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” artificial neural networks, our AMNs will be sparse and 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 the design of experiments to (i) optimize the productivity of an added-value chemical (lycopene) E. coli producing strain defining nutrient compositions and gene deletions and (ii) classify infectious disease severity by engineering an E. coli biosensing strain detecting metabolic biomarkers in COVID-19 clinical samples.