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

Author:

Ortes Faruk1,Jinha Azim2,Herzog Walter2,Ziya Arslan Yunus3ORCID

Affiliation:

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

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

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

Abstract

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.

Publisher

SAGE Publications

Subject

Mechanical Engineering,General Medicine

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