الفهرس | Only 14 pages are availabe for public view |
Abstract Studying and analyzing biological systems is an essential core in order to understand biological phenomena. Toward achieving this goal, it is imperative to construct and execute accurate models to predict the behaviour of the underlying system and get more insights about the interactions between the different model components. Coloured Petri nets are a promising tool to model such biological systems, which extend the power of Petri nets by assigned colours to places. However, the sequential implementation of coloured Petri net execution is prohibitively slow. Particularly, when complex models are considered that may contain reactions or species at different scales. In this thesis, we focus on a specific class of Petri nets called coloured hybrid Petri nets (HPN C), which can combine discrete and continuous components at the same model. However, speeding up the simulation of HPN C models is of a paramount importance. One direction to release this goal is to resort to parallel processing. In this thesis, we use the Graphics Processing Units (GPU) to increase the efficiency of simulating coloured hybrid models. |