Mature epithelial tissues have distinct cellular architecture, which is maintained despite externally applied forces, wounding, and cell division or death. Here we investigate how a model tissue develops and maintains cellular structure by quantifying single cell dynamics and cell shape in newly formed monolayers of MDCK cells. Cells initially aggregate through a process resembling wound healing into a confluent monolayer with elongated cells that remain motile. After formation, individual monolayers evolve over time to reach a similar final state with more hexagonal cell shapes and arrested dynamics, resembling mature epithelial tissues. By quantifying cell trajectories, we observe glassy dynamics controlled by cell shape, which have been previously predicted by vertex models. On substrates of different stiffness, monolayers form and evolve with different cell number density but the same relationship between cell shape and speed suggesting that the dynamics are density independent. We find when inhibiting several regulators of the actin cytoskeleton that cell speed and shape remain correlated but the correlation is shifted toward more elongated cell shapes. The magnitude of this shift differs for each inhibitor but velocity correlation length decreases proportionately to the change in final cell shape. We show that these results can be recapitulated in vertex models which incorporate polarization coupling between neighboring cells. Our results demonstrate that multicellular coordination of cell motility plays an important role in regulation of cell shape and determination of final tissue structure.

Many of the big problems we are facing involve far from equilibrium systems that entail a cataclysmic change. Climate, turbulence and earthquakes, developmental biology, evolution and even aging and death. These phenomena are rare (sometimes occurring only once) and are entirely irreversible. While understanding the physics of such irreversible processes is of both fundamental and practical importance, these problems also pose unique challenges. These challenges, as they manifest in turbulence, were beautifully portrayed by Richardson:

“Big whirls have little whirls that feed on their velocity, and little whirls have lesser whirls and so on to viscosity” Lewis Fry Richardson (1922)

In his short verse, Richardson captures the essence of the turbulent cascade—the conveyance of kinetic energy across scales that underlies the universal dynamics of turbulent flows. Indeed, such conveyance of important physical quantities (energy, stress, frustration and even information) down and up a vast range of scales underlines the dynamics of many systems. The same applies to how a multi-contact frictional interface will form and break or how correlated defect structures determine the strength of a space-rocket, how an intricate network of creases will form when we crumple a thin sheet or when soda can is smashed. The challenge in understanding these systems is in capturing the events as they occur, keeping up with the dynamics on all scales and at all times. Here, I will review our work on several key irreversible system and introduce the new tools we developed to address their unique evolution and discuss the interesting physics we learned.

Multicomponent systems are ubiquitous in nature and industry. While the physics of binary and ternary liquid mixtures is well-understood, the thermodynamic and kinetic properties of N-component mixtures with N>3 have remained relatively unexplored. Inspired by recent examples of intracellular phase separation, we investigate equilibrium phase behavior and morphology of N-component liquid mixtures within the Flory-Huggins theory of regular solutions. In order to determine the number of coexisting phases and their compositions, we developed a new algorithm for constructing complete phase diagrams, based on numerical convexification of the discretized free energy landscape. Together with a Cahn-Hilliard approach for kinetics, we employ this method to study mixtures with N=4 and 5 components. In this talk I will discuss both the coarsening behavior of such systems, as well as the resulting morphologies in 3D. I will also mention how the number of coexisting phases and their compositions can be extracted with Principal Component Analysis (PCA) and K-Means clustering algorithms. Finally, I will discuss how one can reverse engineer the interaction parameters and volume fractions of components in order to achieve a range of desired packing structures, such as nested "Russian dolls" and encapsulated Janus droplets.

Buckling and fracture are ubiquitous phenomena that, despite having been studied for centuries, still pose many interesting conceptual and practical challenges. In this talk, I will summarize recent experimental and theoretical work that aims to understand the role of curvature and torsion in wrinkling and fragmentation processes. First, we will show how changes in curvature can induce phase transitions [1] and topological defects [2] in the wrinkling patterns on curved elastic surfaces. In the second part, we will revisit an observation by Feynman who noted that spaghetti appears to fragment into at least three (but hardly ever two) pieces when placed under large bending stresses. Using a combination of experiments, simulations and analytical scaling arguments, we will demonstrate how twist can be used to control binary fracture of brittle elastic rods [3].

