Machine learning for metabolic engineering: A review
| Autor: | Jose Manuel Martí, Sai Vamshi R. Jonnalagadda, Christopher J. Petzold, Aindrila Mukhopadhyay, Reinhard Gentz, Christopher E. Lawson, Hector Garcia Martin, Joonhoon Kim, Deepti Tanjore, Sean Peisert, Steven W. Singer, Joshua G. Dunn, Tijana Radivojevic, Blake A. Simmons, Nathan J. Hillson |
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| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Computer science Data management Bioengineering Machine learning computer.software_genre 01 natural sciences Applied Microbiology and Biotechnology Industrial Biotechnology Metabolic engineering Omics data Machine Learning 03 medical and health sciences Synthetic biology Deep Learning 010608 biotechnology Production (economics) 030304 developmental biology Gene Editing 0303 health sciences business.industry Deep learning Variety (cybernetics) Metabolic Engineering Synthetic Biology Artificial intelligence business Advice (complexity) computer Algorithms Biotechnology |
| Popis: | Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined. |
| Databáze: | OpenAIRE |
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