SummarySwarming in Pseudomonas aeruginosa is a coordinated movement of bacteria over semisolid surfaces (0.5%–0.7% agar). On soft agar, P. aeruginosa exhibits a dendritic swarm pattern, with multiple levels of branching. However, the swarm patterns typically vary depending upon the experimental design. In the present study, we show that the pattern characteristics of P. aeruginosa swarm are highly environment dependent. We define several quantifiable, macroscale features of the swarm to study the plasticity of the swarm, observed across different nutrient formulations. Furthermore, through a targeted screen of 113 two-component system (TCS) loci of the P. aeruginosa strain PA14, we show that forty-four TCS genes regulate swarming in PA14 in a contextual fashion. However, only four TCS genes—fleR, fleS, gacS, and PA14_59770—were found essential for swarming. Notably, many swarming-defective TCS mutants were found highly efficient in biofilm formation, indicating opposing roles for many TCS loci.

Models based on surfactant driven instabilities have been employed to describe pattern formation by swarming bacteria. However, by definition, such models cannot account for the effect of bacterial sensing and decision making. Here we present a more complete model for bacterial pattern formation which accounts for these effects by coupling active bacterial motility to the passive fluid dynamics. We experimentally identify behaviours which cannot be captured by previous models based on passive population dispersal and show that a more accurate description is provided by our model. It is seen that the coupling of bacterial motility to the fluid dynamics significantly alters the phase space of surfactant driven pattern formation. We also show that our formalism is applicable across bacterial species.

Bacteria are perhaps the simplest living systems capable of complex behaviour involving sensing and coherent, collective behaviour an example of which is the phenomena of swarming on agar surfaces. Two fundamental questions in bacterial swarming is how the information gathered by individual members of the swarm is shared across the swarm leading to coordinated swarm behaviour and what specific advantages does membership of the swarm provide its members in learning about their environment. In this article, we show a remarkable example of the collective advantage of a bacterial swarm which enables it to sense inert obstacles along its path. Agent based computational model of swarming revealed that independent individual behaviour in response to a two-component signalling mechanism could produce such behaviour. This is striking because independent individual behaviour without any explicit communication between agents was found to be sufficient for the swarm to effectively compute the gradient of signalling molecule concentration across the swarm and respond to it.

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