Adaptive laboratory evolution for growth coupled microbial production
Publication Date
11-1-2020
Document Type
Article
Publication Title
World Journal of Microbiology and Biotechnology
Volume
36
Issue
11
DOI
10.1007/s11274-020-02946-8
Abstract
Abstract: Adaptive laboratory evolution (ALE) is a powerful tool to select for strains with growth-coupled phenotypes. When coupled with next-generation sequencing and omic technologies, genotype-to-phenotype relationships and the molecular mechanisms underlying desired complex phenotypes can now be uncovered using ALE. However, in order for ALE to be effective in generating strains with increased productivity, the product-of-interest needs to be coupled with cellular growth or survival. Advances in computational metabolic modeling can now identify metabolic engineering strategies to force the coupling of desired product formation with growth for a wide range of different compounds. Such strategies can potentially be coupled with ALE to further enhance productivity of microbial hosts. In addition to metabolic strategies, if the compound of interest is known to impart beneficial traits to the host, such as stress tolerance, then an environment can be designed to allow product formation to be coupled with growth or survival. This mini-review will cover recent advances in both the metabolic and environmental engineering and synthetic biology strategies to couple production with microbial fitness, successful cases for the use of these strategies with ALE to improve product formation, discuss limitations, and future perspectives. Graphic abstract: [Figure not available: see fulltext.]
Funding Number
CBET-1605347
Funding Sponsor
National Science Foundation
Keywords
ALE, Biotechnology; mini review, Computational strategy, Growth-coupled production, Metabolic engineering
Department
Chemical and Materials Engineering
Recommended Citation
Avinash Godara and Katy C. Kao. "Adaptive laboratory evolution for growth coupled microbial production" World Journal of Microbiology and Biotechnology (2020). https://doi.org/10.1007/s11274-020-02946-8