Validation of the energetics of a Huxley muscle-tendon complex model using experimental data obtained from mouse...

The aim of this study was to advance muscle models that unify mechanical behaviour and metabolic energy expenditure. To that end, we compared predictions of force and metabolic energy expenditure of a Huxley-type muscle-tendon complex (MTC) model with previously obtained experimental data. In our published model, we extended the classic Huxley formulation by incorporating force-length dependency, series elasticity and activation dynamics. Metabolic energy expenditure was modelled as the weighted sum of cross-bridge cycling and calcium pumping costs. In the associated experiment, fibre bundles from nine mouse soleus muscles underwent sinusoidal contractions, while oxygen consumption and tendon force were measured. The bundles were stimulated during both shortening and lengthening, and measurements were taken before and after adding blebbistatin, which blocks cross-bridge cycling but leaves calcium handling unaffected. This enabled separate estimation of metabolic energy costs for each process. In the present study, we modelled these previously published data. Parameters governing model mechanical behaviour were calibrated using trials without oxygen measurements. We used these parameters in simulations of the oxygen measurement trials, and metabolic parameters were optimized to best match average metabolic power. We found that simulated and measured forces corresponded well (root-mean-square error, RMSE <10% of maximum force). Metabolic energy predictions showed higher error (mean RMSE 20.3%, s.d. 12.6% of measured value), with large inter-animal variability. In four animals, where repeated measures were consistent and data followed expected trends, predictions of metabolic energy expenditure were accurate (RMSE <15%). In the remaining five, greater variability or inconsistent data patterns led to poorer fits. Despite this, given the within-animal variability in oxygen measurements, the metabolic predictions are promising. Combined with previous findings, these results support the potential of Huxley-type models in predictive simulations of human metabolic energy expenditure.