Novel artificial selection method improves function of simulated microbial communities

There is increasing interest in artificially selecting or breeding microbial communities, but experiments have reported modest success. Here, we develop computational models to simulate two previously known selection methods and compare them to a new “disassembly” method. We evaluate all three methods in their ability to find a community that could efficiently degrade toxins, whereby investment into degradation results in slower growth. Our disassembly method relies on repeatedly competing different communities of known species combinations against one another, while regularly shuffling around their species combinations. This approach allows many species combinations to be explored, thereby maintaining enough between-community diversity for selection to act on, and resulting in communities with high performance. Nevertheless, selection at the community level in our simulations did not counteract selection at the individual level, nor the communities’ ecological dynamics. Species in our model evolved to invest less into community function and more into growth, but increased growth compensated for reduced investment, such that overall community performance was barely affected by within-species evolution. Within-community ecological dynamics were more of a challenge, as we could control them during the selection process, but community composition and function dropped in the longer term. Our work shows that the strength of disassembly lies mainly in its ability to explore different species combinations, and helps to propose alternative designs for community selection experiments.Author summary: Artificial selection has been extremely powerful in improving properties of complex biological or biochemical entities. The most familiar examples come from the breeding of animals and plants, but directed evolution has also been applied to increasing the efficacy of enzymes. Microbial communities are the promising next frontier for artificial selection, as they have the potential to overcome many human problems, from efficiently degrading plastic to increasing agricultural yield. Yet, breeding stable, well-functioning communities is hard: published selection experiments from the last decade have shown only minor improvements over naturally assembled communities. Before conducting more experiments, models can help to better understand the problems with artificially selecting communities and how to improve current approaches. Here, we develop mathematical models that show one reason why previous selection methods have been unsuccessful, propose a new selection method that overcomes this problem and analyze its strengths, weaknesses and experimental feasibility. The essence of the new method is to explore many different species combinations in a way that gradually improves community performance to ultimately find the best species combinations. According to our models, our new method is expected to outperform previous methods significantly.