Where academic tradition
meets the exciting future

On the Implementation of Quantitative Model Refinement

Bogdan Iancu, Diana-Elena Gratie, Sepinoud Azimi, Ion Petre, On the Implementation of Quantitative Model Refinement. In: Adrian-Horia Dediu, Carlos-Martin Vide, Bianca Truthe (Eds.), Algorithms for Computational Biology, Lecture Notes in Computer Science 8542, 95–106, Springer, 2014.



The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.


Full publication in PDF-format

BibTeX entry:

  title = {On the Implementation of Quantitative Model Refinement},
  booktitle = {Algorithms for Computational Biology},
  author = {Iancu, Bogdan and Gratie, Diana-Elena and Azimi, Sepinoud and Petre, Ion},
  volume = {8542},
  series = {Lecture Notes in Computer Science},
  editor = {Dediu, Adrian-Horia and Vide, Carlos-Martin and Truthe, Bianca},
  publisher = {Springer},
  pages = {95–106},
  year = {2014},
  keywords = {Quantitative model refinement, heat shock response, acetylation, rule-based modeling, Petri nets, model checking},
  ISSN = {0302-9743},

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

Publication Forum rating of this publication: level 1

Edit publication