Modeling history-dependent cross-bridge dynamics for force generation in a muscle unit Pubblico

Abstract

Muscles exhibit history-dependent and transient behaviors during movement. However, the mechanisms of force production that respond to prior movement and activation remain unclear. Existing muscle models, such as the Hill-type model of contraction dynamics, have encompassed important behaviors under static conditions by using experimental results to govern the kinematic responses of the model. However, these force-length and force-velocity relationships are not always generalizable to forces generated during movement. Muscle models have yet to incorporate physiological mechanisms of contraction dynamics on the filament level. The interfilamentary cross-bridge interactions between actin binding sites and cycling myosin heads are thought to be responsible for history-dependent behaviors, such as short-range stiffness that produces an increased initial force response to an imposed stretch.

By modeling the cross-bridge dynamics of a muscle unit, we are able to incorporate key features of force generation during dynamic movements. Our model consists of a two-state cross-bridge system, governed by activation dynamics and the position and time dependent cycling behavior of the myosin heads. Initial simulations confirmed that the short-range stiffness and rapid transient responses of the model are similar to previous simulation results as well as filament level experimental results. These simulations verified that the force behaviors were consistent with cross-bridge mechanisms and exhibited history-dependent responses. We then performed force-length and force-velocity tests to characterize the emergent steady state properties of the model. The cross-bridge model was able to intrinsically produce length and velocity dependent forces under static conditions consistent with experimental results used in Hill-type models. By defining these behaviors, the model has the potential to be applied to dynamic movement simulations. History-dependence is thought to be a major contributor to locomotive strategies needed for navigating unpredictable environments, such as reacting to unexpected ground forces. Modeling the muscle properties that contribute to robust behaviors in complex environments may allow us to understand the mechanisms of movement, and movement control, in healthy and impaired systems.

Table of Contents

Table of Contents

Introduction................................................................................................................................................1 Methods.....................................................................................................................................................11 Results.......................................................................................................................................................21 Discussion..................................................................................................................................................31 References..................................................................................................................................................34

Figures

Figure 1: Imposed length changes during force-length and force-velocity simulations.................18

Figure 2: Force-length curves from the cross-bridge model compared to the Hill-type model.......22

Figure 3: Force-velocity curves from the cross-bridge model compared to the Hill-type model.....25

Figure 4: Active force responses to ramp-hold simulations.............................................................27

Figure 5: Transient active force responses to small-scale length changes.......................................28

Figure 6: Active force responses to timing of ramp-release stretches..............................................29

About this Honors Thesis

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School	Emory College
Department	Neuroscience and Behavioral Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Biology, Neuroscience Engineering, Biomedical Computer Science
Parola chiave	biomechanics musculoskeletal modeling computational neuroscience
Committee Chair / Thesis Advisor	Lena H. Ting, Emory University
Committee Members	Trisha M. Kesar, Emory University Gregory S. Sawicki, Georgia Institute of Technology Samuel J. Sober, Emory University

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