In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2022-9-12), p. e1010469-
Abstract:
Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e . g . hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual’s sex, weight, height, as well as to the individual’s historical data on metabolite dynamics. This tool enables an offline digital twin technology.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010469
DOI:
10.1371/journal.pcbi.1010469.g001
DOI:
10.1371/journal.pcbi.1010469.g002
DOI:
10.1371/journal.pcbi.1010469.g003
DOI:
10.1371/journal.pcbi.1010469.g004
DOI:
10.1371/journal.pcbi.1010469.g005
DOI:
10.1371/journal.pcbi.1010469.g006
DOI:
10.1371/journal.pcbi.1010469.g007
DOI:
10.1371/journal.pcbi.1010469.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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