Constraint-based modeling of tissue-specific metabolism for circulating nutrients

Abstract

Metabolism is a complex network of biochemical reactions essential for maintaining physiological homeostasis and enabling adaptive responses to environmental changes. While traditional metabolomics techniques like LC-MS, GC-MS, and NMR provide static snapshots of metabolite abundances, they fail to capture the dynamic fluxes that reflect true metabolic activity. To address this gap, we present a minimal whole-body metabolic model that integrates thermodynamic constraints and tissue-specific data to reconstruct systemic metabolic fluxes. Our approach bridges the strengths of genome-scale models and curated networks by defining 64 core anabolic and catabolic tasks and extracting minimal reaction subnetworks from the RECON3D model using optimization algorithms. These tasks are then mapped to 12 key organs - including liver, muscle, adipose tissue, kidney, and brain - to create tissue-specific models, which are subsequently merged through defined circulatory compartments to simulate whole-body metabolism. We validate our model through flux variability and sampling analyses under fasted physiological conditions, demonstrating its ability to capture tissue-level metabolism and cross-tissue interactions. This framework provides a scalable, interpretable, and physiologically grounded platform for exploring systemic metabolic regulation, with broad applications in precision medicine and metabolic disease research.