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In silico bacteria evolve robust cooperaion via complex quorum-sensing strategies
Yifei Wang1,2,3, Jennifer B Rattray4,5, Stephen A Thomas4,6,5
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, 30332 GA, USA. yifei.wang@gatech.edu.
Scientific Reports
|May 27, 2020
Summary
Quorum sensing (QS) in bacteria, a system for cooperative traits, has debated functions. Agent-based modeling reveals QS evolution, showing coercive and reciprocal strategies enhance cooperation resilience against cheats.
Area of Science:
- Microbiology
- Evolutionary Biology
- Computational Biology
Background:
- Bacteria use quorum sensing (QS) for collective behavior and cooperative traits.
- The precise ecological functions driving QS evolution remain debated, with potential roles including density and genotype sensing.
- Distinguishing adaptive drivers from byproducts (spandrels) is challenging in extant species.
Purpose of the Study:
- To investigate the evolutionary trajectories of quorum sensing under defined ecological challenges.
- To understand the relationship between ecological pressures and the emergence of diverse QS strategies.
- To explore the role of genetic constraints in shaping QS evolution.
Main Methods:
- Agent-based simulation modeling was employed to simulate bacterial populations.
- Simulations incorporated genetic mixing to model population dynamics and evolution.
- Digital organisms were evolved under controlled ecological conditions.
Main Results:
- Simulations recapitulated diverse microbial QS system features, including coercive and generalized reciprocity strategies.
- These QS strategies, individually and combined, enhance the resilience of QS-controlled cooperation against cheating.
- In silico findings highlight the influence of genetic constraints on short-term QS dynamics, mirroring experimental evolution.
Conclusions:
- Agent-based modeling provides a framework to link ecological challenges with QS evolution.
- QS architectures and functions emerge through evolutionary processes influenced by ecological and genetic factors.
- Experimental evolution of digital organisms is a valuable tool for understanding complex QS systems.
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