Using physiologically based models to predict in vivo skeletal muscle energetics.

IF 2.8 2区 生物学 Q2 BIOLOGY
Journal of Experimental Biology Pub Date : 2025-04-01 Epub Date: 2025-03-31 DOI:10.1242/jeb.249966
Ryan N Konno, Glen A Lichtwark, Taylor J M Dick
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引用次数: 0

Abstract

Understanding how muscles use energy is essential for elucidating the role of skeletal muscle in animal locomotion. Yet, experimental measures of in vivo muscle energetics are challenging to obtain, so physiologically based muscle models are often used to estimate energy use. These predictions of individual muscle energy expenditure are not often compared with indirect whole-body measures of energetic cost. Here, we examined and illustrated the capability of physiologically based muscle models to predict in vivo measures of energy use, which rely on fundamental relationships between muscle mechanical state and energy consumption. To improve model predictions and ensure a physiological basis for model parameters, we refined our model to include data from isolated muscle experiments and account for inefficiencies in ATP recovery processes. Simulations were performed to capture three different experimental protocols, which involved varying contraction frequency, duty cycle and muscle fascicle length. Our results demonstrated the ability of the model to capture the dependence of energetic cost on mechanical state across contractile conditions, but tended to underpredict the magnitude of energetic cost. Our analysis revealed that the model was most sensitive to the force-velocity parameters and the data informing the energetic parameters when predicting in vivo energetic rates. This work highlights that it is the mechanics of skeletal muscle contraction that govern muscle energy use, although the precise physiological parameters for human muscle likely require detailed investigation.

使用基于生理学的模型来预测体内骨骼肌能量学。
了解肌肉如何使用能量对于阐明骨骼肌在动物运动中的作用至关重要。然而,体内肌肉能量的实验测量是具有挑战性的,因此基于生理的肌肉模型经常被用来估计能量使用。这些对个体肌肉能量消耗的预测通常不会与间接的全身能量消耗测量进行比较。在这里,我们检查并说明了基于生理的肌肉模型预测体内能量使用测量的能力,这依赖于肌肉力学状态和能量消耗之间的基本关系。为了改进模型预测并确保模型参数的生理基础,我们改进了我们的模型,纳入了来自分离肌肉实验的数据,并考虑了ATP恢复过程的低效率。模拟了三种不同的实验方案,包括不同的收缩频率、占空比和肌束长度。我们的研究结果表明,该模型能够捕捉能量成本在收缩条件下对机械状态的依赖,但往往低于预测能量成本的大小。我们的分析表明,该模型在预测体内能量率时对力-速度参数和能量参数的数据最为敏感。这项工作强调了控制肌肉能量使用的是骨骼肌收缩的机制,尽管人类肌肉的精确生理参数可能需要详细的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
10.70%
发文量
494
审稿时长
1 months
期刊介绍: Journal of Experimental Biology is the leading primary research journal in comparative physiology and publishes papers on the form and function of living organisms at all levels of biological organisation, from the molecular and subcellular to the integrated whole animal.
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