ArticleEvolutionary applications2020
Controlling evolutionary dynamics to optimize microbial bioremediation.
Article in Evolutionary applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Microbial Metabolic Strategies for Environmental Detoxification: From Enzymatic Mechanisms to Synthetic Biology and Omics.Applied biochemistry and biotechnology · 2026Review
- Applying ecological principles to microbiome engineering.Nature microbiology · 2025Review
- Parallel ecological and evolutionary responses to selection in a natural bacterial community.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Identifying and tracking mobile elements in evolving compost communities yields insights into the nanobiome.ISME communications · 2023Article
- Pulsed, continuous or somewhere in between? Resource dynamics matter in the optimisation of microbial communities.The ISME journal · 2023Article
- Ecological landscapes guide the assembly of optimal microbial communities.PLoS computational biology · 2023Article
- Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia.Royal Society open science · 2022Article
- Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales.Nature communications · 2021Article
- Directed Evolution of Microbial Communities.Annual review of biophysics · 2021Review
- Controlling evolutionary dynamics to optimize microbial bioremediation.Evolutionary applications · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Some microbes have a fascinating ability to degrade compounds that are toxic for humans in a process called bioremediation. Although these traits help microbes survive the toxins, carrying them can be costly if the benefit of detoxification is shared by all surrounding microbes, whether they detoxify or not. Detoxification can thereby be seen as a public goods game, where nondegrading mutants can sweep through the population and collapse bioremediation. Here, we constructed an evolutionary game theoretical model to optimize bioremediation in a chemostat initially containing "cooperating" (detoxifying) microbes. We consider two types of mutants: "cheaters" that do not detoxify, and mutants that become resistant to the toxin through private mechanisms that do not benefit others. By manipulating the concentration and flow rate of a toxin into the chemostat, we identified conditions where cooperators can exclude cheaters that differ in their private resistance. However, eventually, cheaters are bound to invade. To overcome this inevitable outcome and maximize detoxification efficiency, cooperators can be periodically reinoculated into the population. Our study investigates the outcome of an evolutionary game combining both public and private goods and demonstrates how environmental parameters can be used to control evolutionary dynamics in practical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.