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Computational Modeling of Glucose Uptake in the Enterocyte
Absorption of glucose across the epithelial cells of the small intestine is a key process in human nutrition and initiates signaling cascades that regulate metabolic homeostasis. Validated and predictive mathematical models of glucose transport in intestinal epithelial cells are essential for interp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473069/ https://www.ncbi.nlm.nih.gov/pubmed/31031632 http://dx.doi.org/10.3389/fphys.2019.00380 |
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author | Afshar, Nima Safaei, Soroush Nickerson, David P. Hunter, Peter J. Suresh, Vinod |
author_facet | Afshar, Nima Safaei, Soroush Nickerson, David P. Hunter, Peter J. Suresh, Vinod |
author_sort | Afshar, Nima |
collection | PubMed |
description | Absorption of glucose across the epithelial cells of the small intestine is a key process in human nutrition and initiates signaling cascades that regulate metabolic homeostasis. Validated and predictive mathematical models of glucose transport in intestinal epithelial cells are essential for interpreting experimental data, generating hypotheses, and understanding the contributions of and interactions between transport pathways. Here we report on the development of such a model that, in contrast to existing models, incorporates mechanistic descriptions of all relevant transport proteins and is implemented in the CellML framework. The model is validated against experimental and simulation data from the literature. It is then used to elucidate the relative contributions of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2) proteins in published measurements of glucose absorption from human intestinal epithelial cell lines. The model predicts that the contribution of SGLT1 dominates at low extracellular glucose concentrations (<20 mM) and short exposure times (<60 s) while the GLUT2 contribution is more significant at high glucose concentrations and long durations. Implementation in CellML permitted a modular structure in which the model was composed by reusing existing models of the individual transporters. The final structure also permits transparent changes of the model components and parameter values in order to facilitate model reuse, extension, and customization (for example, to simplify, or add complexity to specific transporter/pathway models, or reuse the model as a component of a larger framework) and carry out parameter sensitivity studies. |
format | Online Article Text |
id | pubmed-6473069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64730692019-04-26 Computational Modeling of Glucose Uptake in the Enterocyte Afshar, Nima Safaei, Soroush Nickerson, David P. Hunter, Peter J. Suresh, Vinod Front Physiol Physiology Absorption of glucose across the epithelial cells of the small intestine is a key process in human nutrition and initiates signaling cascades that regulate metabolic homeostasis. Validated and predictive mathematical models of glucose transport in intestinal epithelial cells are essential for interpreting experimental data, generating hypotheses, and understanding the contributions of and interactions between transport pathways. Here we report on the development of such a model that, in contrast to existing models, incorporates mechanistic descriptions of all relevant transport proteins and is implemented in the CellML framework. The model is validated against experimental and simulation data from the literature. It is then used to elucidate the relative contributions of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2) proteins in published measurements of glucose absorption from human intestinal epithelial cell lines. The model predicts that the contribution of SGLT1 dominates at low extracellular glucose concentrations (<20 mM) and short exposure times (<60 s) while the GLUT2 contribution is more significant at high glucose concentrations and long durations. Implementation in CellML permitted a modular structure in which the model was composed by reusing existing models of the individual transporters. The final structure also permits transparent changes of the model components and parameter values in order to facilitate model reuse, extension, and customization (for example, to simplify, or add complexity to specific transporter/pathway models, or reuse the model as a component of a larger framework) and carry out parameter sensitivity studies. Frontiers Media S.A. 2019-04-12 /pmc/articles/PMC6473069/ /pubmed/31031632 http://dx.doi.org/10.3389/fphys.2019.00380 Text en Copyright © 2019 Afshar, Safaei, Nickerson, Hunter and Suresh. http://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 | Physiology Afshar, Nima Safaei, Soroush Nickerson, David P. Hunter, Peter J. Suresh, Vinod Computational Modeling of Glucose Uptake in the Enterocyte |
title | Computational Modeling of Glucose Uptake in the Enterocyte |
title_full | Computational Modeling of Glucose Uptake in the Enterocyte |
title_fullStr | Computational Modeling of Glucose Uptake in the Enterocyte |
title_full_unstemmed | Computational Modeling of Glucose Uptake in the Enterocyte |
title_short | Computational Modeling of Glucose Uptake in the Enterocyte |
title_sort | computational modeling of glucose uptake in the enterocyte |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473069/ https://www.ncbi.nlm.nih.gov/pubmed/31031632 http://dx.doi.org/10.3389/fphys.2019.00380 |
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