Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction discipline in terms that are relatable engineers, as well providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner of management, algorithm libraries, computational resources, important non-technical issues. A variety applications ranging from pathway construction optimization, genetic editing cell factory testing, production scale-up discussed. Moreover, promising relationship between machine mechanistic models is thoroughly reviewed. Finally, future perspectives most directions combination disciplines examined.