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High Performance Hybrid Functional Petri Net Simulations of Biological Pathway Models on CUDA

Georgios Chalkidis, Masao Nagasaki, Satoru Miyano
The University of Tokyo, Tokyo
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010

@article{chalkidis2010high,

   title={High Performance Hybrid Functional Petri Net Simulations of Biological Pathway Models on CUDA},

   author={Chalkidis, G. and Nagasaki, M. and Miyano, S.},

   journal={IEEE IEEE/ACM Transactions on Computational Biology and Bioinformatics},

   issn={1545-5963},

   year={2010},

   publisher={Published by the IEEE Computer Society}

}

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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 speed up of approximately 7x for a model containing 1 600 cells.
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