Selection schemes from evolutionary computing show promise for directed evolution of microbes

Abstract

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Directing 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 computational problems. Despite overlapping aims, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. Here, we summarize recent work wherein we ask if parent selection algorithms from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we developed an agent-based model of directed microbial evolution, which we used to evaluate how well three selection schemes from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to schemes used in the laboratory (elite and top-10% selection). We found that lexicase selection and non-dominated elite selection generally outperformed the commonly used directed evolution approaches. Our results are informing ongoing work to transfer these techniques into the laboratory and motivate future work testing more sophisticated selection schemes from evolutionary computation in a directed evolution context.