Collective behavior of self-propelled rods with quorum sensing

Spontaneous oscillation in collective microswimmers: Insights from a chiral self-propelled rod model

Active systems exhibit fascinating self-organized structures and rich motility patterns, yet the underlying mechanisms governing their emergence and characteristics remain elusive. Here, we develop a chiral self-propelled rod (CSPR) model with mechanical contact-induced quorum sensing to investigate the spatiotemporal dynamics of dense bacteria populations. Our findings show that the CSPR model showcases spontaneous nonequilibrium oscillatory clustering of active systems. The motion characteristics of these clusters depend on colony features (microswimmers' morphology and density) and mechanical contact-induced sensing mechanisms (polarization alignment and angular velocity alignment of CSPR). Interestingly, reinforced strength of polar alignment accelerates the formation of stable oscillations, while decreased density and angular velocity alignment strength modify their emergence pattern. Significantly, our study identifies three distinct oscillation patterns: global stable oscillation, bistable oscillation, and multistable oscillation, and reveals that their phase transitions are driven by variations in the spatial correlation of CSPR. These insights provide a new perspective on understanding the intricate evolution of active matter, opening possible avenues for emerging applications.

Motility-Induced Phase Separation Mediated by Bacterial Quorum Sensing

We study motility-induced phase separation (MIPS) in living active matter, in which cells interact through chemical signaling, or quorum sensing. In contrast to previous theories of MIPS, our multiscale continuum model accounts explicitly for genetic regulation of signal production and motility. Through analysis and simulations, we derive a new criterion for the onset of MIPS that depends on features of the genetic network. Furthermore, we identify and characterize a new type of oscillatory instability that occurs when gene regulation inside cells promotes motility in higher signal concentrations.

Dynamics of Bacteria Scanning a Porous Environment

It has recently been reported that bacteria, such as Escherichia coli Bhattacharjee and Datta, Nat. Commun. 10, 2075 (2019).NCAOBW2041-172310.1038/s41467-019-10115-1 and Pseudomonas putida Alirezaeizanjani et al., Sci. Adv. 6, eaaz6153 (2020).SACDAF2375-254810.1126/sciadv.aaz6153, perform distinct modes of motion when placed in porous media as compared to dilute regions or free space. This has led us to suggest an efficient strategy for active particles in a disordered environment: reorientations are suppressed in locally dilute regions and intensified in locally dense ones. Thereby the local geometry determines the optimal path of the active agent and substantially accelerates the dynamics for up to 2 orders of magnitude. We observe a nonmonotonic behavior of the diffusion coefficient in dependence on the tumbling rate and identify a localization transition, either by increasing the density of obstacles or by decreasing the reorientation rate.

Cluster and conquer: the morphodynamics of invasion of a compliant substrate by active rods

The colonisation of a soft passive material by motile cells such as bacteria is common in biology. The resulting colonies of the invading cells are often observed to exhibit intricate patterns whose morphology and dynamics can depend on a number of factors, particularly the mechanical properties of the substrate and the motility of the individual cells. We use simulations of a minimal 2D model of self-propelled rods moving through a passive compliant medium consisting of particles that offer elastic resistance before being plastically displaced from their equilibrium positions. It is observed that the clustering of active (self-propelled) particles is crucial for understanding the morphodynamics of colonisation. Clustering enables motile colonies to spread faster than they would have as isolated particles. The colonisation rate depends non-monotonically on substrate stiffness with a distinct maximum at a non-zero value of substrate stiffness. This is observed to be due to a change in the morphology of clusters. Furrow networks created by the active particles have a fractal-like structure whose dimension varies systematically with substrate stiffness but is less sensitive to particle activity. The power-law growth exponent of the furrowed area is smaller than unity, suggesting that, to sustain such extensive furrow networks, colonies must regulate their overall growth rate.

Phase separation of an active colloidal suspension via quorum-sensing

We present the Brownian dynamics simulation of an active colloidal suspension in two dimensions, where the self-propulsion speed of a colloid is regulated according to the local density sensed by it. The role of concentration-dependent motility in the phase-separation of colloids and their dynamics is investigated in detail. Interestingly, the system phase separates at a very low packing fraction (Φ≈ 0.125) at higher self-propulsion speeds (Pe), into a dense phase coexisting with a homogeneous phase and attains a long-range crystalline order beyond the transition point. The transition point is quantified here from the local density profiles and local and global-bond order parameters. We have shown that the characteristics of the phase diagram are qualitatively akin to the active Brownian particle (ABP) model. Moreover, our investigation reveals that the density-dependent motility amplifies the slow-down of the directed speed, which facilitates phase-separation even at low packing fractions. The effective diffusivity shows a crossover from quadratic rise to a power-law behavior of exponent 3/2 with Pe in the phase-separated regime. Furthermore, we have shown that the effective diffusion decreases exponentially with packing fraction in the phase-separated regime, while it shows a linear decrease in the single phase regime.

Interspecies evolutionary dynamics mediated by public goods in bacterial quorum sensing

Bacterial quorum sensing is the communication that takes place between bacteria as they secrete certain molecules into the intercellular medium that later get absorbed by the secreting cells themselves and by others. Depending on cell density, this uptake has the potential to alter gene expression and thereby affect global properties of the community. We consider the case of multiple bacterial species coexisting, referring to each one of them as a genotype and adopting the usual denomination of the molecules they collectively secrete as public goods. A crucial problem in this setting is characterizing the coevolution of genotypes as some of them secrete public goods (and pay the associated metabolic costs) while others do not but may nevertheless benefit from the available public goods. We introduce a network model to describe genotype interaction and evolution when genotype fitness depends on the production and uptake of public goods. The model comprises a random graph to summarize the possible evolutionary pathways the genotypes may take as they interact genetically with one another, and a system of coupled differential equations to characterize the behavior of genotype abundance in time. We study some simple variations of the model analytically and more complex variations computationally. Our results point to a simple trade-off affecting the long-term survival of those genotypes that do produce public goods. This trade-off involves, on the producer side, the impact of producing and that of absorbing the public good. On the nonproducer side, it involves the impact of absorbing the public good as well, now compounded by the molecular compatibility between the producer and the nonproducer. Depending on how these factors turn out, producers may or may not survive.

Bose-Einstein-like condensation in scalar active matter with diffusivity edge

Due to their remarkable properties, systems that exhibit self-organization of their components resulting from intrinsic microscopic activity have been extensively studied in the last two decades. In a generic class of active matter, the interactions between the active components are represented via an effective density-dependent diffusivity in a mean-field single-particle description. Here, a class of scalar active matter is proposed by incorporating a diffusivity edge into the dynamics: when the local density of the system surpasses a critical threshold, the diffusivity vanishes. The effect of the diffusivity edge is studied under the influence of an external potential, which introduces the ability to control the behavior of the system by changing an effective temperature, which is defined in terms of the single-particle diffusivity and mobility. At a critical effective temperature, a system that is trapped by a harmonic potential is found to undergo a condensation transition, which manifests formal similarities to Bose-Einstein condensation.

Shared behavioral mechanisms underlie C. elegans aggregation and swarming

In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming-a dynamic phenotype only observed at longer timescales. Using fluorescence multi-worm tracking, we quantify aggregation in terms of individual dynamics and population-level statistics. Then we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules for aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation.