Data from: Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb
microbial metabolic networks, yet bioproduction campaigns often struggle
with parsing complex metabolic datasets to efficiently enhance product
titers. We address this challenge by coupling laboratory automation with
machine learning to systematically optimize the production of isoprenol, a
sustainable aviation fuel (SAF) precursor, in Pseudomonas putida. The
simultaneous downregulation through CRISPR interference of combinations of
up to four gene targets, guided by machine learning (ML), permitted us to
increase isoprenol titer 5-fold in six consecutive DBTL cycles. Moreover,
ML enabled us to swiftly explore a vast experimental design space of
800,000 possible combinations by strategically recommending approximately
400 priority constructs. High-throughput proteomics allowed us to validate
CRISPRi downregulation and identify biological mechanisms driving
production increases. Our work demonstrates that ML-driven automated DBTL
cycles can rapidly enhance titers without specific biological knowledge,
suggesting that it can be applied to any host, product, or
pathway.