While the primary role of metabolism is chemical conversions, can it also serve as an information processing device? To answer this question, we propose to encode various microbial metabolic models into Artificial Metabolic Networks (AMNs), which can be trained on experimental data or model simulations. Unlike “black box” models, our AMNs will reflect faithfully the structure and dynamics of metabolic networks. Our AMNs will be benchmarked on classical machine learning problems to assess what level of computational sophistication metabolism is able to handle. In the context of biotechnology, our AMNs will be applied to (i) design experiments to optimize bioprocesses for nutrient compositions that maximize the production of added value chemicals in engineered bacterial strains and to (ii) engineer the metabolism of an E. coli reporter strain to classify clinical samples containing metabolic biomarkers of infectious diseases.