Available online 1 February 2023

The design-build-test-learn cycle (DBTLc) of synthetic biology can be automated.

Combining automation and machine learning guides enhanced DTBLc performance.

Recent advances in automating the DBTLc are discussed.

Biofoundries will host the creation of next-generation cell factories.

Multi-omic analyses drive the process towards optimal bacterial factories.

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

design-build-test-learn cycle

metabolic flux analysis

artificial intelligence

multiplex automated genome engineering

uracil-specific excision reagent

selected- and multiple-reaction monitoring

data dependent analysis

data independent analysis

flow-injection analysis

sequential window acquisition of all theoretical mass spectra

high resolution mass spectrometry

genome-scale metabolic model

constraint-based reconstruction and analysis

thermodynamics-based FBA

flux variability analysis

mass distribution vectors

elementary metabolite units

support vector machines

variational autoencoder

generative adversarial network

graph neural networks

physics-informed neural networks

tree-based pipeline optimization tool

Synthetic biology

Biofoundry

DBTL cycle

Automation

Machine learning

Metabolic engineering

Synthetic metabolism

Bacteria

© 2023 The Author(s). Published by Elsevier B.V.