Mammalian Muscle Model for Predicting Force and Energetics During Physiological Behaviors

Abstract

Muscles convert metabolic energy into mechanical work. A computational model of muscle would ideally compute both effects efficiently for the entire range of muscle activation and kinematic conditions (force and length). We have extended the original Virtual Muscle algorithm (Cheng , 2000) to predict energy consumption for both slow- and fast-twitch muscle fiber types, partitioned according to the activation process (E(a)), cross-bridge cycling (E(xb)) and ATP/PCr recovery (E(recovery)). Because the terms of these functions correspond to identifiable physiological processes, their coefficients can be estimated directly from the types of experiments that are usually performed and extrapolated to dynamic conditions of natural motor behaviors. We also implemented a new approach to lumped modeling of the gradually recruited and frequency modulated motor units comprising each fiber type, which greatly reduced computational time. The emergent behavior of the model has significant implications for studies of optimal motor control and development of rehabilitation strategies because its trends were quite different from traditional estimates of energy (e.g., activation, force, stress, work, etc.). The model system was scaled to represent three different human experimental paradigms in which muscle heat was measured during voluntary exercise; predicted and observed energy rate agreed well both qualitatively and quantitatively.

Effect of vastus lateralis fatigue on load sharing between quadriceps femoris muscles during isometric knee extensions

“…For instance, as VL was prefatigued, an increase of the force produced by VL during Tlim appears to contradict an optimal control based on minimizing energy cost (Tsianos et al 2012). Interestingly, a similar observation has been reported by De Rugy et al (2012).…”

Muscle Coordination Is Habitual Rather than Optimal

“…As in Experiment 1, however, the muscle activation was well described by a simple linear rescaling of original patterns of activity, and not reoptimized for the novel biomechanics. It should be noted that recruitment of the virtually cut muscle in Experiment 1 and the actually damaged muscle in Experiment 3 would be associated with substantial energy consumption related to activation (Tsianos et al, 2012), so the observed increase in recruitment is not consistent with minimization of energetic cost. It is possible that the brain has an internal model of the defective muscle but chooses not to use it to optimize energy consumption or that it is unable to make corrections to that internal model within the time frame of these experiments.…”

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

“…Among different methods for estimating joint torque from muscle activities using electromyography (EMG) are those models that aim at simulating the real physiological processes of the coupling between excitation and contraction (Buchanan et al 2004) and the mechanical application of muscle tension to generate torques (Tsianos et al 2012). Other techniques are based mainly on the associations between measured muscle activities and joint movement (Castellini et al 2009;Seifert and Fuglevand 2002).…”