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

Carbonell, Pablo and Jervis, Adrian J. and Robinson, Christopher J. and Yan, Cunyu and Dunstan, Mark and Swainston, Neil and Vinaxia, Maria and Hollywood, Katherine A. and Currin, Andrew and Rattray, Nicholas J.W. and Taylor, Sandra and Speiss, Reynard and Sung, Rehana and Williams, Alan R. and Fellows, Donal and Stanford, Natalie J. and Mulherin, Paul and Le Feuvre, Rosalind and Barran, Perdita and Goodacre, Royston and Turner, Nicholas J. and Goble, Carole and Chen, George Guoqiang and Kell, Douglas B. and Mickelfield, Jason and Breitling, Reiner and Takano, Eriko and Faulon, Jean-Loup and Scrutton, Nigel S. (2018) An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1. 66. ISSN 2399-3642

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    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 (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.