Mathematical and computational biology are consequently becoming an integral part of studies of biological systems across many scales . For example, they provide quantitative methods to test hypotheses in a rigorous manner, combined with currently available large data-set collections, they integrate and systematise information in the form of interconnected knowledge hubs, and provide guidance for experimental endeavours. As the complexity of the designs increases, we need to use modelling to guide the first steps in the design phase of the first cycle, being able to guide the choice of molecular modules, guide the assembly and make predictions on the functionality and performance of the devices to be made . In recent years, great hope has been put in modelling efforts to guide synthetic biology approaches in much the same way as they guided engineers to design new technical devices . However, to make predictive models we need a thorough quantitative understanding of the parts in order to combine them, designing more complex systems. Models should also suggest experiments to test prototypes, and then help understand what is needed for their improvement. This approach is most advanced for engineering metabolic networks, because databases with quantitative data on enzymatic activities and functionalities are existing. In contrast, such quantitative information is not widely available for signalling or structural components, and quantitative datasets become even scarcer with regard to synthetic microbial communities (Figure ).