A parametric finite element model of leg campaniform sensilla inDrosophilato study CS location and arrangement

Abstract

Abstract Campaniform sensilla (CS) are mechanosensors embedded within the cuticle of many insects at key locations such as nearby leg segment joints or halters. CS located at leg segments were found to respond to cuticle bending which can be induced by walking or jumping movements or by the underlying tensile forces of the muscles. For Drosophila it is unclear how CS location and material property variation influence stress levels within and around CS but this information is crucial to understand how flies might use CS input to adjust walking behaviour. Here, we designed a parametric model of the femoral CS field for Drosophila to allow for a systematic testing of the influence of CS location, orientation and material property variation on stress levels. The model consists of 7 changeable parameters per CS and 12 which can be changed for the CS field. Simulations of leg bending are in line with general beam bending theory: At the specific proximal CS field location nearby the trochantero-femoral leg joint, displacements are smaller than distal, while stresses are higher. When changing CS location towards more distal leg parts the situation changes towards more displacement and less stress. Changes in material property values for CS substructures or whole CS fields have a very low influence on stress or displacement magnitudes (regarding curve shape and amplitude) at the CS caps to which the nerve cells attach. Taken together, our simulation results indicate that for CS fields located at proximal leg parts, the displacements induced by other sources such as muscle tensile forces might be more relevant stimuli than the overall leg bending induced by typical locomotion scenarios. Future parametric finite element models should contain experimentally validated information on the anisotropic and viscoelastic properties of materials contained in this sensory system to further our understanding of CS activation patterns.