The formation of platelet aggregates during blood clotting is modeled on two scales using ideas motivated by Peskin's Immersed Boundary Method. The microscopic scale models track individual platelets, their mechanical interactions with one another and the surrounding fluid, their detection of and response to chemical activators, and the formation of cohesive and adhesive 'links' between platelets and between platelets and the vascular wall. These models allow inclusion of detailed mechanisms of binding-unbinding, platelet stimulus-response, and chemistry on the platelets' surfaces. The macroscopic scale models treat the same interactions in terms of concentrations of platelets and distributions of cohesive and adhesive links, and can be used to study platelet aggregation in vessels of clinical interest including the coronary and cerebral arteries. In both types of model, the development of platelet aggregates affects the fluid motion only through an evolving fluid force density, and consequently, Cartesian grid methods are effective in solving the model equations.

A computational model of finite-length undulatory swimmers is used to examine the physical origin of the effect of elasticity on swimming speed. We explore two distinct target swimming strokes: one derived from the motion of Caenorhabditis elegans, with large head undulations, and a contrasting stroke with large tail undulations. We show that both favorable stroke asymmetry and swimmer elasticity contribute to a speed-up, but a substantial boost results only when these two effects work together. We reproduce conflicting results from the literature simply by changing relevant physical parameters.

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A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. locomotion | coupled oscillators | phase locking | metachronal waves I t is widely believed that neural circuits have evolved to optimize behavior that increases reproductive fitness. Despite this belief, few studies have clearly identified the neural mechanisms producing optimal behaviors. The complexity of behaviors generally makes it difficult to assess their optimality, and neural circuits are often too complicated to concretely link neural mechanisms to the overt behavior. Energy-intensive locomotion such as steady swimming, walking, and flying provides important model systems for studying optimality because the goal of the behavior is clear and it is likely to have been optimized for efficiency (1). For example, the kinematics of locomotion has been shown to be optimal in the cases of the undulatory motion of the sandfish lizard and the lamprey (2, 3). On the other hand, the neural circuits underlying locomotion in most organisms are not sufficiently characterized to understand how they give rise to the optimal motor behavior. Because of the distinct frequency-invariant stroke pattern and the relative simplicity of the neuronal circuit, limb coordination of long-tailed crustaceans during steady swimming provides an ideal model system ...

This work examines the relationship between spatio-temporal coordination of intracellular flow and traction stress and the speed of amoeboid locomotion of microplasmodia of Physarum polycephalum. We simultaneously perform particle image velocimetry and traction stress microscopy to measure the velocity of cytoplasmic flow and the stresses applied to the substrate by migrating Physarum microamoebae. In parallel, we develop a mathematical model of a motile cell which includes forces from the viscous cytosol, a poro-elastic, contractile cytoskeleton and adhesive interactions with the substrate. Our experiments show that flow and traction stress exhibit back-to-front-directed waves with a distinct phase difference. The model demonstrates that the direction and speed of locomotion are determined by this coordination between contraction, flow and adhesion. Using the model, we identify forms of coordination that generate model predictions consistent with experiments. We demonstrate that this coordination produces near optimal migration speed and is insensitive to heterogeneity in substrate adhesiveness. While it is generally thought that amoeboid motility is robust to changes in extracellular geometry and the nature of extracellular adhesion, our results demonstrate that coordination of adhesive forces is essential to producing robust migration.