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An Excursion Through Quantitative Model Refinement

Sepinoud Azimi, Eugen Czeizler, Cristian Gratie, Diana Gratie, Bogdan Iancu, Nebiat Ibssa, Ion Petre, Vladimir Rogojin, Tolou Shadbahr, Fatemeh Shokri, An Excursion Through Quantitative Model Refinement. In: Grzegorz Rozenberg, Arto Salomaa, José M. Sempere, Claudio Zandron (Eds.), Membrane Computing, Lecture Notes in Computer Science 9504, 25–47, Springer, 2015.

Abstract:

There is growing interest in creating large-scale computational models for biological process. One of the challenges in such a project is to fit and validate larger and larger models, a process that requires more high-quality experimental data and more computational effort as the size of the model grows. Quantitative model refinement is a recently proposed model construction technique addressing this challenge. It proposes to create a model in an iterative fashion by adding details to its species, and to fix the numerical setup in a way that guarantees to preserve the fit and validation of the model. In this survey we make an excursion through quantitative model refinement – this includes introducing the concept of quantitative model refinement for reaction-based models, for rule-based models, for Petri nets and for guarded command language models, and to illustrate it on three case studies (the heat shock response, the ErbB signaling pathway, and the self-assembly of intermediate filaments).

BibTeX entry:

@INBOOK{cAzCzGrGrIaIbPeRoShSh15a,
  title = {An Excursion Through Quantitative Model Refinement},
  booktitle = {Membrane Computing},
  author = {Azimi, Sepinoud and Czeizler, Eugen and Gratie, Cristian and Gratie, Diana and Iancu, Bogdan and Ibssa, Nebiat and Petre, Ion and Rogojin, Vladimir and Shadbahr, Tolou and Shokri, Fatemeh},
  volume = {9504},
  series = {Lecture Notes in Computer Science},
  editor = {Rozenberg, Grzegorz and Salomaa, Arto and Sempere, José M. and Zandron, Claudio},
  publisher = {Springer},
  pages = {25–47},
  year = {2015},
  ISSN = {0302-9743},
}

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

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