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

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%, respectiv

SUBMITTER: Zhang J 

PROVIDER: S-EPMC7519671 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature