الفهرس 
IntroductionOnly 14 pages are availabe for public view
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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.