PT  - JOURNAL ARTICLE
AU  - Li Xie
AU  - Alex Yuan
AU  - Wenying Shou
TI  - Artificial selection of microbial communities can become effective after using evolution-informed strategies
AID  - 10.1101/264689
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 264689
4099  - http://biorxiv.org/content/early/2018/09/20/264689.short
4100  - http://biorxiv.org/content/early/2018/09/20/264689.full
AB  - Multi-species microbial communities often display “community functions” stemming from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest functions to reproduce by randomly partitioning each into multiple “Newborn” communities for the next cycle. However, community selection is challenging since rapid changes in species and genotype compositions can limit the heritability of community function. To understand how to enact community selection, we used an individual-based model to simulate this process to improve a community function that requires two species and is costly to one species. Improvement was stalled by non-heritable variations in community function, such as the stochastic populating of Newborn communities or measurement errors of community function. Community function improved when these non-heritable variations were suppressed in experimentally feasible manners.