The reverse engineering (RE) of biochemical reaction networks is a fundamental and very complex task in Systems Biology. My PhD thesis is focused on the definition of an automatic RE methodology based on the fusion of Genetic Programming and Particle Swarm Optimization. The methodology I propose relies on the execution of a massive number of simulations, whose computational costs are relevant. To the aim of reducing the overall running time, I am implementing the methodology on a parallel architecture, namely, Nvidia’s CUDA.
Nobile, M., Mauri, G. (2013). Evolutionary inference of biochemical reaction networks accelerated on graphics processing units. In 2013 International Conference on High Performance Computing & Simulation (HPCS) (pp.668-670). IEEE [10.1109/HPCSim.2013.6641490].
Evolutionary inference of biochemical reaction networks accelerated on graphics processing units
NOBILE, MARCO SALVATORE;MAURI, GIANCARLO
2013
Abstract
The reverse engineering (RE) of biochemical reaction networks is a fundamental and very complex task in Systems Biology. My PhD thesis is focused on the definition of an automatic RE methodology based on the fusion of Genetic Programming and Particle Swarm Optimization. The methodology I propose relies on the execution of a massive number of simulations, whose computational costs are relevant. To the aim of reducing the overall running time, I am implementing the methodology on a parallel architecture, namely, Nvidia’s CUDA.
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