Chemotactic clusters in confined run-and-tumble bacteria: a numerical investigation

Abstract

We present a simulation study of pattern formation in an ensemble of chemotactic run-and-tumble bacteria, focussing on the effect of spatial confinement, either within traps or inside a maze. These geometries are inspired by previous experiments probing pattern formation in chemotactic strains of E. coli under these conditions. Our main result is that a microscopic model of chemotactic run-and-tumble particles which themselves secrete a chemoattractant is able to reproduce the main experimental observations, namely the formation of bacterial aggregates within traps and in dead ends of a maze. Our simulations also demonstrate that stochasticity plays a key role and leads to a hysteretic response when the chemotactic sensitivity is varied. We compare the results of run-and-tumble particles with simulations performed with a simplified version of the model where the active particles are smooth swimmers which respond to chemotactic gradients by rotating towards the source of chemoattractant. This class of models leads again to aggregation, but with quantitative and qualitative differences in, for instance, the size and shape of clusters.

Nonlinear concentration patterns and bands in autochemotactic suspensions

“…This rotation should be distinguished from rotational diffusion, which acts on very rapid time-scales and is associated with very small changes in direction. Chemotaxis represented as a bias in the direction of individual swimming has often been used in numerical studies of active particles [43][44][45][46][47]. We impose a torque on the swimmers in Eq.…”

The role of particle shape in active depletion

“…The outer surfaces have positive curvature, and colliding particles can slide off rather quickly. The inner surfaces have negative curvature and can create a trap [28] for the active particles, greatly increasing the duration of a collision. The result is a net gradient in particle concentration along the colloidal surface, leading to the effective repulsion reported in our simulations (See Fig.…”

Decision-making at a T-junction by gradient-sensing microscopic agents

“…As a matter of fact, existing methods for the identification of the shortest path in a maze involve big particles and global knowledge harvested from nonlocal quantities, for instance, millimetric droplets by the Marangoni effect [5,6], amoeboid growth [7], the propagation of chemical waves [8], trains of droplets advected by a solvent flow [9], or the parallel exploration of all possible paths in a maze by pressure-driven flows [10]. Microfluidic networks are further used to confine bioagents [11] and chemotactic bacteria, showing the formation of bacterial clusters where bacteria secrete their own chemoattractant [12] or the ability of bacteria to search out each other and dynamically confine themselves [13]. Branching maze geometries have been used in ecology to study the orientation of penguins or birds [14,15] and the routing of plant roots [16] in response to volatile chemical compounds.…”