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

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

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145 Citations (Scopus)
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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
Number of pages10
JournalCommunications Biology
Publication statusPublished - 8 Jun 2018


  • microbial production
  • Design–Build-Test–Learn (DBTL) pipeline
  • biosynthetic pathways


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