Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

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.

Petersen, Søren D.Danmarks Tekniske Universitet,Technical University of Denmark (author)
Radivojevic, TijanaLawrence Berkeley National Laboratory,Joint BioEnergy Institute, California (author)
Ramirez, Andrés (author)
Pérez-Manríquez, Andrés (author)
Abeliuk, Eduardo (author)
Sánchez, Benjamín José,1988Danmarks Tekniske Universitet,Technical University of Denmark(Swepub:cth)bensan (author)
Costello, ZakJoint BioEnergy Institute, California,Lawrence Berkeley National Laboratory (author)
Chen, Yu,1990Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)cheyu (author)
Fero, Michael J. (author)
Martin, Hector GarciaJoint BioEnergy Institute, California,Lawrence Berkeley National Laboratory,Basque Center for Applied Mathematics (BCAM) (author)
Nielsen, Jens B,1962BioInnovation Institute (BII),Chalmers tekniska högskola,Chalmers University of Technology,Danmarks Tekniske Universitet,Technical University of Denmark(Swepub:cth)nielsenj (author)
Keasling, J.D.Danmarks Tekniske Universitet,Technical University of Denmark,Shenzhen Institutes of Advanced Technologies,University of California,Joint BioEnergy Institute, California,Lawrence Berkeley National Laboratory (author)
Jensen, M. K.Danmarks Tekniske Universitet,Technical University of Denmark (author)
Danmarks Tekniske UniversitetLawrence Berkeley National Laboratory (creator_code:org_t)

In:Nature Communications: Springer Science and Business Media LLC11:12041-17232041-1723

By the author/editor: Zhang, J.; Petersen, Søren ...; Radivojevic, Tij ...; Ramirez, Andrés; Pérez-Manríquez, ...; Abeliuk, Eduardo; show more...; Sánchez, Benjamí ...; Costello, Zak; Chen, Yu, 1990; Fero, Michael J.; Martin, Hector G ...; Nielsen, Jens B, ...; Keasling, J.D.; Jensen, M. K.; show less...

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Language Technol ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

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