Synthetic Biology of Natural Products Engineering: Recent Advances Across the Discover-Design-Build-Test-Learn Cycle.

biosynthetic gene clusters genome mining machine learning natural products strain engineering synthetic biology

Journal

ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075

Informations de publication

Date de publication:
20 Aug 2024
Historique:
medline: 20 8 2024
pubmed: 20 8 2024
entrez: 20 8 2024
Statut: aheadofprint

Résumé

Advances in genome engineering and associated technologies have reinvigorated natural products research. Here we highlight the latest developments in the field across the discover-design-build-test-learn cycle of bioengineering, from recent progress in computational tools for AI-supported genome mining, enzyme and pathway engineering, and compound identification to novel host systems and new techniques for improving production levels, and place these trends in the context of responsible research and innovation, emphasizing the importance of anticipatory analysis at the early stages of process development.

Identifiants

pubmed: 39163395
doi: 10.1021/acssynbio.4c00391
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Jonathan Foldi (J)

Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester M1 7DN, United Kingdom.

Jack A Connolly (JA)

Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester M1 7DN, United Kingdom.

Eriko Takano (E)

Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester M1 7DN, United Kingdom.

Rainer Breitling (R)

Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester M1 7DN, United Kingdom.

Classifications MeSH