Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae.

J Biosci Bioeng

Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. Electronic address:

Published: April 2023

AI Article Synopsis

  • Saccharomyces cerevisiae, a yeast with a rich history in biotechnology, is currently benefiting from advancements in systems and synthetic biology, particularly in its applications across different industries.
  • Recent studies focus on omics research to understand its stress tolerance and improve its capabilities for industrial uses.
  • New genome editing tools and metabolic engineering techniques are enhancing the development of genome-scale metabolic models and optimizing fermentation processes, allowing S. cerevisiae to produce various compounds through engineered biosynthetic pathways.

Article Abstract

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

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Source
http://dx.doi.org/10.1016/j.jbiosc.2023.01.010DOI Listing

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