title: 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.
authors.affiliation: Biomedical Engineering Department, University of Southern California, Los Angeles, CA 90089, USA. tsianos@usc.edu
authors.forename: George A
authors.initials: GA
authors.lastname: Tsianos
authors.forename: Cedric
authors.initials: C
authors.lastname: Rustin
authors.forename: Gerald E
authors.initials: GE
authors.lastname: Loeb
mesh_terms: D000255:Adenosine Triphosphate / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D005544:Forecasting; D016013:Likelihood Functions; D008322:Mammals; D008954:Models, Biological; D018656:Muscle Fibers, Fast-Twitch / Q000502:physiology; D018485:Muscle Fibers, Skeletal / Q000502:physiology; D018657:Muscle Fibers, Slow-Twitch / Q000502:physiology; D018482:Muscle, Skeletal / Q000378:metabolism / Q000502:physiology*; D010725:Phosphocreatine / Q000378:metabolism; D005082:Physical Exertion / Q000502:physiology*; D011999:Recruitment, Neurophysiological / Q000502:physiology; D015203:Reproducibility of Results; D012518:Sarcomeres / Q000502:physiology; D013816:Thermodynamics
chemical_list: D010725:Phosphocreatine; D000255:Adenosine Triphosphate