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Villaverde, A.F.* ; Pathirana, D.* ; Fröhlich, F. ; Hasenauer, J.* ; Banga, J.R.*

A protocol for dynamic model calibration.

Brief. Bioinform. 23:bbab387 (2022)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Dynamic Modelling ; Identifiability ; Identification ; Optimization ; Parameter Estimation ; Systems Biology
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Zeitschrift Briefings in Bioinformatics
Quellenangaben Band: 23, Heft: 1, Seiten: , Artikelnummer: bbab387 Supplement: ,
Verlag Oxford University Press
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Ministerio de Ciencia e Innovación
Ramón y Cajal Fellowship