An excursion through quantitative model refinement
Sepinoud Azimi (Åbo Akademi University)
Eugen Czeizler (Åbo Akademi University, Turku Centre for Computer Science)
Cristian Gratie (Turku Centre for Computer Science, Åbo Akademi University)
Diana Gratie (Åbo Akademi University, Turku Centre for Computer Science)
Bogdan Iancu (Åbo Akademi University, Turku Centre for Computer Science)
Nebiat Ibssa (University of Turku)
Ion Petre (Åbo Akademi University)
Vladimir Rogojin (Turku Centre for Computer Science, Åbo Akademi University)
Tolou Shadbahr (Åbo Akademi University)
Fatemeh Shokri (Åbo Akademi University)
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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).