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Composition Colored Petri Nets for the Refinement of Reaction-based Models
Tekijät: Diana-Elena Gratie, Cristian Gratie
Kustantaja: ELSEVIER SCIENCE BV
Julkaisuvuosi: 2016
Journal: Electronic Notes in Theoretical Computer Science
Tietokannassa oleva lehden nimi: ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE
Lehden akronyymi: ELECTRON NOTES THEOR
Vuosikerta: 326
Aloitussivu: 51
Lopetussivu: 72
Sivujen määrä: 22
ISSN: 1571-0661
DOI: https://doi.org/10.1016/j.entcs.2016.09.018
Tiivistelmä
Model refinement is an important step in the model building process. For reaction-based models, data refinement consists in replacing one species with several of its variants in the refined model. We discuss in this paper the implementation of data refinement with Petri nets such that the size of the model (in terms of number of places and transitions) does not increase. We capture the compositional structure of species by introducing a new class of Petri nets, composition Petri nets (ComP-nets), and their colored counterpart, colored composition Petri nets (ComCP-nets). Given a reaction-based model with known compositional structure, represented as a ComP-net, we propose an algorithm for building a ComCP-net which implements the data refinement of the model and has the same network structure as the initial ComP-net.
Model refinement is an important step in the model building process. For reaction-based models, data refinement consists in replacing one species with several of its variants in the refined model. We discuss in this paper the implementation of data refinement with Petri nets such that the size of the model (in terms of number of places and transitions) does not increase. We capture the compositional structure of species by introducing a new class of Petri nets, composition Petri nets (ComP-nets), and their colored counterpart, colored composition Petri nets (ComCP-nets). Given a reaction-based model with known compositional structure, represented as a ComP-net, we propose an algorithm for building a ComCP-net which implements the data refinement of the model and has the same network structure as the initial ComP-net.
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