Composition Colored Petri Nets for the Refinement of Reaction-based Models

Cristian Gratie, Diana-Elena Gratie, Loïc Paulevé (Editor), Nathalie Théret (Editor)

    Research output: Contribution to journalArticleScientificpeer-review

    6 Citations (Scopus)


    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.
    Original languageUndefined/Unknown
    Pages (from-to)51–72
    JournalElectronic Notes in Theoretical Computer Science
    Publication statusPublished - 2016
    MoE publication typeA1 Journal article-refereed

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