Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions

Front Neurosci. 2023 Mar 15:17:1146097. doi: 10.3389/fnins.2023.1146097. eCollection 2023.

Abstract

The stomach is extensively innervated by the vagus nerve and the enteric nervous system. The mechanisms through which this innervation affects gastric motility are being unraveled, motivating the first concerted steps towards the incorporation autonomic regulation into computational models of gastric motility. Computational modeling has been valuable in advancing clinical treatment of other organs, such as the heart. However, to date, computational models of gastric motility have made simplifying assumptions about the link between gastric electrophysiology and motility. Advances in experimental neuroscience mean that these assumptions can be reviewed, and detailed models of autonomic regulation can be incorporated into computational models. This review covers these advances, as well as a vision for the utility of computational models of gastric motility. Diseases of the nervous system, such as Parkinson's disease, can originate from the brain-gut axis and result in pathological gastric motility. Computational models are a valuable tool for understanding the mechanisms of disease and how treatment may affect gastric motility. This review also covers recent advances in experimental neuroscience that are fundamental to the development of physiology-driven computational models. A vision for the future of computational modeling of gastric motility is proposed and modeling approaches employed for existing mathematical models of autonomic regulation of other gastrointestinal organs and other organ systems are discussed.

Keywords: brain-gut axis; electromechanical modeling; electrophysiology; enteric nerves; gastroenterology; multi-scale modeling; vagus nerve.

Publication types

  • Review

Grants and funding

This work was supported by the Marsden Council Fund managed by the Royal Society Te Apārangi, NIH SPARC award (1OT2OD030538-01) and the Ministry of Business, Innovation and Employment’s Catalyst: Strategic fund. OA was supported by a University of Auckland Doctoral Scholarship.