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

Pablo Carbonell, Adrian Jervis, Christopher Robinson, Cunyu Yan, Mark Dunstan, Neil Swainston, Maria Vinaixa, Katherine Hollywood, Andrew Currin, Nicholas Rattray, Sandra Taylor, Reynard Spiess, Rehana Sung, Alan R Williams, Donal Fellows, Natalie Stanford, Paul Mulherin, Rosalind Le Feuvre, Perdita Barran, Royston GoodacreNicholas Turner, Carole Goble, George Guoqiang Chen, Douglas Kell, Jason Micklefield, Rainer Breitling, Eriko Takano, Jean-Loup Faulon, Nigel Scrutton

Research output: Contribution to journalArticlepeer-review


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 (2S)-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.
Original languageEnglish
Article number66
JournalCommunications Biology
Early online date8 Jun 2018
Publication statusPublished - 2018

Research Beacons, Institutes and Platforms

  • Manchester Institute of Biotechnology


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