An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

• DOI: 10.1038/s42003-018-0076-9
• Publication Date: 2018-06-04T16:49:43Z
• Authors: Pablo Carbonell; Adrian J. Jervis; Christopher J. Ro...; Cunyu Yan; Mark Dunstan; Neil Swainston; Maria Vinaixa; Katherine A. Holl...; Andrew Currin; Nicholas J. W. Ra...; Sandra Taylor; Reynard Spiess; Rehana Sung; Alan R. Williams; Donal Fellows; Natalie J. Stanford; Paul Mulherin; Rosalind Le Feuvre; Perdita Barran; Royston Goodacre; Nicholas J. Turner; Carole Goble; George Guoqiang Chen; Douglas B. Kell; Jason Micklefield; Rainer Breitling; Eriko Takano; Jean-Loup Faulon; Nigel S. Scrutton
• Keywords: 570; 0303 health sciences; 03 medical and health sciences; ResearchInstitutes_Networks_Beacons/manchester_institute_of_biotechnology; name=Manchester Institute of Biotechnology; [SDV]Life Sciences [q-bio]; microbial production; fine chemicals; Microbiology; Article; 620; QR

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful growing catalog natural products and high-value chemicals. However, development at industrial levels been hindered by large resource investments required. Here we present an integrated Design-Build-Test-Learn (DBTL) pipeline for discovery optimization biosynthetic pathways, which is designed be compound agnostic automated throughout. We initially applied flavonoid (2S)-pinocembrin in Escherichia coli, demonstrate rapid iterative DBTL cycling with automation every stage. In this case, application two cycles successfully established pathway improved 500-fold, competitive titers up 88 mg L-1. further optimize alkaloids demonstrates how it could facilitate strains any chemical interest.