Yilin Wu

Physics and engineering

A R Mears (Physics Bulletin August 1984 p306) presents an imagery of the physics/engineering duality suggesting almost total separation and ideals – in my view a large step backwards. About 1935 this was indeed the usual view.

Periodic reversal of direction allows Myxobacteria to swarm

Many bacteria can rapidly traverse surfaces from which they are extracting nutrient for growth. They generate flat, spreading colonies, called swarms because they resemble swarms of insects. We seek to understand how members of any dense swarm spread efficiently while being able to perceive and interfere minimally with the motion of others. To this end, we investigate swarms of the myxobacterium, Myxococcus xanthus. Individual M. xanthus cells are elongated; they always move in the direction of their long axis; and they are in constant motion, repeatedly touching each other. Remarkably, they regularly reverse their gliding directions. We have constructed a detailed cell- and behavior-based computational model of M. xanthus swarming that allows the organization of cells to be computed. By using the model, we are able to show that reversals of gliding direction are essential for swarming and that reversals increase the outflow of cells across the edge of the swarm. Cells at the swarm edge gain maximum exposure to nutrient and oxygen. We also find that the reversal period predicted to maximize the outflow of cells is the same (within the errors of measurement) as the period observed in experiments with normal M. xanthus cells. This coincidence suggests that the circuit regulating reversals evolved to its current sensitivity under selection for growth achieved by swarming. Finally, we observe that, with time, reversals increase the cell alignment, and generate clusters of parallel cells.

Social Interactions in Myxobacterial Swarming

Swarming, a collective motion of many thousands of cells, produces colonies that rapidly spread over surfaces. In this paper, we introduce a cell-based model to study how interactions between neighboring cells facilitate swarming. We chose to study Myxococcus xanthus, a species of myxobacteria, because it swarms rapidly and has well-defined cell–cell interactions mediated by type IV pili and by slime trails. The aim of this paper is to test whether the cell contact interactions, which are inherent in pili-based S motility and slime-based A motility, are sufficient to explain the observed expansion of wild-type swarms. The simulations yield a constant rate of swarm expansion, which has been observed experimentally. Also, the model is able to quantify the contributions of S motility and A motility to swarming. Some pathogenic bacteria spread over infected tissue by swarming. The model described here may shed some light on their colonization process.

Microbubbles reveal chiral fluid flows in bacterial swarms

Flagellated bacteria can swim within a thin film of fluid that coats a solid surface, such as agar; this is a means for colony expansion known as swarming. We found that micrometer-sized bubbles make excellent tracers for the motion of this fluid. The microbubbles form explosively when small aliquots of an aqueous suspension of droplets of a water-insoluble surfactant (Span 83) are placed on the agar ahead of a swarm, as the water is absorbed by the agar and the droplets are exposed to air. Using these bubbles, we discovered an extensive stream (or river) of swarm fluid flowing clockwise along the leading edge of an Escherichia coli swarm, at speeds of order 10 μm/s, about three times faster than the swarm expansion. The flow is generated by the action of counterclockwise rotating flagella of cells stuck to the substratum, which drives fluid clockwise around isolated cells (when viewed from above), counterclockwise between cells in dilute arrays, and clockwise in front of cells at the swarm edge. The river provides an avenue for long-range communication in the swarming colony, ideally suited for secretory vesicles that diffuse poorly. These findings broaden our understanding of swarming dynamics and have implications for the engineering of bacterial-driven microfluidic devices.

Water reservoir maintained by cell growth fuels the spreading of a bacterial swarm

