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Lalejini, A.

Publications and source records attributed to Lalejini, A..

2 recordsLinked to original sources

Fluctuating environments promote evolvability by shaping adaptive variation accessible to populations

Living systems are surprisingly effective at exploiting new opportunities, as evidenced by the rapid emergence of antimicrobial resistance and novel pathogens. How populations attain this level of evolvability and the various ways it aids their survival are major open questions with direct implications for human health. Here, we use digital evolution to show that particular kinds of environments facilitate the simultaneous evolution of high mutation rates and a distribution of mutational effects skewed towards beneficial phenotypes. The evolved mutational neighborhoods allow rapid adaptation to previously encountered environments, whereas higher mutation rates aid adaptation to completely new environmental conditions. By precisely tracking evolving lineages and the phenotypes of their mutants, we show that evolving populations localize on phenotypic boundaries between distinct regions of genotype space. Our results demonstrate how evolution shapes multiple determinants of evolvability concurrently, fine-tuning a populations adaptive responses to unpredictable or recurrent environmental shifts.

evolutionary biology↗

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask if parent selection algorithms--procedures for choosing promising progenitors--from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top-10% selection). We found that multi-objective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multi-objective selection procedures into the laboratory. Additionally, our findings suggest that more sophisticated artificial selection methods from evolutionary computation should also be evaluated for use in directed microbial evolution.

evolutionary biology↗