Collective dynamics of gliding filamentous cyanobacteria

• Applicants: Professor Dr. Stefan Karpitschka; Professor Dr. Stefan Klumpp
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 519479626

Filamentous cyanobacteria are phototrophic prokaryotic lifeforms in which the long and flexible individuals consist of many, linearly stacked cells. Many species exhibit bi-directional active gliding motility, self-propelling along their contour when in contact with surfaces or each other. Filaments aggregate and self-organize into a variety of large-scale structures, which is believed to be pivotal for adaptation to different environmental conditions and thus their evolutionary success. Conceptually, gliding filamentous cyanobacteria provide an experimental realization of active polymers – long, thin, flexible, and self-propelling. Moreover, since they react to light, their motility and self-organization can be controlled externally, rendering them rather distinct from other active matter systems. In this project we aim at understanding their self-organization using a combined experimental and computational approach. We will characterize the physical properties of several species of filamentous cyanobacteria, such as their gliding velocity or their bending rigidity, and quantify their behavior of reversing gliding direction, both spontaneously and in responses to external cues. We will determine how these properties depend on – and can be tuned by – culture conditions. Further, by analyzing their response to mechanical collisions or to changes in illumination, and the corresponding spatio-temporal evolution of the propulsion force distributions, we will get insights into how motility is coordinated among the cells in a filament. Equipped with a quantitative description of individual filaments, we will study the development and dynamics of patterns that are formed in dense monolayers of filaments. Individual filaments will be segmented and tracked in imagery of the layers with a dedicated machine-learning approach. The corresponding patterns will be reproduced in simulations using an active polymer model in two dimensions. The direct comparison between experiment and simulation down to the level of the individual will allow us to tailor a near-quantitative model, providing insight into the mechanistic basis of their self-organization. We will determine the polar and nematic order of the pattern and study the generation and mobility of topological defects, applying the same analysis to simulated and experimental patterns. Based on the systematic characterization and variation of the physical properties of different species and different culture conditions, we will map species and culture conditions to a dense phase diagram obtained from the simulations. Finally, we will use their responses to light to develop control strategies to for their pattern formation.

• DFG Programme: Research Grants