Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering

Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems. [Display omitted] •Bioconversion of 77% max theoretical yield of indigoidine from para-coumarate•Rational engineering strategies are aided by computational modeling and proteomics data•Adaptive lab evolution restores growth in initial computed strain design Eng et al. describe how large omics datasets and computational modeling can guide microbial strain design for highly efficient bioprocesses using renewable carbon streams. These methods are applicable to challenges with other microbes, carbon streams, and bioproducts.