ArticleJournal of the Royal Society, Interface2021
An evolutionary algorithm for designing microbial communities via environmental modification.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Niche Construction: A Rational Engineering Strategy for Regulating Microbial Assembly to Enhance Flavor and Bioactivity of Fermented Foods.Comprehensive reviews in food science and food safety · 2026Review
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities.Biophysical journal · 2024Article
- Formation of a constructed microbial community in a nutrient-rich environment indicates bacterial interspecific competition.mSystems · 2024Article
- A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).Nature protocols · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
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
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