An excursion through quantitative model refinement

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Sepinoud Azimi, Eugen Czeizler, Cristian Gratie, Diana Gratie, Bogdan Iancu, Nebiat Ibssa, Ion Petre, Vladimir Rogojin, Tolou Shadbahr, Fatemeh Shokri
Redaktörer: Grzegorz Rozenberg, Arto Salomaa, José M. Sempere, Claudio Zandron
Publiceringsår: 2015
Förläggare: Springer
Moderpublikationens namn: Membrane Computing
Seriens namn: Lecture notes in computer science
Nummer i serien: 9504
Artikelns första sida, sidnummer: 25
Artikelns sista sida, sidnummer: 47
ISBN: 978-3-319-28474-3
eISBN: 978-3-319-28475-0
ISSN: 0302-9743


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).

Senast uppdaterad 2020-01-06 vid 01:31