Until a more fundamental understanding about the organisational principles of living systems is established, the dilemma that we are dealing with highly complex systems, for which we did not even establish a solid theoretical foundation, leaves us with a limited number of practical solutions trading-off between controllability and complexity . One current option is to reduce the complexity of our models so that we can understand and systematically test all assumptions that enter the model. This way, we will gain control, and thus understanding, over the system under investigation at the expense of detailed quantitative predictability. Such simple models are extremely useful and have also been successfully employed to design synthetic systems (see genetic toggle switch ). Alternatively, we can assemble highly complex and extremely detailed models and feed these with the enormous amount of experimental data available, copying the digital twin (DT) approach. In industry, DTs are virtual accurate representations of physical objects assembled from numerous digital components, providing cost effective alternatives to physical objects . This approach trades control and understanding in favour of predictability. Conceptually they follow the machine learning and artificial intelligence approaches, by which highly complex models are automatically constructed by algorithms which reproduce the data very well. Although they prove to be extremely useful in making quantitative predictions, and are currently developed for many biofoundries , due to their complexity and specificity, it is nearly impossible to infer common and universal principles that will hold true for other biological systems.