Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2007

Open Access Research

CellExcite: an efficient simulation environment for excitable cells

Ezio Bartocci13*, Flavio Corradini1, Emilia Entcheva2, Radu Grosu3 and Scott A Smolka3

Author Affiliations

1 Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n.9, Camerino, Italy

2 Department of Biomedical Engineering, Stony Brook University, NY 11794-4400, USA

3 Department of Computer Science, Stony Brook University, NY 11794-4400, USA

For all author emails, please log on.

BMC Bioinformatics 2008, 9(Suppl 2):S3  doi:10.1186/1471-2105-9-S2-S3

Published: 26 March 2008

Abstract

Background

Brain, heart and skeletal muscle share similar properties of excitable tissue, featuring both discrete behavior (all-or-nothing response to electrical activation) and continuous behavior (recovery to rest follows a temporal path, determined by multiple competing ion flows). Classical mathematical models of excitable cells involve complex systems of nonlinear differential equations. Such models not only impair formal analysis but also impose high computational demands on simulations, especially in large-scale 2-D and 3-D cell networks. In this paper, we show that by choosing Hybrid Automata as the modeling formalism, it is possible to construct a more abstract model of excitable cells that preserves the properties of interest while reducing the computational effort, thereby admitting the possibility of formal analysis and efficient simulation.

Results

We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior.

Conclusions

The CellExcite simulation framework for multicellular HA arrays exhibits significantly improved computational efficiency in large-scale simulations, thus opening the possibility for formal analysis based on HA theory. A demo of CellExcite is available at http://www.cs.sunysb.edu/~eha/ webcite.