APPLICATIONS:
Companies performing synthetic biology/metabolic engineering
ADVANTAGES:
- Demonstrated high predictive accuracy
- Shorter strain development times
TECHNOLOGY OVERVIEW:
Researchers at Berkeley Lab’s Joint BioEnergy Institute and the Agile BioFoundry have developed a tool that uses machine learning to make further advancements in the field of synthetic biology. The patent-pending Automated Recommendation Tool (ART) uses probabilistic modeling techniques to guide metabolic engineering systematically without requiring 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. ART is built around a unique uncertainty quantification approach and has been demonstrated to have high predictive accuracy. Using ART improved tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training.
FOR MORE INFORMATION:
Zhang, J., Petersen, S.D., Radivojevic, T. et al. “Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.” Nat Commun 11, 4880 (2020). https://doi.org/10.1038/s41467-020-17910-1
Radivojević, T., Costello, Z., Workman, K. Martin, H.G. (2020) “A Machine Learning Automated Recommendation Tool for Synthetic Biology.” Nature Communications 11, 4879 doi: 10.1038/s41467-020-18008-4
STATUS: Patent pending.
OPPORTUNITIES: Available for licensing (free for academia, fee for commercial use).
