Francis G. Woodhouse

Confinement stabilizes a bacterial suspension into a spiral vortex.

Confining surfaces play crucial roles in dynamics, transport, and order in many physical systems, but their effects on active matter, a broad class of dynamically self-organizing systems, are poorly understood. We investigate here the influence of global confinement and surface curvature on collective motion by studying the flow and orientational order within small droplets of a dense bacterial suspension. The competition between radial confinement, self-propulsion, steric interactions, and hydrodynamics robustly induces an intriguing steady single-vortex state, in which cells align in inward spiraling patterns accompanied by a thin counterrotating boundary layer. A minimal continuum model is shown to be in good agreement with these observations.

Cytoplasmic streaming in plant cells emerges naturally by microfilament self-organization

Many cells exhibit large-scale active circulation of their entire fluid contents, a process termed cytoplasmic streaming. This phenomenon is particularly prevalent in plant cells, often presenting strikingly regimented flow patterns. The driving mechanism in such cells is known: myosin-coated organelles entrain cytoplasm as they process along actin filament bundles fixed at the periphery. Still unknown, however, is the developmental process that constructs the well-ordered actin configurations required for coherent cell-scale flow. Previous experimental works on streaming regeneration in cells of Characean algae, whose longitudinal flow is perhaps the most regimented of all, hint at an autonomous process of microfilament self-organization driving the formation of streaming patterns during morphogenesis. Working from first principles, we propose a robust model of streaming emergence that combines motor dynamics with both microscopic and macroscopic hydrodynamics to explain how several independent processes, each ineffectual on its own, can reinforce to ultimately develop the patterns of streaming observed in the Characeae and other streaming species.

Membrane Viscosity Determined from Shear-Driven Flow in Giant Vesicles

The viscosity of lipid bilayer membranes plays an important role in determining the diffusion constant of embedded proteins and the dynamics of membrane deformations, yet it has historically proven very difficult to measure. Here we introduce a new method based on quantification of the large-scale circulation patterns induced inside vesicles adhered to a solid surface and subjected to simple shear flow in a microfluidic device. Particle image velocimetry based on spinning disk confocal imaging of tracer particles inside and outside of the vesicle and tracking of phase-separated membrane domains are used to reconstruct the full three-dimensional flow pattern induced by the shear. These measurements show excellent agreement with the predictions of a recent theoretical analysis, and allow direct determination of the membrane viscosity.

Ferromagnetic and antiferromagnetic order in bacterial vortex lattices

Despite their inherent non-equilibrium nature1, living systems can self-organize in highly ordered collective states2,3 that share striking similarities with the thermodynamic equilibrium phases4,5 of conventional condensed matter and fluid systems. Examples range from the liquid-crystal-like arrangements of bacterial colonies6,7, microbial suspensions8,9 and tissues10 to the coherent macro-scale dynamics in schools of fish11 and flocks of birds12. Yet, the generic mathematical principles that govern the emergence of structure in such artificial13 and biological6–9,14 systems are elusive. It is not clear when, or even whether, well-established theoretical concepts describing universal thermostatistics of equilibrium systems can capture and classify ordered states of living matter. Here, we connect these two previously disparate regimes: Through microfluidic experiments and mathematical modelling, we demonstrate that lattices of hydrodynamically coupled bacterial vortices can spontaneously organize into distinct phases of ferro- and antiferromagnetic order. The preferred phase can be controlled by tuning the vortex coupling through changes of the inter-cavity gap widths. The emergence of opposing order regimes is tightly linked to the existence of geometry-induced edge currents15,16, reminiscent of those in quantum systems17–19. Our experimental observations can be rationalized in terms of a generic lattice field theory, suggesting that bacterial spin networks belong to the same universality class as a wide range of equilibrium systems.

Shear-driven circulation patterns in lipid membrane vesicles

Recent experiments have shown that when a near-hemispherical lipid vesicle attached to a solid surface is subjected to a simple shear flow it exhibits a pattern of membrane circulation much like a dipole vortex. This is in marked contrast to the toroidal circulation that would occur in the related problem of a drop of immiscible fluid attached to a surface and subjected to shear. This profound difference in flow patterns arises from the lateral incompressibility of the membrane, which restricts the observable flows to those in which the velocity field in the membrane is two-dimensionally divergence free. Here we study these circulation patterns within the simplest model of membrane fluid dynamics. A systematic expansion of the flow field based on Papkovich–Neuber potentials is developed for general viscosity ratios between the membrane and the surrounding fluids. Comparison with experimental results (Vezy, Massiera & Viallat, Soft Matt. , vol. 3, 2007, pp. 844–851) is made, and it is shown how such studies could allow measurements of the membrane viscosity. Issues of symmetry-breaking and pattern selection are discussed.

