On the Implementation of Quantitative Model Refinement

Bogdan Iancu; Diana-Elena Gratie; Sepinoud Azimi Rashti; Ion Petre

doi:10.1007/978-3-319-07953-0_8

On the Implementation of Quantitative Model Refinement

Bogdan Iancu, Diana-Elena Gratie, Sepinoud Azimi Rashti, Ion Petre

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

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.

Original language	Undefined/Unknown
Title of host publication	Algorithms for Computational Biology
Editors	A Dediu, C Vide, B Truthe
Publisher	Springer
Pages	95–106
ISBN (Electronic)	978-3-319-07953-0
ISBN (Print)	978-3-319-07952-3
DOIs	https://doi.org/10.1007/978-3-319-07953-0_8
Publication status	Published - 2014
MoE publication type	A4 Article in a conference publication
Event	conference; 2014-07-01; 2014-07-03 - Tarragona, Spain Duration: 1 Jul 2014 → 3 Jul 2014

Conference

Conference	conference; 2014-07-01; 2014-07-03
Period	01/07/14 → 03/07/14

Access to Document

10.1007/978-3-319-07953-0_8

http://link.springer.com/chapter/10.1007%2F978-3-319-07953-0_8

Cite this

@inproceedings{c455ebc8dba24760beb0e16231ff0bef,

title = "On the Implementation of Quantitative Model Refinement",

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.",

author = "Bogdan Iancu and Diana-Elena Gratie and {Azimi Rashti}, Sepinoud and Ion Petre",

year = "2014",

doi = "10.1007/978-3-319-07953-0_8",

language = "Odefinierat/ok{\"a}nt",

isbn = "978-3-319-07952-3",

pages = "95–106",

editor = "A Dediu and C Vide and B Truthe",

booktitle = "Algorithms for Computational Biology",

publisher = "Springer",

note = "conference; 2014-07-01; 2014-07-03 ; Conference date: 01-07-2014 Through 03-07-2014",

}

TY - GEN

T1 - On the Implementation of Quantitative Model Refinement

AU - Iancu, Bogdan

AU - Gratie, Diana-Elena

AU - Azimi Rashti, Sepinoud

AU - Petre, Ion

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-07953-0_8

DO - 10.1007/978-3-319-07953-0_8

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BT - Algorithms for Computational Biology

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