The understanding of the interaction between the human gut microbiota and diet remains one major challenge in biomedicine. In particular, the human gut microbiota degrades nutrients derived from the diet and releases key bioactive compounds that exert a strong influence on host health. Due to the high interindividual variation of the microbial composition in the human gut, personalized dietary recommendations are mandatory to efficiently promote human health. To address this complex challenge, holistic approaches that integrate experimental and computational tools have been developed in the last years. In this context, genome-scale metabolic models (GEMs) and constraint-based modeling (CBM) approaches are promising tools to widely explore the metabolic capabilities of gut microbial communities and develop personalized nutrition applications. In this chapter, we first present a brief background of GEMs and CBM, including their main concepts, assumptions, and basic mathematical models. Then, we introduce key databases and computational platforms to generate gut microbial community GEMs (co-GEMs), as well as the most relevant mathematical methods to study and explore them, including steady-state and dynamic approaches. Finally, we review seminal works that highlight the relevance of co-GEMs and CBM methods towards advancing personalized nutrition, delineating key challenges to be addressed in the coming years.