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BMC Neuroscience

Open Access

Synaptic bouton sizes are tuned to best fit their physiological performances

  • Markus Knodel1Email author,
  • Gillian Queisser1,
  • Dan Bucher2,
  • Romina Geiger2,
  • Lee How Ge2,
  • Alfio Grillo1,
  • Christoph Schuster2 and
  • Gabriel Wittum1
BMC Neuroscience201112(Suppl 1):P371

Published: 18 July 2011

To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain the rather astonishing motor patterns presented here which we observed within in vivo recordings which underlie peristaltic contractions at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva. To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and Po),the assigning of two different tasks with respect to short- and long-time behaviour could allow for an optimized interplay of different synapse types. As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues. Figure 1.
Figure 1

Stimulated Bouton, Simulation

Authors’ Affiliations

GCSC Frankfurt, Frankfurt University
IZN Heidelberg, Heidelberg University


  1. Jan L, Jan Y: Properties of the larval neuromuscular junction in Drosophila melanogaster. J Physiol. 1976, 262 (1): 189-214.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Schuster C, Davis G, Fetter R, Goodman C: Genetic dissection of structural and functional components of synaptic plasticity. ii fasciclin ii controls presynaptic structural plasticity. Neuron. 1996, 17 (4): 655-67. 10.1016/S0896-6273(00)80198-1.View ArticlePubMedGoogle Scholar
  3. Delgado R, Maureira C, Oliva C, Kidokoro Y, Labarca P: Size of vesicle pools, rates of mobilization, and recycling at neuromuscular synapses of a Drosophila mutant, shibire. Neuron. 2000, 28: 941-53. 10.1016/S0896-6273(00)00165-3.View ArticlePubMedGoogle Scholar
  4. Bastian P, Birken K, Johannsen K, Lang S, Reichenberger V, Wieners C, Wittum G, Wrobel CA: parallel software-platform for solving problems of partial differential equations using unstructured gr ids and adaptive multigrid methods. High performance computing in science and engineering. Edited by: W. Jäger and E. Krause. 1999, Springer, 326-339.Google Scholar


© Knodel et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.