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

Jie Zhang, Søren D. Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J. Sánchez, Zak Costello, Yu Chen, Michael J. Fero, Hector Garcia Martin, Jens Nielsen, Jay D. Keasling and Michael K. Jensen ()
Additional contact information
Jie Zhang: Technical University of Denmark, Kgs.
Søren D. Petersen: Technical University of Denmark, Kgs.
Tijana Radivojevic: Joint BioEnergy Institute
Andrés Ramirez: TeselaGen SpA
Andrés Pérez-Manríquez: TeselaGen SpA
Eduardo Abeliuk: TeselaGen Biotechnology
Benjamín J. Sánchez: Technical University of Denmark, Kgs.
Zak Costello: Joint BioEnergy Institute
Yu Chen: Chalmers University of Technology
Michael J. Fero: TeselaGen Biotechnology
Hector Garcia Martin: Joint BioEnergy Institute
Jens Nielsen: Technical University of Denmark, Kgs.
Jay D. Keasling: Technical University of Denmark, Kgs.
Michael K. Jensen: Technical University of Denmark, Kgs.

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-17910-1

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