Determining the conditions for culturing microorganisms is a difficult and recurring problem in microbiology, as it requires identifying nutritional requirements and characterising metabolism. \textit{Genome-scale metabolic networks} (GSMNs) obtained from genomic data enable simulations of the metabolic potential from a predefined environment. We developed methods that solve the inverse problem: predicting nutrient sources, or \textit{seeds}, from a GSMN and a metabolic objective. The methods propose hybrid models, combining a discrete and iterative Boolean approximation of metabolic activity with numerical flux balance analysis (FBA). Applied at the scale of an individual population on GSMNs, the logic modelling method is a good approximation of flux balance constraints. The problem was then extended to communities of microorganisms. At this scale, it is necessary to consider possible transfers between networks, which increases the combinatorial complexity of the problem. We hypothesise that it is relevant to identify in priority minimal sets of nutrients that ensure the functionality of the metabolic network, which led us to consider prioritising optimisations on sets, first seeking to minimise seeds, then transfers. Three algorithms were developed, two of which ensure subset minimality. The application of methods to small communities of network reveals the combinatorial complexity, but also the complementarity of the algorithms.