Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering
Title:
Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering
Other Titles:
Metabolic Engineering Communications
DOI:
10.1016/j.mec.2020.e00149
Publication Date:
09 October 2020
Citation:
Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metab Eng Commun. 2020 Dec;11:e00149. doi: 10.1016/j.mec.2020.e00149. Epub 2020 Oct 9. PMID: 33072513; PMCID: PMC7546651.
Abstract:
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
Singapore Institute of Food and Biotechnology Innovation (SIFBI, A∗STAR) for supporting this work (under IAFPP3 - H20H6a0028).
Description:
Please find the link to the article at the publisher's URL: https://doi.org/10.1016/j.mec.2020.e00149
ISSN:
2214-0301
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