Spreading out Muscle Mass within a Hill-Type Model: A Computer Simulation Study

Günther, Michael; Röhrle, Oliver; Haeufle, Daniel F. B.; Schmitt, Syn

doi:10.1155/2012/848630

Cited by 39 publications

(39 citation statements)

References 67 publications

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“…However, virtually all Hill-type muscle models, which are ubiquitously used in biomechanics to understand and predict muscle behaviour, ignore the inertial effect of tissue mass. The importance of inertial effects within muscle was recently demonstrated in a second-order dynamic model that distributed the tissue mass in a series of discrete points through the muscle [13]. This mass model showed time-delays in force development that were due to the internal inertial mass, with the effect being more pronounced for larger muscle where the ratio of mass to contractile force was greater.…”

Section: Introductionmentioning

confidence: 98%

“…This mass model showed time-delays in force development that were due to the internal inertial mass, with the effect being more pronounced for larger muscle where the ratio of mass to contractile force was greater. This model makes it possible to consider the inertial effects of fibres within whole muscle, even if they are inactive during submaximal contractions; however, this was not studied at the time [13]. Inertial effects may be considerable for muscles that have a large mass, particularly when activations are low and there is little contractile force to move the inertial load.…”

Section: Introductionmentioning

confidence: 99%

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Muscle shortening velocity depends on tissue inertia and level of activation during submaximal contractions

Ross¹,

Wakeling²

2016

Biol. Lett.

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In order to perform external work, muscles must do additional internal work to deform their tissue, and in particular, to overcome the inertia due to their internal mass. However, the contribution of the internal mass within a muscle to the mechanical output of that muscle has only rarely been studied. Here, we use a dynamic, multi-element Hill-type muscle model to examine the effects of the inertial mass within muscle on its contractile performance. We find that the maximum strain-rate of muscle is slower for lower activations and larger muscle sizes. As muscle size increases, the ability of the muscle to overcome its inertial load will decrease, as muscle tension is proportional to cross-sectional area and inertial load is proportional to mass. Thus, muscles that are larger in size will have a higher inertial cost to contraction. Similarly, when muscle size and inertial load are held constant, decreasing muscle activation will increase inertial cost to contraction by reducing muscle tension. These results show that inertial loads within muscle contribute to a slowing of muscle contractile velocities (strain-rates), particularly at the submaximal activations that are typical during animal locomotion.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Muscle shortening velocity depends on tissue inertia and level of activation during submaximal contractions

Ross¹,

Wakeling²

2016

Biol. Lett.

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“…Owing to their descent, the use of WM models seems to be currently restricted to estimating the influence of inertia on skeletal loads in impact situations. Beyond this, we are aware of just four studies that have made an attempt to assess the backlash of mass distribution on end‐point muscle contraction, whereas local contraction phenomena within the muscle itself, including wave propagation, have not been addressed at all. All these four studies had however assumed Hill‐type contractile characteristics, which reflect steady‐state properties, while simulating obviously dynamic, nonsteady‐state loading situations of the muscle.…”

Section: Hill‐type Muscle Models: From Pure Phenomenology To a Firstmentioning

confidence: 99%

“…All these four studies had however assumed Hill‐type contractile characteristics, which reflect steady‐state properties, while simulating obviously dynamic, nonsteady‐state loading situations of the muscle. Assuming that such first‐order contraction dynamics yet interact with muscle internal mass distribution, two questions were addressed by our own study (Figure ): (a) what is the time scale on which the muscle responds to a force step? (b) How does this response scale with muscle design parameters?…”

Section: Hill‐type Muscle Models: From Pure Phenomenology To a Firstmentioning

confidence: 99%

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The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models

2019

Self Cite

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Skeletal muscle is one of the most fascinating and crucial ingredients of motion generation in nature. Since the beginning of science, people dedicate their life as researchers to enhance knowledge about this biological motor. Thus, the scientific knowledge about the skeletal muscle is overwhelmingly broad and detailed. This contribution collects knowledge about the active and passive dynamics of skeletal muscle. Furthermore, it highlights a special perspective in which not only the muscle itself, but also the role muscles play in the interaction with other structures is studied. The first section introduces this systems biophysics perspective, which clusters the investigation of the relations, interactions and dependencies between muscles and the other structures in the movement apparatus. In the second section, the muscles are considered in more detail by describing three approaches to muscle modeling. The third section deals with recent advances based on Hill‐type models, such as, for example, the integration of mass.

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Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

Homs‐Pons,

Lautenschlager,

Schmid

et al. 2024

GAMM-Mitteilungen

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The functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

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Spreading out Muscle Mass within a Hill-Type Model: A Computer Simulation Study

Cited by 39 publications

References 67 publications

Muscle shortening velocity depends on tissue inertia and level of activation during submaximal contractions

Muscle shortening velocity depends on tissue inertia and level of activation during submaximal contractions

The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models

Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

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