Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots

Discrete modes of social information processing predict individual behavior of fish in a group

Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an active mode in which they are sensitive to the swimming patterns of conspecifics, and a passive mode where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors behavior. At the group level, switching between active and passive modes is uncorrelated among fish, yet correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates, as well as to other species

A Model for Collective Dynamics in Ant Raids

Ant raiding, the process of identifying and returning food to the nest or bivouac, is a fascinating example of collective motion in nature. During such raids ants lay pheromones to form trails for others to find a food source. In this work a coupled PDE/ODE model is introduced to study ant dynamics and pheromone concentration. The key idea is the introduction of two forms of ant dynamics: foraging and returning, each governed by different environmental and social cues. The model accounts for all aspects of the raiding cycle including local collisional interactions, the laying of pheromone along a trail, and the transition from one class of ants to another. Through analysis of an order parameter measuring the orientational order in the system, the model shows self-organization into a collective state consisting of lanes of ants moving in opposite directions as well as the transition back to the individual state once the food source is depleted matching prior experimental results. This indicates that in the absence of direct communication ants naturally form an efficient method for transporting food to the nest/bivouac. The model exhibits a continuous kinetic phase transition in the order parameter as a function of certain system parameters. The associated critical exponents are found, shedding light on the behavior of the system near the transition.Comment: Preprint Version, 30 pgs., 18 figures, complete version with supplementary movies to appear in Journal of Mathematical Biology (Springer

Swimmers at Interfaces Enhance Interfacial Transport

The behavior of fluid interfaces far from equilibrium plays central roles in nature and in industry. Active swimmers trapped at interfaces can alter transport at fluid boundaries with far reaching implications. Swimmers can become trapped at interfaces in diverse configurations and swim persistently in these surface adhered states. The self-propelled motion of bacteria makes them ideal model swimmers to understand such effects. We have recently characterized the swimming of interfacially-trapped Pseudomonas aeruginosa PA01 moving in pusher mode. The swimmers adsorb at the interface with pinned contact lines, which fix the angle of the cell body at the interface and constrain their motion. Thus, most interfacially-trapped bacteria swim along circular paths. Fluid interfaces form incompressible two-dimensional layers, altering leading order interfacial flows generated by the swimmers from those in bulk. In our previous work, we have visualized the interfacial flow around a pusher bacterium and described the flow field using two dipolar hydrodynamic modes; one stresslet mode whose symmetries differ from those in bulk, and another bulk mode unique to incompressible fluid interfaces. Based on this understanding, swimmers-induced tracer displacements and swimmer-swimmer pair interactions are explored using analysis and experiment. The settings in which multiple interfacial swimmers with circular motion can significantly enhance interfacial transport of tracers or promotemixing of other swimmers on the interface are identified through simulations and compared to experiment. This study identifies important factors of general interest regarding swimmers on or near fluid boundaries, and in the design of biomimetic swimmers to enhance transport at interfacesComment: arXiv admin note: substantial text overlap with arXiv:2204.0230

Directional collective cell migration emerges as a property of cell interactions.

Collective cell migration is a fundamental process, occurring during embryogenesis and cancer metastasis. Neural crest cells exhibit such coordinated migration, where aberrant motion can lead to fatality or dysfunction of the embryo. Migration involves at least two complementary mechanisms: contact inhibition of locomotion (a repulsive interaction corresponding to a directional change of migration upon contact with a reciprocating cell), and co-attraction (a mutual chemoattraction mechanism). Here, we develop and employ a parameterized discrete element model of neural crest cells, to investigate how these mechanisms contribute to long-range directional migration during development. Motion is characterized using a coherence parameter and the time taken to reach, collectively, a target location. The simulated cell group is shown to switch from a diffusive to a persistent state as the response-rate to co-attraction is increased. Furthermore, the model predicts that when co-attraction is inhibited, neural crest cells can migrate into restrictive regions. Indeed, inhibition of co-attraction in vivo and in vitro leads to cell invasion into restrictive areas, confirming the prediction of the model. This suggests that the interplay between the complementary mechanisms may contribute to guidance of the neural crest. We conclude that directional migration is a system property and does not require action of external chemoattractants

Microbes in motion: a characterization of the surface properties conducive to bacterial swarms

Under the right conditions, bacteria can collectively move across a surface in a whirling, flagella-driven motion reminiscent of flocking birds or schooling fish. Known as a bacterial swarm, this phenomenon is exhibited by many different species and is the fastest way bacteria can colonize a surface. Yet, the prevalence of bacterial swarms in the wild is unknown, in part because the surface properties conducive to swarming have not been well characterized. This has made it difficult to identify the natural surfaces that might be conducive to swarming. In particular, the amount of liquid required for swarming and the effects of swarming biproducts on surface conditions have been misunderstood. The surface tension and stiffness of substrates used in swarming experiments have never been measured, nor has the sensitivity of swarms to gradual changes in their local environments, although it’s clear from experiments that even slight changes can stop a swarm. In the work presented below, I use confocal microscopy to measure the liquid profile at the front of swarms for four representative species, showing that swarms generally move along dry surfaces using only the liquid that collects around them via capillary action. I then use shadowgraphy to track the spread of surfactants from colonies of Bacillus subtilis and show that it occurs in two phases and that the rate of surfactant expansion is highest on moist and thin surfaces. To measure the effects of surfactants on a substrate, I use a custom-build cantilever and a nanoindenter to measure the changes in surface tension between surfactant-free agar gel and surfactant-covered agar gel and find that the surfactant produced by B. subtilis can reduce surface tension by nearly 40%. I also use nanoindentation to measure the Young’s Modulus of substrates conducive to swarming in B. subtilis and find that at around 8 kPa, B. subtilis cells sharply transition to a different kind of collective surface motility called sliding. Together, these results characterize the surface properties conducive to bacterial swarms.Physic

Putting ecological theories to the test : individual-based simulations of synthetic microbial community dynamics

Microbial communities are critical for the proper functioning of each and every ecosystem on Earth. The ability to understand the structure and functioning of these complex communities is crucial to manage and protect natural communities, as well as to rationally design engineered microbial communities for important applications ranging from medical and pharmaceutical uses to various bioindustrial processes. In recent years, synthetic microbial communities have gained increasing interest from microbiologists due to their reduced complexity and increased controllability, which favours them over more complex natural systems for examining ecological theories. In this thesis, the in silico counterpart of this approach was used to test ecological theories relating to biodiversity and functionality through the use of mathematical models. Models are abstractions of reality which allow for the testing of hypotheses in a controlled way. In this thesis, individual-based models of synthetic microbial communities were developed and used in simulation studies to answer research questions relating to community diversity, stability, productivity and functionality. The models are spatially explicit and track through time the characteristics, interactions and activities of every individual in the community. The modelling framework is flexible and thus also extendable to other avenues of research