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Bronk, Benedikt von; Gotz, Alexandra; Opitz, Madeleine (2018): Locality of interactions in three-strain bacterial competition in E. coli. In: Physical Biology, Vol. 16, No. 1
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The population dynamics that determine the composition and stability of ecosystems ultimately emerge from interactions between individual organisms. One well-studied system is the three-strain E. coli interaction of a heterogeneously toxin-producing C strain that interacts with a toxin-sensitive S and a toxin-resistant R strain. Here, we employ a multi-scale fluorescence microscopy approach, that has been proven useful in identifying previously unknown or underestimated stochastic effects in C-S competition. This approach allows us to investigate the microscopic interaction of the R strain and to quantify the role of stochastic effects in the spatially structured C-R-S interaction. We show that the early colony patterning at 12 h and at small length scales (near single cell level) is characterized by a number of microscopic variables (the number of C and R cell clusters and the area occupied by S) and is subject to random processes in positioning and toxin production. Then, in a second competition phase, mainly deterministic processes such as bacterial growth and global toxin action determine the following population dynamics. Consequently, together with environmental factors, the microscopic variables were predictive of the competition outcome. However, interactions of neighboring R and C clusters could amplify local variations. If R clusters originated near a C cell cluster, R could profit from the toxin produced by C without bearing the cost of production-a mechanism called cheating. By combining information from the micro- and macro-scale dynamics, we can estimate the distance at which the cheating interaction significantly changes to be in the order of 250 microm. In summary, after an initial phase influenced by stochastic patterning, largely deterministic growth dynamics follow, which are additionally affected by local interactions of neighboring clusters. As such, the results underline the importance of stochasticity and local effects in the context of ecological interactions.