HYDRA-GUT will develop the mathematical foundations of a personalized digital twin of host–microbiota interactions. It will combine reduced-order modeling, scientific machine learning, data assimilation, and uncertainty quantification to connect a mechanistic multiscale colon model with sparse biological observations. The resulting framework will enable efficient subject-specific simulations, model updating, and reliable assessment of dietary, microbial, and inflammatory perturbations.