[1] Nature Materials 14, 337 (2015) [2] PRL 116: 104301 (2016) [3] PNAS 115: 8665 (2018)

Non-spherical particles in turbulent flows are important in a wide range of problems including ice crystals in clouds, fibers in paper-making, marine plankton, and additives for turbulent drag reduction. We have developed experimental methods for precise tracking of the position and orientation of non-spherical particles in intense 3D turbulence. Using 3D printed particles, we can fabricate a wide range of shapes and explore how particle orientation and rotation are affected by particle shape. We find particles are strongly aligned by the turbulence. A simple picture in which particles are aligned by the fluid stretching they experience explains many of the key observations about how particles align and rotate. This same picture sheds new light on some old problems about how vorticity aligns with the strain rate tensor in turbulent flows. It has also allowed us to create a fascinating particle shape which we call a chiral dipole that shows a preferential rotation direction in isotropic turbulent flow.

Active fluids are a novel class of non-equilibrium complex fluids with examples across a wide range of biological and physical systems such as flocking animals, swarming microorganisms, vibrated granular rods, and suspensions of synthetic colloidal swimmers. Different from familiar non-equilibrium systems where free energy is injected from boundaries, an active fluid is a dispersion of large numbers of self-propelled units, which convert the ambient/internal free energy and maintain non-equilibrium steady states at microscopic scales. Due to this distinct feature, active fluids exhibit fascinating and unusual behaviors unseen in conventional complex fluids. Here, combining high-speed confocal microscopy, holographic imaging, rheological measurements and biochemical engineering, we experimentally investigate the dynamics of active fluids. In particular, we use E. coli suspensions as our model system and illustrate three unique properties of active fluids, i.e., (i) abnormal rheology, (ii) enhanced diffusion of passive tracers and (iii) emergence of collective swarming. Using theoretical tools of fluid mechanics and statistical mechanics, we develop a quantitative understanding of these interesting behaviors. Our study illustrates the general organizing principles of active fluids that can be exploited for designing “smart” fluids with controllable fluid properties. Our results also shed new light on fundamental transport processes in microbiological systems.

In 1939, Walter Cannon wrote in his book The Wisdom of the Body: “The living being is an agency of such sort that each disturbing influence induces by itself the calling forth of compensatory activity to neutralize or repair the disturbances”. Since this remarkable statement that postulates the use of feedback control to support life, we have come to appreciate that the use of feedback loops is ubiquitous at every level of biological organization, from the gene to the ecosystem. In this talk, we introduce a technology to study feedback operation in endogenous biological systems. We also discuss some recent progress in building feedback control systems with biological molecules that can modulate the operation of cellular pathways.

We usually design materials to target desired behaviors defined in a top-down manner. Learning theory offers an alternative where desired behaviors are defined by a list of examples. In learning, a material changes as it physically experiences such examples. We then test the material to see if it has the “correct” response to novel conditions never seen before (‘generalization’). Can real materials ‘learn’ from their history in this manner? We study the physical requirements for such information processing in terms of disorder, non-equilibrium driving and non-linearities using theory and experiments in disordered sheets, elastic networks, and molecular self-assembly.

My work centers on turbulence, both its intrinsic properties and its role in various environmental settings. Over a bed of sand, it lifts and transports the grains. Left to itself, the turbulence slowly dissipates and disappears. In the first part of my talk, I will introduce experiments motivated by the question of how quickly turbulence consumes kinetic energy. Surprisingly we do not generally know how to predict the consumption rate, though the process underlies general turbulence phenomena and modeling. What we found is that the rate is invariant with respect to changes in the intensity of the turbulence, so long as the flow is slow relative to the speed of sound. I will introduce a new experiment in which we observe how the picture changes when the flow is no longer so slow. In the second part of my talk, I describe an experiment motivated by the question of how turbulence deforms granular beds. The experiments reveal a new mechanism that produces bedforms, a mechanism associated with fluctuating pressure gradients generated in a fluid-saturated particle bed by a plate oscillating in the water above it.

