Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access

Integration of predictive-corrective incompressible SPH and Hodgkin-Huxley based models in the OpenWorm in silico model of C. elegans

  • Michael Vella1Email author,
  • Andrey Palyanov2,
  • Padraig Gleeson3 and
  • Sergey Khayrulin2
BMC Neuroscience201314(Suppl 1):P209

DOI: 10.1186/1471-2202-14-S1-P209

Published: 8 July 2013

OpenWorm is an international collaboration with the aim of producing an integrative computational model of Caenorhabditis elegans to further the understanding of how macroscopic behaviour of the organism emerges from aggregated biophysical processes. A core component of the project involves the integration of electrophysiological modelling and predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) to model how neuronal and muscle dynamics effect the nematode's behaviour. Several tools are being utilised and developed in the course of the project:

  • Electrophysiological model parameters are constrained to reproduce experimental measurements using the Optimal Neuron toolkit [1]

  • A PCISPH solver is under development [2] - a combination of general PCISPH algorithms proposed by [3], boundary-handling algorithms proposed by [4], a surface tension model based on [5] and our own implementation of elastic matter and biophysics-specific features, as well as parallelization, optimization and tuning. It is the first open source, parallel OpenCL/C++ PCISPH high-performance implementation.

  • A generic model integration framework (Gepetto [6]) will be used to integrate electrophysiology and body-wall interactions

  • All electrophysiological models are NeuroML-compatible [7].

  • The Open Worm Browser provides a powerful way to visualise C. Elegans anatomy [8]

All of the above mentioned applications are open source, freely available and can be used for modelling other neuronal systems.

Authors’ Affiliations

(1)
Department of Physiology, Development and Neuroscience, University of Cambridge
(2)
Lab. of complex systems simulations, A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences
(3)
Department of Neuroscience, Physiology and Pharmacology, University College London

References

  1. Optimal Neuron Toolkit. [https://github.com/vellamike/Optimal-Neuron]
  2. Open Worm PCISPH Simulator. [https://github.com/openworm/Smoothed-Particle-Hydrodynamics]
  3. Solenthaler B, Pajarola R: Predictive-corrective incompressible SPH. ACM Trans Graph. 2009, 28 (3):
  4. Ihmsen M, Akinci N, Gissler M, Teschner M: Boundary Handling and Adaptive Time-stepping for PCISPH. Proc VRIPHYS. 2010, Copenhagen, Denmark, 79-88. Nov 11-12Google Scholar
  5. Becker M, Teschner M: Weakly compressible SPH for free surface flows. Proceedings of the 2007 ACM SIG-GRAPH/Eurographics. 2007Google Scholar
  6. Geppetto Simulation Engine. [https://github.com/openworm/OpenWorm/wiki/Geppetto--Overview]
  7. Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, et al: NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Comput Biol. 2010, 6 (6): e1000815-10.1371/journal.pcbi.1000815. doi:10.1371/journal.pcbi.1000815PubMed CentralView ArticlePubMedGoogle Scholar
  8. Open Worm Browser. [http://browser.openworm.org/]

Copyright

© Vella et al; licensee BioMed Central Ltd. 2013

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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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