Efficient learning in metabolic pathway designs through optimal assembling

Engineering biology is a key enabling technology at the forefront of the new industrial bioeconomy. Rapid prototyping for bio-based production of chemicals and materials in the new biofoundries faces the challenge of dealing with increasingly complex libraries of genetic circuits consisting of multiple gene variants from different sources and with different translational tuning, along with multiple promoter libraries, different vector copy number, resistance cassette, or host strain. In order to streamline the biomanufacturing pipeline, smart design rules are necessary to find the trade-offs between experimental design and predictive strain modeling for synthetic biology production of chemicals. Here, we explore the Pareto surface spanned by the optimal experimental design space of combinatorial libraries that are found in a large-scale diverse set of genetic circuits and plasmid vectors, and learning efficiency of their associated metabolic pathway dynamics. Engineering rules for metabolic pathway design are validated by these means, suggesting optimal synthetic biology design approaches for biomanufacturing pipelines.