Luria and Delbrück discovered that mutations that occur early during a growth process lead to exceptionally large mutant clones. These mutational “jackpot” events are thought to dominate the genetic diversity of growing cellular populations, including biofilms, solid tumors and developing embryos. In my talk I show that jackpot events can be generated not only when mutations arise early but also when they occur at favourable locations, which exacerbates their role in adaptation and disease. I will also consider the impact of recurrent jackpot events, which lead to a bias favoring alleles that happen to be present in the majority of the population. I argue that this peculiar rich-get-richer phenomenon is a general feature of evolution driven by rare events.

Cellular structures constantly consume and dissipate energy on a variety of spatiotemporal scales in order to function. While progress has been made in elucidating their organizing principles, much of their thermodynamics remains unknown. In this talk, I will address the question: why measure dissipation in such nonequilibrium systems? I will show that by measuring a multi-scale irreversibility (time-reversal asymmetry) one can extract model-independent estimates of the time-scales of energy dissipation based on time series data collected in an experimental biological system. I further demonstrate that the irreversibility measure maintains a monotonic relationship with the underlying biological nonequilibrium activity. The basic idea of estimating irreversibility for various levels of coarse-graining is quite general; we expect it to lead to important inferences whenever there is a well-defined notion of dissipative scale.

While the evaporation of a single component droplet meanwhile is pretty well understood, the richness of phenomena in multicomponent droplet evaporation keeps surprising us. In this talk we will show and explain several of such phenomena, namely evaporation-triggered segregation thanks to either weak solutal Marangoni flow or thanks to gravitational effects, and the evaporation of ternary liquid droplet, which can lead to spontaneous nucleation of droplets consisting of a new phase. We will also show how this new phase can be utilized to self-lubricate the droplet in order to suppress the coffee stain effects. The research work shown in this talk combines experiments, numerical simulations, and theory.

Deep neural networks have proved to be extremely effective in image recognition, machine translation, and a variety of other data centered engineering tasks. However, generalizing neural networks to learning physical systems requires a careful examination of how they reflect symmetries. In this talk we give an overview of recent developments in the field of covariant/equivariant neural networks. Specifically, we focus on three applications: learning properties of chemical compounds from their molecular structure, image recognition on the sphere, and learning force fields for molecular dynamics. The work presented in this talk was done in collaboration with Brandon Anderson, Zhen Lin, Truong Son Hy, Horace Pan, and Shubhendu Trivedi.

A major unresolved question in microbiome research is whether the complex ecological patterns observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly and a lack of theoretical tools for modeling diverse ecosystems. In the first part of the talk, I will present a simple ecological model of microbial communities that reproduces large-scale ecological patterns observed across multiple experimental settings including compositional gradients, clustering by environment, diversity/harshness correlations, and nestedness. Surprisingly, our model works despite having a “random metabolisms” and “random consumer preferences”. This raises the natural of question of why random ecosystems can describe real-world experimental data. In the second, more theoretical part of the talk, I will answer this question by showing that when a community becomes diverse enough, it will always self-organize into a stable state whose properties are well captured by a “typical random ecosystems”. If time permits, I will also highlight surprising connections between ecological dynamics, constrained optimization, and kernel-based machine learning methods such as Support Vector Machines.

Talk is based on: Advani et al J. Stat. Phys (2018); Golford et al Science (2018); Marsland et al. PLoS Comp Bio (2019); arXiv:1809.04221;arXiv:1901.09673; arXiv:1904.02610; unpublished

Physical models that describe the dynamics of matter, whether they be discrete, like those for interacting particles or dust, or continuum models, like those for fluids and plasmas, possess structure. Structure may manifest by sets of conservation laws resulting from Galilean or Poincare invariance, or by the property of entropy production giving relaxation to thermal equilibrium. Ultimately, structure arises from an underlying Hamiltonian form that may or may not be maintained in approximations and/or reductions of various kinds.

