Hybrid Digital–Chemical Intelligence for Engineering Gut–Immune Cellular Networks

The gut microbiome forms one of the most intricate programmable microenvironments in the human body, where nutrient-dependent metabolic fluxes and microbial interactions generate signals that modulate epithelial barrier function, innate immunity, and T-cell differentiation. These immune-relevant metabolites e.g. short-chain fatty acids constitute a biochemical language linking microbial ecology to host physiology. This PhD will investigate and engineer this communication landscape using programmable bioreactors that precisely control nutrient dynamics, redox conditions, and ecological perturbations. Multi-omics profiling will map how microbial communities reorganise and alter metabolic signalling under defined conditions. Building on our AI-guided liquid-phase molecular communication (MIMIC) framework, metabolite patterns will be treated as semantic messages; machine-learning models will decode these signatures, infer ecological context, and predict epithelial or immune-relevant outcomes using mechanistic priors and existing datasets. Conditioned-media assays on epithelial or immune cells will provide experimental validation. This hybrid digital–chemical platform will underpin next-generation ecological and AI-augmented microbial therapeutics.