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

Pablo Carbonell(University of Manchester), Adrian J. Jervis(University of Manchester), Christopher Robinson(University of Manchester), Cunyu Yan(University of Manchester), Mark S. Dunstan(University of Manchester), Neil Swainston(University of Manchester), María Vinaixa(University of Manchester), Katherine A. Hollywood(University of Manchester), Andrew Currin(University of Manchester), Nicholas J. W. Rattray(University of Manchester), Sandra Taylor(University of Manchester), Reynard Spiess(University of Manchester), Rehana Sung(University of Manchester), Alan Williams(University of Manchester), Donal Fellows(University of Manchester), Natalie Stanford(University of Manchester), Paul Mulherin(University of Manchester), Rosalind Le Feuvre(University of Manchester), Perdita E. Barran(Ollscoil na Gaillimhe – University of Galway), Royston Goodacre(Ollscoil na Gaillimhe – University of Galway), Nicholas J. Turner(Ollscoil na Gaillimhe – University of Galway), Carole Goble(University of Manchester), George Guo-Qiang Chen(University of Manchester), Douglas B. Kell(Ollscoil na Gaillimhe – University of Galway), Jason Micklefield(Ollscoil na Gaillimhe – University of Galway), Rainer Breitling(Ollscoil na Gaillimhe – University of Galway), Eriko Takano(Ollscoil na Gaillimhe – University of Galway), Jean‐Loup Faulon(Ollscoil na Gaillimhe – University of Galway), Nigel S. Scrutton(Ollscoil na Gaillimhe – University of Galway)
Communications Biology
June 4, 2018
10.1038/s42003-018-0076-9
Cited by 272Open Access
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Abstract

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

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