We argue, that in order to unlock the full potential of synthetic biology (e.g., to find new antimicrobial compounds, to detect more efficient synthesis pathways), it is necessary to move from the well known model organisms into uncharted territories of non-model organisms, for which quantitative data is sparse. Several approaches allowing this shift have been recently proposed. On one hand, with new tools such as μbialSim, interpretations of the experimental meta-OMICS data focusing on metabolite exchange as the main interaction type allows simulation of hundreds of species of the gut microbiome . This method, although promising, is limited by the availability of huge data collections. Therefore another possible direction is to follow a modular approach, where complex metabolic networks are simplified to more manageable modules, based on their functionality . These modules can then be stored in a database, much like BioBricks [59], to provide a library of circuits to choose from. Such a coherent database with modules described in a standardised format could facilitate automatic creation of artificial communities. A proof of concept of how models can be automatically generated from a set of simple parts is provided by Karkaria et al. . With a set of simple assumptions regarding bacteriocin expression and sensitivity Bayesian methods are employed for model selection, and to identify most appropriate candidates which in combination lead to a stable two or three-strain community. A schematic of how such an organism-free modular process of computational modelling is provided in Figure .