Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering-Reference-Cited by-同舟云学术

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Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Published:2023-08-24 Issue:9 Volume:12 Page:2588-2599
ISSN:2161-5063
Container-title:ACS Synthetic Biology
language:en
Short-container-title:ACS Synth. Biol.

Author:

van Lent Paul¹^ORCID,Schmitz Joep²,Abeel Thomas¹³^ORCID

Affiliation:

1. Delft Bioinformatics Lab, Delft University of Technology Van Mourik, Delft 2628 XE, The Netherlands

2. Department of Science and Research, Joep Schmitz - dsm-firmenich, Science & Research, P.O. Box 1, 2600 MA Delft, The Netherlands

3. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States

Funder

Firmenich

DSM

Publisher

American Chemical Society (ACS)

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Biomedical Engineering,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acssynbio.3c00186

Reference56 articles.

1. Machine learning applications in systems metabolic engineering

2. Systems Metabolic Engineering Strategies: Integrating Systems and Synthetic Biology with Metabolic Engineering

3. Staphanopoulos, G.; Aristidou, A. A.; Nielsen, J.Metabolic Engineering: Principles and Methodologies; Elsevier Science, 1998; pp 205–273.

4. Inverse metabolic engineering: A strategy for directed genetic engineering of useful phenotypes

5. Integrating Biological Redesign: Where Synthetic Biology Came From and Where It Needs to Go

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