Rising as a key technology for the new bioeconomy, the bio-based engineering of specialty and commodity compounds offers an alternative to the chemical industry, through products sourced from nature. Green, sustainable bioplastics, drug manufacturing and renewable energy sources has becoming today’s biotechnology. In the transition from an emerging technology into a truly biomanufacturing technology, accelerated reductions of the time from concept to development to scale-up are essential. Early proof of concepts took over 10 years in their making. We cannot afford such latencies anymore. Modern manufacturing biofoundries have adopted an agile approach in order to improve their Design-Build-Test-Learn cycle efficiency. Synthetic biology is becoming in that way intimately intertwined with automation and machine learning technologies. Biofactories delivering at full steam face major design challenges in their metabolic pathway designs. To that end, modern manufacturing should provide agile solutions based on continuous upgrades through team interactions. Therefore, rather than keeping a fixed set of design recipes, optimal synbio design provides a flexible strategy.

We provide solutions based on optimal experimental model-free and model-based synbio design, which allows a good trade-off between experimental capabilities and design space. Design factors can cover a wide range of bottom-up elements such as genetic parts like promoters, gene coding sequences, ribosome binding sites, vector copy number, etc., as well as assembly spatial arrangements, chassis strains or experimental conditions. Such designs are generated in an automated way that provides the list of genetic parts to be synthetized. Following the cloud biomanufacturing paradigm, the automated build and test stages proceed through robotic platforms allowing full sample traceability so that experimental results are fed back into the learn and design engines. Statistical analytics are then applied in order to infer an ensemble of models for the design factor-response relationships under either mechanistic hypothesis, i.e., kinetic models, or using model-free approaches, i.e., machine learning. This fully automated design workflow complies with the flexibility requirements of agile design and is therefore poised to become widely adopted in future biomanufacturing key systems of the circular bioeconomy.