2026/05/25 by Alberto Calderone · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Bridging (networking) #Computational complexity theory #Computational model #Diet and metabolism studies #Emulation #In silico #Muscle metabolism and nutrition #Nutrition, Genetics, and Disease #Postprandial #cs.CE #q-bio.OT
paper · pdf · doi:10.48550/arxiv.2605.27459
openalex publication_date 2026/05/25 · arxiv published 2026/05/25 · arxiv updated 2026/05/25 · openalex created_date 2026/05/29 · openalex updated_date 2026/07/28
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 ∼18% while maintaining an algorithmic complexity of O(n log n).