Novel artificial selection method improves function of simulated microbial communities.

Area of Science:

• Microbial Ecology
• Computational Biology
• Evolutionary Biology

Background:

• Artificial selection of microbial communities is gaining interest but faces challenges with modest experimental success.
• Understanding how to effectively breed microbial communities for desired functions, like toxin degradation, is crucial.

Purpose of the Study:

• To computationally model and compare existing microbial community selection methods with a novel

Main Methods:

• Simulated two established artificial selection methods and a new "disassembly" method using computational models.
• Evaluated methods based on their ability to evolve microbial communities for efficient toxin degradation, balancing degradation investment with growth rate.
• The "disassembly" method involved competing diverse, regularly shuffled species combinations.

Main Results:

• The "disassembly" method excelled at exploring numerous species combinations, maintaining diversity for effective selection and achieving high community performance.
• Individual species evolved to prioritize growth over community function (toxin degradation), but increased growth compensated for reduced investment.
• While community selection didn't override individual selection or ecological dynamics, long-term community composition and function declined.

Conclusions:

• The primary strength of the "disassembly" method lies in its capacity for extensive exploration of species combinations.
• Community-level selection did not fully counteract individual-level selection or inherent ecological dynamics within the simulated communities.
• Findings suggest alternative experimental designs for microbial community selection, emphasizing the importance of managing within-community dynamics.