On the Implementation of Quantitative Model Refinement

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Iancu B, Gratie D, Azimi S, Petre I
Redaktörer: Dediu A, Vide C, Truthe B
Förläggare: Springer
Publiceringsår: 2014
Tidskrift: Lecture Notes in Computer Science
Förläggare: Springer
Moderpublikationens namn: Algorithms for Computational Biology
Seriens namn: Lecture Notes in Computer Science
Volym: 8542
Artikelns första sida, sidnummer: 95
Artikelns sista sida, sidnummer: 106
ISBN: 978-3-319-07952-3
eISBN: 978-3-319-07953-0
ISSN: 0302-9743


The iterative process of adding details to a model while preserving its numerical behavior is calledquantitative model refinement, and it has been previously discussed for ODE-based models and forkappa-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.

Senast uppdaterad 2020-07-04 vid 04:13