Flagellated bacteria can swim across moist surfaces within a thin layer of fluid, a means for surface colonization known as swarming. This fluid spreads with the swarm, but how it does so is unclear. We used micron-sized air bubbles to study the motion of this fluid within swarms of Escherichia coli. The bubbles moved diffusively, with drift. Bubbles starting at the swarm edge drifted inward for the first 5 s and then moved outward. Bubbles starting 30 μm from the swarm edge moved inward for the first 20 s, wandered around in place for the next 40 s, and then moved outward. Bubbles starting at 200 or 300 μm from the edge moved outward or wandered around in place, respectively. So the general trend was inward near the outer edge of the swarm and outward farther inside, with flows converging on a region about 100 μm from the swarm edge. We measured cellular metabolic activities with cells expressing a short-lived GFP and cell densities with cells labeled with a membrane fluorescent dye. The fluorescence plots were similar, with peaks about 80 μm from the swarm edge and slopes that mimicked the particle drift rates. These plots suggest that net fluid flow is driven by cell growth. Fluid depth is largest in the multilayered region between approximately 30 and 200 μm from the swarm edge, where fluid agitation is more vigorous. This water reservoir travels with the swarm, fueling its spreading. Intercellular communication is not required; cells need only grow.

Osmotic pressure in a bacterial swarm.

Using Escherichia coli as a model organism, we studied how water is recruited by a bacterial swarm. A previous analysis of trajectories of small air bubbles revealed a stream of fluid flowing in a clockwise direction ahead of the swarm. A companion study suggested that water moves out of the agar into the swarm in a narrow region centered ∼ 30 μm from the leading edge of the swarm and then back into the agar (at a smaller rate) in a region centered ∼ 120 μm back from the leading edge. Presumably, these flows are driven by changes in osmolarity. Here, we utilized green/red fluorescent liposomes as reporters of osmolarity to verify this hypothesis. The stream of fluid that flows in front of the swarm contains osmolytes. Two distinct regions are observed inside the swarm near its leading edge: an outer high-osmolarity band (∼ 30 mOsm higher than the agar baseline) and an inner low-osmolarity band (isotonic or slightly hypotonic to the agar baseline). This profile supports the fluid-flow model derived from the drift of air bubbles and provides new (to our knowledge) insights into water maintenance in bacterial swarms. High osmotic pressure at the leading edge of the swarm extracts water from the underlying agar and promotes motility. The osmolyte is of high molecular weight and probably is lipopolysaccharide.

Self-organization in bacterial swarming: lessons from myxobacteria

When colonizing surfaces, many bacteria are able to self-organize into an actively expanding biofilm, in which millions of cells move smoothly and orderly at high densities. This phenomenon is known as bacterial swarming. Despite the apparent resemblance to patterns seen in liquid crystals, the dynamics of bacterial swarming cannot be explained by theories derived from equilibrium statistical mechanics. To understand how bacteria swarm, a central question is how order emerges in dense and initially disorganized populations of bacterial cells. Here we briefly review recent efforts, with integrated computational and experimental approaches, in addressing this question.

CA models of myxobacteria swarming

We develop two models for Myxobacteria swarming, a modified Lattice Gas Cellular Automata (LGCA) model and an off-lattice CA model In the LGCA model each cell is represented by one node for the center of mass and an extended rod-shaped cell profile Cells check the surrounding area and choose in which direction to move based on the local interactions Using this model, we obtained a density vs expansion rate curve with the shape similar to the experimental curve for the wild type Myxobacteria In the off-lattice model, each cell is represented by a string of nodes Cells can bend and move freely in the two-dimensional space We use a phenomenological algorithm to determine the moving direction of cells guided by slime trail; the model allows for cell bending and alignment during collisions In the swarming simulations for A+S- Myxobacteria, we demonstrate the formation of peninsula structures, in agreement with experiments.

Collective motion of bacteria in two dimensions

Collective motion can be observed in biological systems over a wide range of length scales, from large animals to bacteria. Collective motion is thought to confer an advantage for defense and adaptation. A central question in the study of biological collective motion is how the traits of individuals give rise to the emergent behavior at population level. This question is relevant to the dynamics of general self-propelled particle systems, biological self-organization, and active fluids. Bacteria provide a tractable system to address this question, because bacteria are simple and their behavior is relatively easy to control. In this mini review we will focus on a special form of bacterial collective motion, i.e., bacterial swarming in two dimensions. We will introduce some organization principles known in bacterial swarming and discuss potential means of controlling its dynamics. The simplicity and controllability of 2D bacterial behavior during swarming would allow experimental examination of theory predictions on general collective motion.