Diminishing the turnaround time of DBTL cycles is a crucial aspect for accelerating the development of microbial chassis (31, 32). Cutting edge advances in fields like systems biology (33), machine learning (34), metabolomics (35), automation (31), or genome engineering (36-38) can contribute to accelerate one or more parts of the DBTL cycle. Biofoundries have been realized, in which automated and high-throughput DBTL cycles are integrated for rapidly and efficiently reprogramming cell factories (31, 39). While rapid and standardized DBTL cycles can be performed for model organisms like E. coli or S. cerevisiae, their use for non-model microorganisms is not trivial (15). For example, potentially interesting chassis might lack genetic accessibility, which would render the 'build' phase cumbersome. Also, the metabolic pathways supporting product formation could be unknown. Therefore, implementation of DBTL cycles in non-traditional microorganisms is an intriguing challenge for biotechnology. Succeeding in such a task would allow to investigate and assess non-traditional microorganisms as potential cell factories. Isoprenoid compounds: a successful example for the bioeconomy obtained via DBTL cycles The case of semi-synthetic artemisinin synthesis in yeast Probably the most scientifically relevant achievement obtained via DBTL cycles is the microbial synthesis of the antimalarial drug artemisinin (40). Such a compound is natively produced by the plant Artemisia annua, but dependence on yearly harvesting makes its production challenging (41). Therefore, alternatives like microbial synthesis were explored for feasible and costcompetitive artemisinin production (40, 41). This endeavour started with the implementation of a heterologous production pathway in E. coli (42), and was concluded a decade later by producing up to 25 g/L of artemisinic acid in Fld/Fd red Fld/Fd ox