Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
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...
Saved in:
Main Authors: | 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 |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2020
|
Subjects: | |
Online Access: | https://doaj.org/article/12b7d69682c04fc38cfe252ad2aedc1b |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A machine learning Automated Recommendation Tool for synthetic biology
by: Tijana Radivojević, et al.
Published: (2020) -
Tryptophan Side-Chain Oxidase Enzyme Suppresses Hepatocellular Carcinoma Growth through Degradation of Tryptophan
by: Yang Ai, et al.
Published: (2021) - International journal of tryptophan research IJTR.
-
Microbial tryptophan catabolites in health and disease
by: Henrik M. Roager, et al.
Published: (2018) -
A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
by: Frederik R Dalby, et al.
Published: (2021)