Konno Ryan N, Lichtwark Glen A, Dick Taylor J M
School of Biomedical Sciences, The University of Queensland, St Lucia, QLD 4072, Australia.
School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia.
J Exp Biol. 2025 Apr 1;228(7). doi: 10.1242/jeb.249966. Epub 2025 Mar 31.
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.
了解肌肉如何利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,获取体内肌肉能量学的实验测量具有挑战性,因此基于生理学的肌肉模型常被用于估计能量消耗。这些对单个肌肉能量消耗的预测通常不与能量消耗的间接全身测量进行比较。在此,我们研究并说明了基于生理学的肌肉模型预测体内能量利用测量值的能力,这依赖于肌肉机械状态与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学基础,我们对模型进行了改进,纳入了来自分离肌肉实验的数据,并考虑了ATP恢复过程中的低效性。进行了模拟以捕捉三种不同的实验方案,这些方案涉及改变收缩频率、占空比和肌肉束长度。我们的结果表明,该模型能够捕捉能量消耗在不同收缩条件下对机械状态的依赖性,但往往低估了能量消耗的幅度。我们的分析表明,在预测体内能量率时,该模型对力 - 速度参数以及为能量参数提供信息的数据最为敏感。这项工作强调,尽管人类肌肉精确的生理参数可能需要详细研究,但正是骨骼肌收缩的力学机制决定了肌肉的能量利用。