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Microbial interactions regulate their spread and survival in competitive environments. It is not clear if the physical parameters of the environment regulate the outcome of these interactions. In this work, we show that the opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the substratum in the presence of yeast such as Cryptococcus neoformans , than without it. At the microscopic level, bacterial cells show an enhanced activity in the vicinity of yeast cells. We observe this behaviour even when the live yeast cells are replaced with heat-killed cells or with spherical glass beads of similar morphology, which suggests that the observed behaviour is not specific to the biology of microbes. Upon careful investigation, we find that a fluid pool is formed around yeast cells which facilitates the swimming of the flagellated P. aeruginosa , causing their enhanced motility. Using mathematical modeling we demonstrate how this local enhancement of bacterial motility leads to the enhanced spread observed at the level of the plate. We find that the dynamics of the fluid landscape around the bacteria, mediated by the growing yeast lawn, affects the spreading. For instance, when the yeast lawn grows faster, a bacterial colony prefers a lower initial loading of yeast cells for optimum enhancement in the spread. We confirm our predictions using Candida albicans and C. neoformans, at different initial compositions. In summary, our work shows the importance of considering the dynamically changing physical environment while studying bacterial motility in complex environments., Comment: 14 pages of main text, 5 figures, 4 pages of SI added

Coarse-grained descriptions of collective motion of flocking systems are often derived for the macroscopic or the thermodynamic limit. However, many real flocks are small sized (10 to 100 individuals), called the mesoscopic scales, where stochasticity arising from the finite flock sizes is important. Developing mesoscopic scale equations, typically in the form of stochastic differential equations, can be challenging even for the simplest of the collective motion models. Here, we take a novel data-driven equation learning approach to construct the stochastic mesoscopic descriptions of a simple self-propelled particle (SPP) model of collective motion. In our SPP model, a focal individual can interact with k randomly chosen neighbours within an interaction radius. We consider k = 1 (called stochastic pairwise interactions), k = 2 (stochastic ternary interactions), and k equalling all available neighbours within the interaction radius (equivalent to Vicsek-like local averaging). The data-driven mesoscopic equations reveal that the stochastic pairwise interaction model produces a novel form of collective motion driven by a multiplicative noise term (hence termed, noise-induced flocking). In contrast, for higher order interactions (k > 1), including Vicsek-like averaging interactions, yield collective motion driven primarily by the deterministic forces. We find that the relation between the parameters of the mesoscopic equations describing the dynamics and the population size are sensitive to the density and to the interaction radius, exhibiting deviations from mean-field theoretical expectations. We provide semi-analytic arguments potentially explaining these observed deviations. In summary, our study emphasizes the importance of mesoscopic descriptions of flocking systems and demonstrates the potential of the data-driven equation discovery methods for complex systems studies.

Traffic and pedestrian systems consist of human collectives where agents are intelligent and capable of processing available information, to perform tactical manoeuvres that can potentially increase their movement efficiency. In this study, we introduce a social force model for agents that possess memory. Information of the agent's past affects the agent's instantaneous movement in order to swiftly take the agent towards its desired state. We show how the presence of memory is akin to an agent performing a proportional-integral control to achieve its desired state. The longer the agent remembers and the more impact the memory has on its motion, better is the movement of an isolated agent in terms of achieving its desired state. However, when in a collective, the interactions between the agents lead to non-monotonic effect of memory on the traffic dynamics. A group of agents with memory exiting through a narrow door exhibit more clogging with memory than without it. We also show that a very large amount of memory results in variation in the memory force experienced by agents in the system at any time, which reduces the propensity to form clogs and leads to efficient movement., Comment: This work was presented at the Traffic and Granular Flow 22 conference, held in New Delhi, India

Stochastic differential equations (SDEs) are an important framework to model dynamics with randomness, as is common in most biological systems. The inverse problem of integrating these models with empirical data remains a major challenge. Here, we present an equation discovery methodology that takes time series data as an input, analyses fine scale fluctuations and outputs an interpretable SDE that can correctly capture long-time dynamics of data. We achieve this by combining traditional approaches from stochastic calculus literature with state-of-the-art equation discovery techniques. We validate our approach on synthetic datasets, and demonstrate the generality and applicability of the method on two real-world datasets of vastly different spatiotemporal scales: (i) collective movement of fish school where stochasticity plays a crucial role, and (ii) confined migration of a single cell, primarily following a relaxed oscillation. We make the method available as an easy-to-use, open-source Python package, PyDaddy (Python Library for Data Driven Dynamics)., Comment: Updates: v3: Significantly reorganized the paper and added a section analysis of a cell migration dataset. v4: Update arXiv title to match the updated title of the manuscript. v5: Added sections detailing the limitations of the approach

Classic computational models of collective motion suggest that simple local averaging rules can promote many observed group level patterns. Recent studies, however, suggest that rules simpler than local averaging may be at play in real organisms; for example, fish stochastically align towards only one randomly chosen neighbour and yet the schools are highly polarised. Here, we ask -- how do organisms maintain group cohesion? Using a spatially-explicit model, inspired from empirical investigations, we show that group cohesion can be achieved even when organisms randomly choose only one neighbour to interact with. Cohesion is maintained even in the absence of local averaging that requires interactions with many neighbours. Furthermore, we show that choosing a neighbour randomly is a better way to achieve cohesion than interacting with just its closest neighbour. To understand how cohesion emerges from these random pairwise interactions, we turn to a graph-theoretic analysis of the underlying dynamic interaction networks. We find that randomness in choosing a neighbour gives rise to well-connected networks that essentially cause the groups to stay cohesive. We compare our findings with the canonical averaging models (analogous to the Vicsek model). In summary, we argue that randomness in the choice of interacting neighbours plays a crucial role in collective motion., Comment: For supplementary text: https://www.dannyraj.com/random-cohesion-supp-info

Most real world collectives, including active particles, living cells, and grains, are heterogeneous, where individuals with differing properties interact. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals, based on their observed movement patterns. However, the true individual properties may be masked by emergent effects in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects such as jamming and clustering affect individual motion, an agent's own movement does not have sufficient information to perform the classification well: a simple observer algorithm, based only on individual velocities cannot accurately estimate the level of heterogeneity of the system, and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent's motion is explicitly accounted for. We use insights about the physics of collective motion to quantify the effect of the neighbourhood on an agent using a neighbourhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately, and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighbourhood effects is often necessary to correctly infer intrinsic properties of individuals., Comment: Supplementary movies can be found in: https://www.dannyraj.com/obsinf-supp-info

In this article, we propose a traffic rule inspired from nature, that facilitates an elite agent to move efficiently through a crowd of inert agents. When an object swims in a fluid medium or an intruder is forced through granular matter, characteristic flow-fields are created around them. We show that if inert agents, made small movements based on a traffic rule derived from these characteristic flow-fields, they efficiently reorganize and transport enough space for the elite to pass through. The traffic rule used in the article is a dipole-field which satisfactorily captures the features of the flow-fields around a moving intruder. We study the effectiveness of this dipole traffic rule using numerical simulations in a 2D periodic domain, where one self-propelled elite agent tries to move through a crowd of inert agents that prefer to stay in a state of rest. Simulations are carried out for a wide range of strengths of the traffic rule and packing density of the crowd. We characterize and analyze four regions in the parameter space, free-flow, motion due to cooperation, frozen and collective drift regions, and discuss the consequence of the traffic rule in light of the collective behavior observed. We believe that the proposed method can be of use in a swarm of robots working in constrained environments., Comment: For supplementary information see: https://www.dannyraj.com/mtc-supp-info