Engineering complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes. A simulation study integrates existing artificial selection methods to develop a 'top-down' approach to engineering complex, stable microbial communities based on iterated randomization and selection of community structure and function.

Automated summary

The study utilized a flexible modeling framework to systematically explore possible strategies for artificial selection of microbial communities, revealing that simple screening of mature communities outperforms traditional breeding methods. The results emphasize that directed evolution can be used to find stable, dynamic, and ecologically resilient communities with desired attributes.