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Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions
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 gastri...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050371/ https://www.ncbi.nlm.nih.gov/pubmed/37008202 http://dx.doi.org/10.3389/fnins.2023.1146097 |
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author | Athavale, Omkar N. Avci, Recep Cheng, Leo K. Du, Peng |
author_facet | Athavale, Omkar N. Avci, Recep Cheng, Leo K. Du, Peng |
author_sort | Athavale, Omkar N. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10050371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100503712023-03-30 Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions Athavale, Omkar N. Avci, Recep Cheng, Leo K. Du, Peng Front Neurosci Neuroscience 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. Frontiers Media S.A. 2023-03-15 /pmc/articles/PMC10050371/ /pubmed/37008202 http://dx.doi.org/10.3389/fnins.2023.1146097 Text en Copyright © 2023 Athavale, Avci, Cheng and Du. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Athavale, Omkar N. Avci, Recep Cheng, Leo K. Du, Peng Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title | Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title_full | Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title_fullStr | Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title_full_unstemmed | Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title_short | Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions |
title_sort | computational models of autonomic regulation in gastric motility: progress, challenges, and future directions |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050371/ https://www.ncbi.nlm.nih.gov/pubmed/37008202 http://dx.doi.org/10.3389/fnins.2023.1146097 |
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