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Bacterial Games

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Abstract

Microbial laboratory communities have become model systems for studying the complex interplay between nonlinear dynamics of evolutionary selection forces, stochastic fluctuations arising from the probabilistic nature of interactions, and spatial organization. Major research goals are to identify and understand mechanisms that ensure viability of microbial colonies by allowing for species diversity, cooperative behavior and other kinds of “social” behavior. A synthesis of evolutionary game theory, nonlinear dynamics, and the theory of stochastic processes provides the mathematical tools and conceptual framework for a deeper understanding of these ecological systems. We give an introduction to the modern formulation of these theories and illustrate their effectiveness, focusing on selected examples of microbial systems. Intrinsic fluctuations, stemming from the discreteness of individuals, are ubiquitous, and can have important impact on the stability of ecosystems. In the absence of speciation, extinction of species is unavoidable, may, however, take very long times. We provide a general concept for defining survival and extinction on ecological time scales. Spatial degrees of freedom come with a certain mobility of individuals. When the latter is sufficiently high, bacterial community structures can be understood through mapping individual-based models, in a continuum approach, onto stochastic partial differential equations. These allow progress using methods of nonlinear dynamics such as bifurcation analysis and invariant manifolds. We conclude with a perspective on the current challenges in quantifying bacterial pattern formation, and how this might have an impact on fundamental research in nonequilibrium physics .

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Notes

  1. 1.

    The two-species Lotka–Volterra equations describe a predator–prey system where the per capita growth rate of the prey decreases linearly with the number of predators present. In the absence of prey, predators die, but there is a positive contribution to their growth which increases linearly with the amount of prey present [22, 23].

  2. 2.

    Note that ε is the fraction of carbon source kept by cooperators solely for themselves and \(x(1-\epsilon)\) is the amount of carbon source shared with the whole community. Hence, the linear growth rates of cooperators and defectors would be \(\epsilon + x(1-\epsilon) -c\) and \(x(1-\epsilon)\), respectively, where c is the metabolic cost for invertase production.

  3. 3.

    You may also want to haven a look at the movies posted on http://www.theorie.physik.uni-muenchen.de/lsfrey/research/fields/biological_physics/2007_004/. There is also a Wolfram demonstration project that can be downloaded from the internet: http://demonstrations.wolfram.com/BiodiversityInSpatialRockPaperScissorsGames/.

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Acknowledgements

We are indebted to Benjamin Andrae, Maximilian Berr, Jonas Cremer, Alexander Dobrinevsky, Anna Melbinger, Mauro Mobilia, Steffen Rulands, and Anton Winkler, with whom we had the pleasure to work on game theory. They have contributed with a multitude of creative ideas and through many insightful discussions have shaped our understanding of the topic. Financial support from the German Excellence Initiative via the program “Nanosystems Initiative Munich” and the German Research Foundation via the SFB TR12 “Symmetries and Universalities in Mesoscopic Systems” is gratefully acknowledged.

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Frey, E., Reichenbach, T. (2011). Bacterial Games. In: Meyer-Ortmanns, H., Thurner, S. (eds) Principles of Evolution. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18137-5_13

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