I will survey the Hamiltonian structure possessed by a variety of models, with an emphasis on a general magnetofluid model and Vlasov-Maxwell theory. In addition I will discuss structure preservation in numerical implementation. Although symplectic integration has been well studied and widely used for finite-dimensional systems, the preservation of the structure that occurs in continuum models such as extended magnetohydrodynamics with generalized helicities, is considerably more difficult to implement. Progress in developing a discrete version of the Maxwell-Vlasov system that preserves its Hamiltonian structure, and its numerical implementation will be discussed.

Cells live in communities where they interact with each other and their environment. By coordinating individuals, such interactions often result in collective behavior that emerge on scales larger than the individuals that are beneficial to the population. At the same time, populations of individuals, even isogenic ones, display phenotypic heterogeneity, which diversifies individual behavior and enhances the resilience of the population in unexpected situations. This raises a dilemma: although individuality provides advantages, it also tends to reduce coordination. I will report on our recent experimental and theoretical efforts that use bacterial chemotaxis as model system to understand, the origin of individual cellular behavior and performance, and how populations of cells reconciliate individuality with group behavior.

Many organisms fly in order to survive and reproduce. My lab focusses on understanding bird flight to improve flying robots—because birds fly further, longer, and more reliable in complex visual and wind environments. I use this multidisciplinary lens that integrates biomechanics, aerodynamics, and robotics to advance our understanding of the evolution of flight more generally across birds, bats, insects, and autorotating seeds. The development of flying organisms as an individual and their evolution as a species are shaped by the physical interaction between organism and surrounding air. The organism’s architecture is tuned for propelling itself and controlling its motion. Flying animals and plants maximize performance by generating and manipulating vortices. These vortices are created close to the body as it is driven by the action of muscles or gravity, then are ‘shed’ to form a wake (a trackway left behind in the fluid). I study how the organism’s architecture is tuned to utilize these and other aeromechanical principles to compare the function of bird wings to that of bat, insect, and maple seed wings. The experimental approaches range from making robotic models to training birds to fly in a custom-designed wind tunnel as well as in visual flight arena’s—and inventing methods to 3D scan birds and measure the aerodynamic force they generate—nonintrusively—with a novel aerodynamic force platform. The studies reveal that animals and plants have converged upon the same solution for generating high lift: A strong vortex that runs parallel to the leading edge of the wing, which it sucks upward. Why this vortex remains stably attached to flapping animal and spinning plant wings is elucidated and linked to kinematics and wing morphology. While wing morphology is quite rigid in insects and maple seeds, it is extremely fluid in birds. I will show how such ‘wing morphing’ significantly expands the performance envelope of birds during flight, and will dissect the mechanisms that enable birds to morph better than any aircraft can. Finally, I will show how these findings have inspired my students to design new flapping and morphing aerial robots.

The soil dwelling bacterium Myxococcus xanthus is an amazing organism that uses collective motility to hunt in giant packs when near prey and to form beautiful and protective macroscopic structures comprising millions of cells when food is scarce. I will present an overview of how these cells move and how they regulate that motion to produce different phases of collective behavior. Inspired by recent work on active matter and the physics liquid crystals, I will discuss experiments that reveal how these cells generate nematic order, how defect structure can dictate global behavior, and how Myxo actively tune the Péclet number of the population to drive a phase transition from a gas-like flocking state to an aggregated liquid-droplet state during starvation.

