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Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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Controlling evolutionary dynamics to optimize microbial bioremediation.

Shota Shibasaki1, Sara Mitri1

  • 1Department of Fundamental Microbiology University of Lausanne Lausanne Switzerland.

Evolutionary Applications
|October 2, 2020
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Summary
This summary is machine-generated.

Microbes detoxifying toxins (bioremediation) face costly cooperation. Periodic reinoculation of cooperators can prevent cheater invasion and maximize bioremediation efficiency.

Keywords:
chemostatdetoxificationeco‐evolutionary feedbackevolutionary game theorypublic goods game

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Area of Science:

  • Microbiology
  • Evolutionary Biology
  • Environmental Science

Background:

  • Microbes can degrade toxic compounds via bioremediation.
  • Detoxification is a costly public good, leading to invasion by non-detoxifying mutants (cheaters).
  • This evolutionary dynamic can collapse bioremediation efforts.

Purpose of the Study:

  • To model bioremediation as an evolutionary game.
  • To identify conditions for optimizing bioremediation efficiency.
  • To explore strategies for preventing cheater invasion.

Main Methods:

  • Constructed an evolutionary game theoretical model.
  • Simulated bioremediation in a chemostat with cooperating microbes and two mutant types (cheaters and private resisters).
  • Manipulated toxin concentration and flow rate.

Main Results:

  • Identified conditions where cooperators can exclude cheaters.
  • Demonstrated that cheaters eventually invade.
  • Showed that periodic reinoculation of cooperators can maximize detoxification efficiency.

Conclusions:

  • Bioremediation dynamics are influenced by public and private goods.
  • Environmental parameters can control microbial evolutionary dynamics.
  • Periodic reinoculation is a viable strategy to sustain bioremediation.