The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
Open accessAbstract
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
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Cited by
Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme
d’Oelsnitz, S., Diaz, D. J., Kim, W., Acosta, D. J., Dangerfield, T. L., Schechter, M. W., Minus, M. B., Howard, J. R., Do, H., Loy, J. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46356-y · Cited by 58
Machine learning predicts system-wide metabolic flux control in cyanobacteria
Kugler, A., & Stensjö, K. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.02.013 · Cited by 31
Metadata
- Authors
- Zhang, J., Petersen, S. D., Radivojevic, T., Ramirez, A., Pérez-Manríquez, A., Abeliuk, E., Sánchez, B. J., Costello, Z., Chen, Y., Fero, M. J., Martin, H. G., Nielsen, J., Keasling, J. D., & Jensen, M. K.
- Venue
- Nature Communications
- Published
- 2020-09-25
- Volume
- 11
- Issue
- 1
- Pages
- 4880-4880
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3088063380
- Cited by
- 282
- References
- 72
- FWCI
- 9.96
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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