APPLICATIONS OF TECHNOLOGY:
Companies performing synthetic biology/metabolic engineering
- Unique uncertainty quantification approach
- High predictive accuracy
- Shorter development times
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.
DEVELOPMENT STAGE: Proven principle
STATUS: Patent pending. Available for licensing or collaborative research.