Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a time-honored approach to obtain desired microbes in microbial engineering. Advanced strategies and tools facilitate and streamline ALE in growth coupling, genotypic diversification, phenotypic selection, and genotype–phenotype mapping. These strategies and tools have made considerable recent progress and have led to remarkable applications. Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype–phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments. Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype–phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments. chemostats with additional feedback control of cell density [e.g., optical density (OD) or pH-based control]. continuous-culturing devices in which influx and efflux of medium are equal. genetic devices that can respond to the concentration of biological analytes and output detectable signals, including transcription factor-based biosensor and riboswitch-based biosensor. a model that computationally describes the entire biochemical metabolic networks in an organism. methods in which DNA variants are generated in vitro and need to be introduced into microbes by transformation, such as error-prone PCR and oligonucleotide-mediated recombineering. methods that continuously introduce mutations at a rate higher than spontaneous mutation, such as UV. a discipline of engineering to genetically reconstruct organisms to produce targeted products. organisms that can use C1 compounds, such as methane and methanol, as the carbon and energy source. analysis based on multi-omics data sets (i.e., metabolomics, proteomics, and transcriptomics). genes that increase the mutation frequency in organisms. nicotinamide adenine dinucleotide, a critical coenzyme related to ATP regeneration. an approach to identify the function of mutated genes found in strain variants.