A machine learning Automated Recommendation Tool for synthetic biology.
Nat Commun
; 11(1): 4879, 2020 09 25.
Article
in En
| MEDLINE
| ID: mdl-32978379
ABSTRACT
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, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Synthetic Biology
/
Metabolic Engineering
/
Machine Learning
Type of study:
Guideline
/
Prognostic_studies
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2020
Document type:
Article
Affiliation country:
United States