Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
Artikel i vetenskaplig tidskrift, 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.

Författare

J. Zhang

Danmarks Tekniske Universitet (DTU)

Søren D. Petersen

Danmarks Tekniske Universitet (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

Danmarks Tekniske Universitet (DTU)

Zak Costello

DOE Agile BioFoundry

Joint BioEnergy Institute, California

Lawrence Berkeley National Laboratory

Yu Chen

Chalmers, Biologi och bioteknik, Systembiologi

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, Biologi och bioteknik, Systembiologi

Danmarks Tekniske Universitet (DTU)

J.D. Keasling

Danmarks Tekniske Universitet (DTU)

Shenzhen Institutes of Advanced Technologies

University of California

Joint BioEnergy Institute, California

Lawrence Berkeley National Laboratory

M. K. Jensen

Danmarks Tekniske Universitet (DTU)

Nature Communications

2041-1723 (ISSN)

Vol. 11 1 4880

Predictive and Accelerated Metabolic Engineering Network (PAcMEN)

Europeiska kommissionen (EU), 2016-09-01 -- 2020-08-30.

Ämneskategorier

Språkteknologi (språkvetenskaplig databehandling)

Bioinformatik (beräkningsbiologi)

Datavetenskap (datalogi)

DOI

10.1038/s41467-020-17910-1

Mer information

Senast uppdaterat

2020-10-22