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BioMed Research International
Volume 2014 (2014), Article ID 980501, 10 pages
Research Article

Parallel Solutions for Voxel-Based Simulations of Reaction-Diffusion Systems

1Institute of Applied Mathematics and Information Technologies, National Research Council of Italy, Via de Marini, 16149 Genoa, Italy
2Genetic Unit, IRCCS Saint John of God, Clinical Research Centre, Via Pilastroni 4, 25125 Brescia, Italy
3Institute of Biomedical Technologies, National Research Council of Italy, Via Fratelli Cervi 93, 20090 Segrate, Milan, Italy

Received 21 February 2014; Revised 12 May 2014; Accepted 18 May 2014; Published 12 June 2014

Academic Editor: Horacio Pérez-Sánchez

Copyright © 2014 Daniele D’Agostino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There is an increasing awareness of the pivotal role of noise in biochemical processes and of the effect of molecular crowding on the dynamics of biochemical systems. This necessity has given rise to a strong need for suitable and sophisticated algorithms for the simulation of biological phenomena taking into account both spatial effects and noise. However, the high computational effort characterizing simulation approaches, coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviours, makes such kind of algorithms very time-consuming for studying real systems. So far, different parallelization approaches have been deployed to reduce the computational time required to simulate the temporal dynamics of biochemical systems using stochastic algorithms. In this work we discuss these aspects for the spatial TAU-leaping in crowded compartments (STAUCC) simulator, a voxel-based method for the stochastic simulation of reaction-diffusion processes which relies on the S -DPP algorithm. In particular we present how the characteristics of the algorithm can be exploited for an effective parallelization on the present heterogeneous HPC architectures.