International Conference on Membrane Computing

Membrane Computing pp 25-47

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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9504)

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).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sepinoud Azimi
    • 1
    • 3
  • Eugen Czeizler
    • 1
    • 3
  • Cristian Gratie
    • 1
    • 3
  • Diana Gratie
    • 1
    • 3
  • Bogdan Iancu
    • 1
    • 3
  • Nebiat Ibssa
    • 2
  • Ion Petre
    • 1
    • 3
  • Vladimir Rogojin
    • 1
    • 3
  • Tolou Shadbahr
    • 1
  • Fatemeh Shokri
    • 1
  1. 1.Department of Computer ScienceÅbo Akademi UniversityTurkuFinland
  2. 2.Department of Information TechnologyUniversity of TurkuTurkuFinland
  3. 3.Turku Centre for Computer Science (TUCS)TurkuFinland

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