Real-Time In Silico Modeling of Postprandial Macronutrient Kinetics: A Validated Computational Engine for Nutrition Research and Digital Health
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918525017260032 |
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| author | Calderone, Alberto |
| author_facet | Calderone, Alberto |
| contents | Simulation of post-prandial pharmacokinetics, such as muscle protein synthesis (MPS) through mTORC1 and insulin-induced glucose uptake, is often challenging due to the computational intensity of the multi-compartmental approach. In this study, I introduce an in silico metabolic simulator that uses bi-compartmental Bateman kinetic processes, gamma-variate distributions, and finite state machine reasoning to solve temporal differential equations instantaneously, generating metabolic curves and predictions depending on input meals. The novel underlying algorithm was custom-built entirely independent of third-party libraries or external services. This original computational engine, bridging the gap between academia and the digital health sector, is integrated within a web dashboard and provided as a service via REST APIs. The average response time is approximately 135 ms with a maximum below 750 ms. The multi-dimensional model was calibrated using a Landmark Validation approach across diverse dietary conditions (Whey Protein, mixed meal, OGTT) and optimized via Grid Search. Ultimately, the system achieved a global physiologically optimal Mean Absolute Percentage Error (MAPE) of $\sim18\%$ while maintaining an algorithmic complexity of $O(n \log n)$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27459 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time In Silico Modeling of Postprandial Macronutrient Kinetics: A Validated Computational Engine for Nutrition Research and Digital Health Calderone, Alberto Other Quantitative Biology Computational Engineering, Finance, and Science 92C45, 68U20 I.6.3; I.6.5; D.2.11; J.3 Simulation of post-prandial pharmacokinetics, such as muscle protein synthesis (MPS) through mTORC1 and insulin-induced glucose uptake, is often challenging due to the computational intensity of the multi-compartmental approach. In this study, I introduce an in silico metabolic simulator that uses bi-compartmental Bateman kinetic processes, gamma-variate distributions, and finite state machine reasoning to solve temporal differential equations instantaneously, generating metabolic curves and predictions depending on input meals. The novel underlying algorithm was custom-built entirely independent of third-party libraries or external services. This original computational engine, bridging the gap between academia and the digital health sector, is integrated within a web dashboard and provided as a service via REST APIs. The average response time is approximately 135 ms with a maximum below 750 ms. The multi-dimensional model was calibrated using a Landmark Validation approach across diverse dietary conditions (Whey Protein, mixed meal, OGTT) and optimized via Grid Search. Ultimately, the system achieved a global physiologically optimal Mean Absolute Percentage Error (MAPE) of $\sim18\%$ while maintaining an algorithmic complexity of $O(n \log n)$. |
| title | Real-Time In Silico Modeling of Postprandial Macronutrient Kinetics: A Validated Computational Engine for Nutrition Research and Digital Health |
| topic | Other Quantitative Biology Computational Engineering, Finance, and Science 92C45, 68U20 I.6.3; I.6.5; D.2.11; J.3 |
| url | https://arxiv.org/abs/2605.27459 |