Throughout evolution, bacteria have acquired the ability to sense variations of the concentrations of nutrients in their growth medium. According to the medium composition, they adapt their metabolic behaviour by activating or repressing the appropriate metabolic pathways through a wide array of regulation mechanisms including transcriptional, translational or post-translational responses. Bacterial metabolism can therefore be compared to an algorithm taking as inputs the media composition and yielding as outputs metabolic fluxes describing its metabolic phenotype. Our work focuses on this perspective of the bacterial metabolism as an information processing unit. Our objective is to demonstrate that E. coli’s metabolism is capable of neural-like computation and to assess to what extend it can solve classical machine-learning problems (whether regression or classification). Our first step has been to generate an accurate model of E. coli’s metabolism, using the AMN (Artificial Metabolic Network), a metabolic hybrid model previously developed in our lab. This model has then been used to solve machinelearning problems of different complexities in order to assess the capacity of our metabolic model.