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

Diana-Elena Gratie, Cristian Gratie, Composition Colored Petri Nets for the Refinement of Reaction-based Models. Electronic Notes in Theoretical Computer Science 326, 51–72, 2016.

http://dx.doi.org/10.1016/j.entcs.2016.09.018

Abstract:

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.

BibTeX entry:

@ARTICLE{jGrGr16a,
  title = {Composition Colored Petri Nets for the Refinement of Reaction-based Models},
  author = {Gratie, Diana-Elena and Gratie, Cristian},
  journal = {Electronic Notes in Theoretical Computer Science},
  volume = {326},
  publisher = {Elsevier},
  pages = {51–72},
  year = {2016},
  keywords = {Composition Petri nets; composition colored Petri nets; compositional structure; reaction-based model; data refinement},
  ISSN = {1571-0661},
}

Belongs to TUCS Research Unit(s): Computational Biomodeling Laboratory (Combio Lab)

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