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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,\n                    <jats:italic>e<\/jats:italic>\n                    .\n                    <jats:italic>g<\/jats:italic>\n                    . 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\u2019s sex, weight, height, as well as to the individual\u2019s historical data on metabolite dynamics. This tool enables an offline digital twin technology.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010469","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T13:46:41Z","timestamp":1662990401000},"page":"e1010469","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":23,"title":["Digital twin predicting diet response before and after long-term 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