The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Machine learning applications in systems metabolic engineering
Abstract
Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.
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Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Strategies for Fermentation Medium Optimization: An In-Depth Review
Singh, V., Haque, S., Niwas, R., Srivastava, A., Pasupuleti, M., & Tripathi, C. K. M. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.02087 · Cited by 629
End-to-End Differentiable Learning of Protein Structure
AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371
In silico method for modelling metabolism and gene product expression at genome scale
Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., et al. (2012) · Nature Communications · doi:10.1038/ncomms1928 · Cited by 266
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Digital-to-biological converter for on-demand production of biologics.
Boles, K. S., Kannan, K., Gill, J., Felderman, M., Gouvis, H., Hubby, B., Kamrud, K. I., Venter, J. C., & Gibson, D. G. (2017) · Nature Biotechnology · doi:10.1038/nbt.3859 · Cited by 80
Cited by
Design of synthetic promoters for cyanobacteria with generative deep-learning model
Seo, E., Choi, Y.-N., Shin, Y. R., Kim, D., & Lee, J. W. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad451 · Cited by 61
Mathematical modeling of proteome constraints within metabolism
Chen, Y., & Nielsen, J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.03.003 · Cited by 44
Pepe, M., Hesami, M., de la Cerda, K. A., Perreault, M. L., Hsiang, T., & Jones, A. M. P. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108247 · Cited by 36
Metadata
- Authors
- Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y.
- Venue
- Current Opinion in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 64
- Pages
- 1-9
- ISSN
- 0958-1669 (ISSN-L)
- OpenAlex
- W2994115888
- Cited by
- 192
- References
- 7
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- closed
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