Background As the central organ of metabolic regulation, the liver undergoes significant metabolic network remodeling and functional disruption during disease states, yet the underlying mechanisms remain insufficiently elucidated. The advancement of multi-omics data integration and computational modeling offers new research avenues and theoretical support for precise reconstruction of metabolic networks and in-depth exploration of pathogenic mechanisms.

Objective To construct a computational model of liver metabolic pathways and optimize its parameters through multi-omics data integration, enabling quantitative characterization of the transition from healthy to diseased states, thereby elucidating the remodeling mechanisms of liver disease metabolic networks and identifying critical regulatory nodes and potential therapeutic targets.

Methods A computational model encompassing major pathways including glycolysis, TCA cycle, fatty acid metabolism, and amino acid metabolism was established firstly; then transcriptomic, proteomic, and metabolomic data from healthy controls and patients with three types of liver diseases were optimized; finally the model performance was evaluated through internal cross-validation and external validation with an independent cohort, and key regulatory nodes was identified through parameter sensitivity analysis and metabolic control analysis.

Results The optimized model accurately predicted metabolic features of the three liver diseases, with correlation coefficients between predicted and experimental metabolite concentrations exceeding 0.85. The study revealed common metabolic reprogramming features in liver diseases: shift from aerobic oxidation to glycolysis, from fatty acid oxidation to synthesis, and amino acid metabolism disorders. Fifteen key regulatory nodes were identified, including three common nodes (hexokinase, pyruvate dehydrogenase, fatty acid synthase) and twelve disease-specific nodes. A liver disease classification and staging algorithm developed based on these findings achieved a classification accuracy of 92.5% in the external validation cohort, significantly outperforming traditional methods (76.3%).

Conclusion This study has achieved parameter optimization of a computational model for liver metabolic pathways based on multi-omics data, quantitatively characterized metabolic features in healthy and disease states, revealed mechanisms of metabolic network reconstruction, and identified key regulatory nodes, providing a computational foundation for precision diagnosis and personalized treatment of liver diseases, while offering new insights for systems biology-based drug development.