An evolutionary algorithm for designing microbial communities via environmental modification

Pacheco, Alan R.; Segrè, Daniel

doi:10.1098/rsif.2021.0348

An evolutionary algorithm for designing microbial communities via environmental modification

Journal Article · Wed Jun 23 00:00:00 EDT 2021 · Interface (Online)

DOI:https://doi.org/10.1098/rsif.2021.0348· OSTI ID:1889427

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Boston University, MA (United States)

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); Howard Hughes Medical Institute; National Academies of Sciences, Engineering, and Medicine Ford Foundation; National Institutes of Health; National Science Foundation (NSF); Human Frontiers Science Program; Boston University

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1889427

Journal Information:: Interface (Online), Journal Name: Interface (Online) Journal Issue: 179 Vol. 18; ISSN 1742-5662

Publisher:: The Royal SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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An evolutionary algorithm for designing microbial communities via environmental modification

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