Meeting Abstract

P3-105   -   Using small-amplitude oscillations in Manduca sexta to create dynamic models for predicting complex workloops Curameng, K*; Wold, E; Sponberg, S; Nishikawa, K; Venkadesan, M; Western New England University; Georgia Institute of Technology; Georgia Institute of Technology; Northern Arizona University; Yale University kayla.curameng@gmail.com

Workloops are used to capture the steady, periodic biomechanical function of muscle. Because muscles behave differently when stretched dynamically than under static conditions and the properties of muscle are so variable amongst and within organisms, several muscles that perform similarly during isometric force response measurements may not perform similarly under other workloop conditions. As such, existing muscle models cannot be used to accurately predict the work produced by a muscle given only its twitch and static material properties. Linear materials usually produce sheared ellipses with a long axis defined by the material’s springy element and a short axis defined by the damping element. New rheological models of muscle suggest that we may be able to predict workloops from a family of these linear models. In this study, we developed a small-amplitude oscillation protocol that would allow us to compare muscle’s dynamic behavior from its linear material behavior, and create semi-elliptic workloops that can be spliced together to predict the shape of complex workloops in a tunable material such as muscle. The data included small-oscillation workloops with constant activation from 0.05mm to 0.5mm. As predicted, the results of the experiment show that, at constant activation, the DLM of Manduca sexta acts as a non-tunable material represented by elliptic workloops with no changes in the net production of force. The workloops strayed further from the standard ellipse shape that was predicted as the amplitude increased towards the in vivo amplitude (0.5mm). These data can be used in future pursuits to validate novel models of muscle that suggest that complex workloops can be predicted by the splicing of linear material models.