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Global sensitivity analysis of a homogenized constrained mixture model of arterial growth and remodeling
Citation Link: https://doi.org/10.15480/882.3854
Publikationstyp
Journal Article
Date Issued
2021-05-20
Sprache
English
TORE-DOI
Journal
Volume
145
Issue
1-2
Start Page
191
End Page
221
Citation
Journal of Elasticity 145 (1-2): 191-221 (2021-08-01)
Publisher DOI
Scopus ID
Publisher
Springer Science + Business Media B.V.
Growth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.
Subjects
Abdominal aortic aneurysm
Global sensitivity analysis
Growth and remodeling
Homogenized constrained mixture
DDC Class
570: Biowissenschaften, Biologie
600: Technik
610: Medizin
620: Ingenieurwissenschaften
Funding Organisations
International Graduate School of Science and Engineering (IGSSE)
More Funding Information
Open Access funding enabled and organized by Projekt DEAL.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 257981274, Projektnummer
386349077. The authors also gratefully acknowledge financial support by the International Graduate School of Science and Engineering (IGSSE) of Technical University of Munich, Germany.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 257981274, Projektnummer
386349077. The authors also gratefully acknowledge financial support by the International Graduate School of Science and Engineering (IGSSE) of Technical University of Munich, Germany.
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