Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in <em>Pseudomonas putida</em>

Synthetic and systems biology now produces vast combinatorial designs, but high-throughput analytical methods are poorly matched to interrogate this search space. We addressed this challenge with a biosensor-driven strategy in Pseudomonas putida to enhance isoprenol production, a key precursor for an advanced aviation fuel. Our biosensor leverages the native response of P. putida to short-chain alcohols, enabling a conditional growth-based selection that identified competing cellular processes as targets to improve isoprenol production. An iterative and combinatorial strain engineering approach yielded a 36-fold increase in isoprenol production (~900 mg/L). Ensemble -omics analysis revealed key causal metabolic rewiring that enhanced production. Techno-economic analysis provided an economic viability context and confirmed that the benefits of adding amino acid supplements outweigh the additional costs. This study establishes a modular and broadly applicable biosensor-driven approach for optimizing heterologous pathways, advancing the science of microbial bioproduction, and driving sustainable bioproducts development for a resilient economy. This companion dataset contains the several raw datasets generated from this study that are not uploaded in specific repositories.