Context-Specific Genome-Scale Metabolic Modeling to Unravel the Metabolic Rewiring in Human Diseases

Disruptions in cellular metabolism contributes to the development and progression of various diseases. A comprehensive understanding of these metabolic alterations is essential for developing targeted therapeutic interventions that can address the underlying disease mechanisms. Although the advancements in multi-omics research offers an overview of metabolic reprogramming in diseased conditions, they often fall short of precisely characterizing the intricate metabolic alterations and their functional implications in human health. Genome-scale metabolic models (GEMs) has emerged as an advanced in silico framework for deciphering cellular metabolic activities. Integrating multi-omics data in human GEMs enables the reconstruction of context-specific models, offering a more precise representation of metabolic rewiring in diseased individuals compared to healthy counterparts. Here, we review recent advances in the reconstruction of context-specific genome-scale metabolic models, highlighting their role in studying metabolic alterations across various human diseases such as cancer, diabetes, Parkinson's, Alzheimer's, and nonalcoholic fatty liver. Those context-specific GEMs have facilitated identification of metabolic vulnerabilities, the prediction of novel drug targets, and the assessment of therapeutic interventions. Advancements in model reconstruction algorithms and the assurance of experimental validation will be crucial for unlocking the full potential of context-specific GEMs in understanding complex metabolic diseases and developing targeted therapeutic strategies.