inference. To be published in Proceedings Third International Workshop on Hybrid Systems Biology, Vienna, Austria (July 2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  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

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