Volume 14 Supplement 1
Integration of predictive-corrective incompressible SPH and Hodgkin-Huxley based models in the OpenWorm in silico model of C. elegans
© Vella et al; licensee BioMed Central Ltd. 2013
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 
A PCISPH solver is under development  - a combination of general PCISPH algorithms proposed by , boundary-handling algorithms proposed by , a surface tension model based on  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 ) will be used to integrate electrophysiology and body-wall interactions
All electrophysiological models are NeuroML-compatible .
The Open Worm Browser provides a powerful way to visualise C. Elegans anatomy 
All of the above mentioned applications are open source, freely available and can be used for modelling other neuronal systems.
- Optimal Neuron Toolkit. [https://github.com/vellamike/Optimal-Neuron]
- Open Worm PCISPH Simulator. [https://github.com/openworm/Smoothed-Particle-Hydrodynamics]
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- Geppetto Simulation Engine. [https://github.com/openworm/OpenWorm/wiki/Geppetto--Overview]
- 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
- Open Worm Browser. [http://browser.openworm.org/]
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.