Adaptive laboratory evolution — harnessing the power of biology for metabolic engineering
Abstract
Adaptive laboratory evolution (ALE) strategies allow for the metabolic engineering of microorganisms by combining genetic variation with the selection of beneficial mutations in an unbiased fashion. These ALE strategies have been proven highly effective in the optimization of production strains. In contrast to rational engineering strategies and directed modification of specific enzymes, ALE has the advantage of letting nonintuitive beneficial mutations occur in many different genes and regulatory regions in parallel. So far, the majority of applications of ALE in metabolic engineering have used well-characterized platform organisms such as Saccharomyces cerevisiae and Escherichia coli; however, applications for other microorganisms are on the rise. This review will focus on current applications of ALE as a tool for metabolic engineering and discuss advancements and achievements that have been made in this field.
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Classical genetic engineering was recently successfully complemented with artificial laboratory selection of microbial cells in order to generate potentially robust and optimized microbial production systems.
“Recently, classical genetic engineering was successfully complemented with artificial laboratory selection of microbial cells in order to generate potentially robust and optimized microbial production systems [15,16].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [15]
Deletions accounted for approximately 29% of the observed genomic changes, followed by insertions (7%) and IS movements (3%).
“Deletions accounted for approximately 29% of the observed genomic changes, followed by insertions (7%) and IS movements (3%) [15].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [15]
SNPs account for the majority of observed genetic changes, with G:C to T:A mutations being disproportionally frequent.
“As summarized in a recent review, SNPs (61%, with G:C to T:A mutations being disproportionally frequent) account for the majority of observed genetic changes [15].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [15]
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References 66 · Cited by 309
- Authors
- Portnoy, V. A., Bezdan, D., & Zengler, K.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Zengler, Karsten
- Venue
- Current Opinion in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2011-08
- Volume
- 22
- Issue
- 4
- Pages
- 590-594
- ISSN
- 0958-1669 (ISSN-L)
- OpenAlex
- W2008681679
- Cited by
- 309
- References
- 66
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 18 · 2023: 21 · 2022: 24 · 2021: 41 · 2020: 28 · 2019: 32 · 2018: 18 · 2017: 18 · 2016: 18 · 2015: 18 · 2014: 14 · 2013: 20 · 2012: 7
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- adaptive laboratory evolution, metabolic engineering, microorganisms, beneficial mutations, genetic variation, regulatory elements, Saccharomyces cerevisiae, Escherichia coli
- MeSH terms
- Escherichia coli, Saccharomyces cerevisiae, Directed Molecular Evolution, Metabolic Networks and Pathways
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