Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
Journal article, 2020
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.
Author
J. Zhang
Technical University of Denmark (DTU)
Søren D. Petersen
Technical University of Denmark (DTU)
Tijana Radivojevic
DOE Agile BioFoundry
Lawrence Berkeley National Laboratory
Joint BioEnergy Institute, California
Andrés Ramirez
TeselaGen SpA
Andrés Pérez-Manríquez
TeselaGen SpA
Eduardo Abeliuk
TeselaGen Biotechnology, Inc.
Benjamín José Sánchez
Technical University of Denmark (DTU)
Zak Costello
DOE Agile BioFoundry
Joint BioEnergy Institute, California
Lawrence Berkeley National Laboratory
Yu Chen
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Michael J. Fero
TeselaGen Biotechnology, Inc.
Hector Garcia Martin
Joint BioEnergy Institute, California
DOE Agile BioFoundry
Lawrence Berkeley National Laboratory
Basque Center for Applied Mathematics (BCAM)
Jens B Nielsen
BioInnovation Institute
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Technical University of Denmark (DTU)
J.D. Keasling
Technical University of Denmark (DTU)
Shenzhen Institutes of Advanced Technologies
University of California
Joint BioEnergy Institute, California
Lawrence Berkeley National Laboratory
M. K. Jensen
Technical University of Denmark (DTU)
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 11 1 4880Predictive and Accelerated Metabolic Engineering Network (PAcMEN)
European Commission (EC) (EC/H2020/722287), 2016-09-01 -- 2020-08-30.
Subject Categories
Language Technology (Computational Linguistics)
Bioinformatics (Computational Biology)
Computer Science
DOI
10.1038/s41467-020-17910-1