Sensitivity of muscle force response of a two-state cross-bridge model to variations in model parameters.

Cross-bridge theory Huxley-type models Monte Carlo simulation muscle contraction muscle model sensitivity analysis

Journal

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
ISSN: 2041-3033
Titre abrégé: Proc Inst Mech Eng H
Pays: England
ID NLM: 8908934

Informations de publication

Date de publication:
Oct 2022
Historique:
pubmed: 17 9 2022
medline: 18 10 2022
entrez: 16 9 2022
Statut: ppublish

Résumé

Muscle models based on the cross-bridge theory (Huxley-type models) are frequently used to calculate muscle forces for different contractile conditions. Dynamic and nonlinear characteristics of muscle forces produced during isometric, concentric, and eccentric contractions can be represented to a limited extent by using cross-bridge models. Cross-bridge models use various parameters to simulate force responses. However, there remains uncertainty as to the effect of changes in model parameters on force responses in Huxley-type models. In this study, we aimed to analyze the sensitivity of force response to changes in model parameters in Huxley-type models. A two-state Huxley model was used to determine the cross-bridge attachment distributions and forces for shortening and lengthening contractions. Sensitivity of muscle force to changes in attachment rate, detachment rate, and cross-bridge binding distance was examined within a range of ±20% of the nominal value using Monte Carlo simulations. Changes in the detachment rate influenced the predicted muscle forces the most for lengthening contractions, while changes in attachment rate and binding distance affected forces the most for shortening contractions. These results show once more the asymmetry between shortening and lengthening contractions and the difficulty in using a single cross-bridge model to predict forces during shortening and elongation accurately.

Identifiants

pubmed: 36113060
doi: 10.1177/09544119221122062
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1513-1520

Auteurs

Faruk Ortes (F)

Department of Mechanical Engineering, Faculty of Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey.

Azim Jinha (A)

Human Performance Lab, Faculty of Kinesiology, University of Calgary, Calgary, Canada.

Walter Herzog (W)

Human Performance Lab, Faculty of Kinesiology, University of Calgary, Calgary, Canada.

Yunus Ziya Arslan (Y)

Institute of Graduate Studies in Science and Engineering, Department of Robotics and Intelligent Systems, Turkish-German University, Istanbul, Turkey.

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Classifications MeSH