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Issue No. 06 - November/December (2011 vol. 8)
ISSN: 1545-5963
pp: 1545-1556
Georgios Chalkidis , The University of Tokyo, Tokyo
Masao Nagasaki , The University of Tokyo, Tokyo
Satoru Miyano , The University of Tokyo, Tokyo
Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7{\times} for a model containing 1,600 cells.
CUDA, GPGPU, hybrid functional Petri nets, biological pathway modeling, delta-notch signaling.

S. Miyano, M. Nagasaki and G. Chalkidis, "High Performance Hybrid Functional Petri Net Simulations of Biological Pathway Models on CUDA," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1545-1556, 2010.
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