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Register a new userPhase transitions during fruiting body formation in Myxococcus xanthus
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The formation of a collectively moving group benefits individuals within a population in a variety of ways such as ultra-sensitivity to perturbation, collective modes of feeding, and protection from environmental stress. While some collective groups use a single organizing principle, others can dynamically shift the behavior of the group by modifying the interaction rules at the individual level. The surface-dwelling bacterium Myxococcus xanthus forms dynamic collective groups both to feed on prey and to aggregate during times of starvation. The latter behavior, termed fruiting-body formation, involves a complex, coordinated series of density changes that ultimately lead to three-dimensional aggregates comprising hundreds of thousands of cells and spores. This multi-step developmental process most likely involves several different single-celled behaviors as the population condenses from a loose, two-dimensional sheet to a three-dimensional mound. Here, we use high-resolution microscopy and computer vision software to spatiotemporally track the motion of thousands of individuals during the initial stages of fruiting body formation. We find that a combination of cell-contact-mediated alignment and internal timing mechanisms drive a phase transition from exploratory flocking, in which cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into streams, which act as slow-spreading, quasi-one-dimensional nematic fluids. These observations lead us to an active liquid crystal description of the myxobacterial development cycle.
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Combining high-resolution single cell tracking experiments with numerical simulations, we show that starvation-induced fruiting body (FB) formation in Myxococcus xanthus is a phase separation driven by cells that tune their motility over time. The phase separation can be understood in terms of cell density and a dimensionless Peclet number that captures cell motility through speed and reversal frequency. Our work suggests that M. xanthus take advantage of a self-driven non-equilibrium phase transition that can be controlled at the single cell level.
Formation of spatial patterns of cells is a recurring theme in biology and often depends on regulated cell motility. Motility of M. xanthus depends on two motility machineries: the S-engine and A-engine. Moving M. xanthus cells can organize into spreading colonies or spore-filled fruiting bodies depending on their nutritional status. To understand these two pattern formation processes and the contributions by the two motility machineries, as well as cell reversal, we analyze spatial self-organization in 3 strains: i) a mutant that moves unidirectionally without reversing by the A-motility system only, ii) a unidirectional mutant that is also equipped with the S-motility system, and iii) the wild-type that, in addition to the two motility systems, reverses its direction of movement. The mutant moving by the A-engine illustrates that collective motion in the form of large moving clusters can arise in gliding bacteria due to steric interactions of the rod-shaped cells, without the need of invoking any biochemical signal regulation. The two-engine strain mutant reveals that the same phenomenon emerges when both motility systems are present, and as long as cells exhibit unidirectional motion only. From the study of these two strains, we conclude that unidirectional cell motion induces the formation of large moving clusters at low and intermediate densities, while it results into vortex formation at very high densities. These findings are consistent with what is known from self-propelled rods which strongly suggests that the combined effect of self-propulsion and volume exclusion interactions is the pattern formation mechanism leading to the observed phenomena. In addition, we learn that when cells reverse, as observed in the wild-type, cells form small but strongly elongated clusters and self-organize into a mesh-like structure at high enough densities.
Myxococcus xanthus is a model organism for studying bacterial social behaviors due to its ability to form complex multi-cellular structures. Knowledge of M. xanthus surface gliding motility and the mechanisms that coordinate it are critically important to our understanding of collective cell behaviors. Although the mechanism of gliding motility is still under investigation, recent experiments suggest that there are two possible mechanisms underlying force production for cell motility: the focal adhesion mechanism and the helical rotor mechanism which differ in the biophysics of the cell-substrate interactions. Whereas the focal adhesion model predicts an elastic coupling, the helical rotor model predicts a viscous coupling. Using a combination of computational modeling, imaging, and force microscopy, we find evidence for elastic coupling in support of the focal adhesion model. Using a biophysical model of the M. xanthus cell, we investigated how the mechanical interactions between cells are affected by interactions with the substrate. Comparison of modeling results with experimental data for cell-cell collision events pointed to a strong, elastic attachment between the cell and substrate. These results are robust to variations in the mechanical and geometrical parameters of the model. We then directly measured the motor-substrate coupling by monitoring the motion of optically trapped beads and find that motor velocity decreases exponentially with opposing load. At high loads, motor velocity approaches zero velocity asymptotically and motors remain bound to beads indicating a strong, elastic attachment.
We introduce a model for the global optimization problem of nectar harvesting by flower visitors, e.g., nectar-feeding bats, as a generalization of the (multiple) traveling-salesperson problem (TSP). The model includes multiple independent animals and many flowers with time-dependent content. This provides an ensemble of realistic combinatorial optimization problems, in contrast to previously studied models like random Satisfiability or standard TSP. We numerically studied the optimum harvesting of these foragers, with parameters obtained from experiments, by using genetic algorithms. For the distribution of travel distances, we find a power-law (or Levy) distribution, as often found for natural foragers. Note, in contrast to many models, we make no assumption about the nature of the flight-distance distribution, the power law just emerges. This is in contrast to the TSP, where we find in the present study an exponential tail. Furthermore, the optimization problem exhibits a {phase transition}, similar to the TSP, at a critical value for the amount of nectar which can be harvested. This phase transition coincides with a dramatic increase in the typical running time of the optimization algorithm. For the value of the critical exponent nu, describing the divergence of the correlation length, we find nu=1.7(4), which is on the other hand compatible with the value found for the TSP. Finally, we also present data from field experiments in Costa Rica for the resource use for freely visiting flower bats. We found that the temporal patterns in experiments and model agree remarkably, confirming our model. Also the data show that the bats are able to memorize the positions of food sources and optimize, at least partially, their routes.
For group-living animals, reaching consensus to stay cohesive is crucial for their fitness, particularly when collective motion starts and stops. Understanding the decision-making at individual and collective levels upon sudden disturbances is central in the study of collective animal behavior, and concerns the broader question of how information is distributed and evaluated in groups. Despite the relevance of the problem, well-controlled experimental studies that quantify the collective response of groups facing disruptive events are lacking. Here we study the behavior of groups of uninformed individuals subject to the departure and stop of a trained conspecific within small-sized groups. We find that the groups reach an effective consensus: either all uninformed individuals follow the trained one (and collective motion occurs) or none does it. Combining experiments and a simple mathematical model we show that the observed phenomena results from the interplay between simple mimetic rules and the characteristic duration of the stimulus, here, the time the trained individual is moving away. The proposed mechanism strongly depends on group size, as observed in the experiments, and though group splitting can occur, the most likely outcome is always a coherent collective group response (consensus). The prevalence of a consensus is expected even if the groups of naives face conflicting information, e.g. if groups contain two subgroups of trained individuals, one trained to stay and one trained to leave. Our results indicate that collective decision-making and consensus in (small) animal groups are likely to be self-organized phenomena that do not involve concertation or even communication among the group members.
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