Systems Metabolic Engineering Meets Machine Learning: A new era for data-driven metabolic engineering.

08:00 EDT 30th March 2019 | BioPortfolio

Summary of "Systems Metabolic Engineering Meets Machine Learning: A new era for data-driven metabolic engineering."

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning 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 we discuss the nature of 'omics and provide a broad introduction to the machine learning algorithms combining these datasets into predictive models of metabolism and metabolic rewiring. Next, we highlight recent work in the literature that utilize 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 machine learning algorithms with metabolic engineering and systems biology related datasets. This article is protected by copyright. All rights reserved.


Journal Details

This article was published in the following journal.

Name: Biotechnology journal
ISSN: 1860-7314
Pages: e1800416


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A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

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