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Quantitative Model Refinement in Four Different Frameworks, with Applications to the Heat Shock Response

Diana-Elena Gratie, Bogdan Iancu, Sepinoud Azimi, Ion Petre, Quantitative Model Refinement in Four Different Frameworks, with Applications to the Heat Shock Response. In: Luigia Petre, Emil Sekerinski (Eds.), From Action Systems to Distributed Systems, 201–2014, Taylor & Francis, 2016.

Abstract:

Quantitative model refinement is an essential step in the model development cycle. Starting with a high level, abstract representation of a biological system, one often needs to add details to this representation to reflect changes in its constituent elements. Any such refinement step has two aspects: one structural and one quantitative. The structural
aspect of the refinement defines an increase in the resolution of its representation, while the
quantitative one specifies a numerical setup for the model that ensures its fit preservation at every refinement step. We discuss in this paper the implementation of quantitative model refinement in four extensively used bio-modelling frameworks: ODE-based models, rule-based models, Petri net models, and guarded command language models, emphasizing the
specificity for every model implementation. We argue that quantitative model refinement is framework-independent, being implementable in all chosen frameworks despite their different underlying modelling paradigms.

BibTeX entry:

@INBOOK{cGrIaAzPe16a,
  title = {Quantitative Model Refinement in Four Different Frameworks, with Applications to the Heat Shock Response},
  booktitle = {From Action Systems to Distributed Systems},
  author = {Gratie, Diana-Elena and Iancu, Bogdan and Azimi, Sepinoud and Petre, Ion},
  editor = {Petre, Luigia and Sekerinski, Emil},
  publisher = {Taylor & Francis},
  pages = {201–2014},
  year = {2016},
  ISSN = {978-0-335; 978-0-415; 978-0-84},
}

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

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