Nature Communications (Sep 2020)

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,
  • Michael K. Jensen

DOI
https://doi.org/10.1038/s41467-020-17910-1
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 13

Abstract

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In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker’s yeast.