  Biology has changed radically in the last two decades, transitioning from a
descriptive science into a design science. Synthetic biology allows us to
bioengineer cells to synthesize novel valuable molecules such as renewable
biofuels or anticancer drugs. However, traditional synthetic biology approaches
involve ad-hoc engineering practices, which lead to long development times.
Here, we present the Automated Recommendation Tool (ART), a tool that leverages
machine learning and probabilistic modeling techniques to guide synthetic
biology in a systematic fashion, without the need for a full mechanistic
understanding of the biological system. Using sampling-based optimization, ART
provides a set of recommended strains to be built in the next engineering
cycle, alongside probabilistic predictions of their production levels. We
demonstrate the capabilities of ART on simulated data sets, as well as
experimental data from real metabolic engineering projects producing renewable
biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we
discuss the limitations of this approach, and the practical consequences of the
underlying assumptions failing.
