TY - JOUR
T1 - An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals
AU - Carbonell, Pablo
AU - Jervis, Adrian J.
AU - Robinson, Christopher J.
AU - Yan, Cunyu
AU - Dunstan, Mark
AU - Swainston, Neil
AU - Vinaxia, Maria
AU - Hollywood, Katherine A.
AU - Currin, Andrew
AU - Rattray, Nicholas J.W.
AU - Taylor, Sandra
AU - Speiss, Reynard
AU - Sung, Rehana
AU - Williams, Alan R.
AU - Fellows, Donal
AU - Stanford, Natalie J.
AU - Mulherin, Paul
AU - Le Feuvre, Rosalind
AU - Barran, Perdita
AU - Goodacre, Royston
AU - Turner, Nicholas J.
AU - Goble, Carole
AU - Chen, George Guoqiang
AU - Kell, Douglas B.
AU - Mickelfield, Jason
AU - Breitling, Reiner
AU - Takano, Eriko
AU - Faulon, Jean-Loup
AU - Scrutton, Nigel S.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - 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.
AB - 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.
KW - microbial production
KW - Design–Build-Test–Learn (DBTL) pipeline
KW - biosynthetic pathways
UR - https://www.nature.com/commsbio/
U2 - 10.1038/s42003-018-0076-9
DO - 10.1038/s42003-018-0076-9
M3 - Article
VL - 1
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
M1 - 66
ER -