Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding. The metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic interdependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function, and stability of microbial communities. In this talk, we couple a resource allocation model into a population dynamics model to evaluate the effect of a range of amino acid concentrations and population structures on the susceptibility profile to different antibiotics. We validate our theoretical predictions using a synthetic model system consisting of Escherichia coli K12 strains with amino acid auxotrophies and metabolic interdependencies. Both our computational and experimental approaches conclude that drug susceptibility is contingent upon the interaction profile exhibited by microbial communities.