On the Implementation of Quantitative Model Refinement

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Internal Authors/Editors


Publication Details

List of Authors: Iancu B, Gratie D, Azimi S, Petre I
Editors: Dediu A, Vide C, Truthe B
Publisher: Springer
Publication year: 2014
Journal: Lecture Notes in Computer Science
Publisher: Springer
Book title: Algorithms for Computational Biology
Title of series: Lecture Notes in Computer Science
Volume number: 8542
Start page: 95
End page: 106
ISBN: 978-3-319-07952-3
eISBN: 978-3-319-07953-0
ISSN: 0302-9743


Abstract

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.

Last updated on 2019-16-06 at 04:33