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

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.

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

“…BASIC provides this high accuracy and efficiency through long linker overhangs, guiding the assembly of linker-ligated parts. Currently, BASIC and alternative DNA assembly methods have only been automated using expensive infrastructure, limiting community access to the benefits automated DNA assembly brings to research and applications in biology 6,[8][9][10][11] .…”

Large scale active-learning-guided exploration for in vitro protein production optimization

“…Active learning is now being used in many fields related to biology including medicinal chemistry 18 or structural biology 19 . In the context of bioproduction optimization, Design of Experiment (DoE) methods are generally preferred over active machine learning because the training set sizes on which learning is performed are rather limited 20,21 . Because cell-free systems enable one to generate large amount of data in a short time span, we explore here the use of an active machine learning strategy to optimize and understand the impact of cell-free buffer compositions on protein production in cell-free systems.…”

Organizing genome engineering for the gigabase scale

“…Gigabase engineering will likely require large-scale integration of efforts for the division, distribution, and coordination of labor and materials. Both genome engineering and smaller-scale organism engineering workflows have often been abstracted and organized in terms of design-build-test-learn cycles [3,18,[21][22][23][24][25][26]. This iterative approach is helpful given the complexity and uncertainties in engineering biology, progressing in incremental steps, implementing genetic modifications in stages, and adjusting designs based on information learned from testing prototypes and partial implementations.…”