Predicting Knee Osteoarthritis

Treatment options for osteoarthritis (OA) beyond pain relief or total knee replacement are very limited. Because of this, attention has shifted to identifying which factors increase the risk of OA in vulnerable populations in order to be able to give recommendations to delay disease onset or to slow disease progression. The gold standard is then to use principles of risk management, first to provide subject-specific estimates of risk and then to find ways of reducing that risk. Population studies of OA risk based on statistical associations do not provide such individually tailored information. Here we argue that mechanistic models of cartilage tissue maintenance and damage coupled to statistical models incorporating model uncertainty, united within the framework of structural reliability analysis, provide an avenue for bridging the disciplines of epidemiology, cell biology, genetics and biomechanics. Such models promise subject-specific OA risk assessment and personalized strategies for mitigating or even avoiding OA. We illustrate the proposed approach with a simple model of cartilage extracellular matrix synthesis and loss regulated by daily physical activity.

Stochastic cycle selection in active flow networks

Significance Nature often uses interlinked networks to transport matter or information. In many cases, the physical or virtual flows between network nodes are actively driven, consuming energy to achieve transport along different links. However, there are currently very few elementary principles known to predict the behavior of this broad class of nonequilibrium systems. Our work develops a generic foundational understanding of mass-conserving active flow networks. By merging previously disparate concepts from mathematical graph theory and physicochemical reaction rate theory, we relate the self-organizing behavior of actively driven flows to the fundamental topological symmetries of the underlying network, resulting in a class of predictive models with applicability across many biological scales. Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Short-term consolidation of articular cartilage in the long-term context of osteoarthritis

Over ten percent of the population are afflicted by osteoarthritis, a chronic disease of diarthrodial joints such as the knees and hips, costing hundreds of billions of dollars every year. In this condition, the thin layers of articular cartilage on the bones degrade and weaken over years, causing pain, stiffness and eventual immobility. The biggest controllable risk factor is long-term mechanical overloading of the cartilage, but the disparity in time scales makes this process a challenge to model: loading events can take place every second, whereas degradation occurs over many months. Therefore, a suitable model must be sufficiently simple to permit evaluation over long periods of variable loading, yet must deliver results sufficiently accurate to be of clinical use, conditions unmet by existing models. To address this gap, we construct a two-component poroelastic model endowed with a new flow restricting boundary condition, which better represents the joint space environment compared to the typical free-flow condition. Under both static and cyclic loading, we explore the rate of gradual consolidation of the medium. In the static case, we analytically characterise the duration of consolidation, which governs the duration of effective fluid-assisted lubrication. In the oscillatory case, we identify a region of persistent strain oscillations in otherwise consolidated tissue, and derive estimates of its depth and magnitude. Finally, we link the two cases through the concept of an equivalent static stress, and discuss how our results help explain the inexorable cartilage degeneration of osteoarthritis.

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding and virus evolution to brain activity and neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. While efficient algorithms for cluster detection and data completion in high-dimensional spaces have been developed and applied over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here, we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. Our approach combines Gaussian mixture approximations and self-consistent dimensionality reduction with minimal-energy path estimation and multi-dimensional transition-state theory. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein folding transitions and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data and phylogenetic trees. The underlying numerical protocol thus allows the recovery of relevant dynamical information from instantaneous ensemble measurements, effectively alleviating the need for time-dependent data in many situations. Owing to its generic structure, the framework introduced here will be applicable to modern cryo-electron-microscopy and high-throughput single-cell RNA sequencing data and can guide the design of new experimental approaches towards studying complex multiphase phenomena.

Measurement of Lipid Bilayer Viscosity by Microfluidic Shear Transmission

We use an experimental method for measuring shear transmission through an anchored hemispherical vesicle to estimate the viscosity for several different lipid membranes. We also validate predicted flow patterns and confirm a recently calculated universal ratio [1] for the flow geometry. Comparing experimental with calculated results provides insight about how membrane viscosity contributes to flow patterns in biological configurations, such as characean algae cells, where a lipid membrane experiences strong shear flow [2]. We apply a similar flow to vesicles containing an electrostatically coupled actin cortex in order to observe the effects of external flow on reorganization of proteins inside the vesicle.References1. F.G. Woodhouse and R.E. Goldstein, Shear driven circulation patterns in lipid membrane vesicles, Journal of Fluid Mechanics, 705, 165-175 (2012)2. K. Wolff, D. Marenduzzo, and M.E. Cates. Cytoplasmic streaming in plant cells: the role of wall slip. Journal of the Royal Society Interface, v. 9 n. 71 p. 1398 (2012).