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).
Original language | Undefined/Unknown |
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Title of host publication | Membrane Computing |
Editors | Grzegorz Rozenberg, Arto Salomaa, José M. Sempere, Claudio Zandron |
Publisher | Springer |
Pages | 25–47 |
ISBN (Electronic) | 978-3-319-28475-0 |
ISBN (Print) | 978-3-319-28474-3 |
DOIs | |
Publication status | Published - 2015 |
MoE publication type | A4 Article in a conference publication |
Event | conference - XV European Congress of Ichthyology Duration: 1 Jan 2015 → … |
Conference
Conference | conference |
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Period | 01/01/15 → … |