Multi-species microbial communities often display community functions, biochemical activities that arise from interactions among 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, previous selection efforts have often generated mixed outcomes that are difficult to interpret. We will investigate how to improve the rate of community function improvement, using mathematical modelling and experiments.
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