Biophysical model of muscle spindle encoding

Housley, Stephen N.; Powers, Randal K.; Nardelli, Paul; Lee, Sebinne; Blum, Kyle; Bewick, Guy S.; Banks, Robert W.; Cope, Timothy C.

Abstract

Muscle spindles encode mechanosensory information by mechanisms that remain only partially understood. Their complexity is expressed in mounting evidence of various molecular mechanisms that play essential roles in muscle mechanics, mechanotransduction and intrinsic modulation of muscle spindle firing behaviour. Biophysical modelling provides a tractable approach to achieve more comprehensive mechanistic understanding of such complex systems that would be difficult/impossible by more traditional, reductionist means. Our objective here was to construct the first integrative biophysical model of muscle spindle firing. We leveraged current knowledge of muscle spindle neuroanatomy and in vivo electrophysiology to develop and validate a biophysical model that reproduces key in vivo muscle spindle encoding characteristics. Crucially, to our knowledge, this is the first computational model of mammalian muscle spindle that integrates the asymmetric distribution of known voltage-gated ion channels (VGCs) with neuronal architecture to generate realistic firing profiles, both of which seem likely to be of great biophysical importance. Results predict that particular features of neuronal architecture regulate specific characteristics of Ia encoding. Computational simulations also predict that the asymmetric distribution and ratios of VGCs is a complementary and, in some instances, orthogonal means to regulate Ia encoding. These results generate testable hypotheses and highlight the integral role of peripheral neuronal structure and ion channel composition and distribution in somatosensory signalling.

Citation

Housley, S. N., Powers, R. K., Nardelli, P., Lee, S., Blum, K., Bewick, G. S., Banks, R. W., & Cope, T. C. (2024). Biophysical model of muscle spindle encoding. Experimental Physiology, 109(1), 55-65. https://doi.org/10.1113/ep091099

• Journal Article Type: Article
• Acceptance Date: Mar 9, 2023
• Online Publication Date: Mar 26, 2023
• Publication Date: Jan 1, 2024
• Deposit Date: Jun 7, 2023
• Publicly Available Date: Jun 7, 2023
• Journal: Experimental Physiology
• Print ISSN: 0958-0670
• Electronic ISSN: 1469-445X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Issue: 1
• Pages: 55-65
• DOI: https://doi.org/10.1113/ep091099
• Public URL: https://durham-repository.worktribe.com/output/1173025