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Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering
Author(s) -
Presnell Kristin V.,
Alper Hal S.
Publication year - 2019
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201800416
Subject(s) - metabolic engineering , systems biology , computer science , synthetic biology , profiling (computer programming) , data science , machine learning , biochemical engineering , artificial intelligence , computational biology , biology , engineering , biochemistry , enzyme , operating system
The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.