Topological states of matter have been intensively studied in crystals, leading to fascinating phenomena such as scattering-free edge current in topological insulators. However, the power of topological protection goes well beyond ordered crystal lattices. In this talk we explore how topology protects mechanical edge modes in messy, noncrystalline, systems. We will use disordered fiber networks and quasicrystals as our examples, to demonstrate how topological edge floppy modes can be induced in these structures by controlling their geometry. Fiber networks are ubiquitous in nature and especially important in bio-related materials. Establishing topological mechanics in fiber networks may shed light on understanding robust processes in mechanobiology. Quasicrystals show unusual orientational order with quasiperiodic translational order. We found that a bulk topological polarization can be defined for mechanics of quasicrystals that is unique to their non-crystallographic orientational symmetry. References: (1) Di Zhou, Leyou Zhang, Xiaoming Mao, “Topological Edge Floppy Modes in Disordered Fiber Networks”, Phys. Rev. Lett. 120, 068003 (2018); (2) Di Zhou, Leyou Zhang, Xiaoming Mao, “Topological Boundary Floppy Modes in Quasicrystals”, arXiv:1809.09188 (2018).

In this talk I will present the study of the non-holonomic rotational dynamics of the classical harmonic three mass system in the strongly nonlinear regime. This is the simplest isolated spring-mass model capable of displaying rotation with zero angular momentum as well as chaotic dynamics. Combined together these two phenomena lead to a wide variety of qualitatively distinct dynamical phases as a function of the system's internal energy. For low energy, where dynamics are regular, we observe a constant rotation rate with zero angular momentum. For sufficiently high energy we observe a rotational random walk driven by the system's internal chaotic dynamics. For intermediate energies, we observe ballistic bouts of constant rotation rates interrupted by unpredictable orientation reversal events. In this regime, the system constitutes a simple physical model for Levy walks and the orientation reversal statistics lead to fractional rotational diffusion interpolating smoothly between the ballistic and regular diffusive regimes.

Recent observations of bacterial populations in the laboratory and in natural environments have exacerbated long-standing puzzles about evolution: Can evolution in a fixed environment continue forever? Why is there so much diversity on all scales, including coexistence of many within-species variants? A key role of theory in biology is to ask what is truly puzzling and what can already arise in simple models and thus should perhaps not be so puzzling. Some progress on these questions by statistical physics approaches will be the focus of this talk.

We tend to characterize simple and complex systems in terms of their steady state properties. Transients before reaching a steady state are seen as a temporary annoyance, even in non-equilibrium systems. However, transients are all important in understanding a system in a time varying environment where the environmental changes are neither slow (adiabatic) nor fast compared to the internal dynamics of the system. We show how transients can be exploited to counter fast evolving viruses, design adaptable materials and to implement recursive Bayesian algorithms using biomolecules. Along the way, we discuss choices a physicist has in picking problems in biology and roads not taken.

Robots generally excel at specific tasks in structured environments, but lack the versatility and adaptability required to interact-with and locomote-within the natural world. To increase versatility in robot design, my research group is developing robotic skins that can wrap around arbitrary deformable objects to induce the desired motions and deformations. Our robotic skins integrate programmable composites to embed actuation and sensing into a planar substrate that may be applied-to, removed-from, and transferred-between different objects to create a multitude of controllable robots with different functions to accommodate the demands of different environments. We have shown that attaching the same robotic skin to a deformable object in different ways, or to different objects, leads to unique motions. Further, we have shown that combining multiple robotic skins enables complex motions and functions. During this talk, I will demonstrate the versatility of this soft robot design approach by showing robotic skins in a wide range of applications - including manipulation tasks, locomotion, and wearables - using the same 2D robotic skins reconfigured on the surface of various 3D soft, inanimate objects.

Stochastic gradient descent (SGD) forms the core optimization method for deep neural networks, contributing to their resurgence. While some theoretical progress has been made, it remains unclear why SGD leads the learning dynamics in overparameterized networks to solutions that generalize well. Here we show that for overparameterized networks with a degenerate valley in their loss landscape, SGD on average decreases the trace of the Hessian of the loss. We also show that isotropic noise in the non-degenerate subspace of the Hessian decreases its determinant. In addition to explaining SGDs role in sculpting the Hessian spectrum, this opens the door to new optimization approaches that guides the model to solutions with better generalization. We test our results with experiments on toy models and deep neural networks.

Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. These simulations have been paired with multivariate methods for many years in search of the smallest distance scales in nature. Deep learning tools hold great promise to qualitatively change this paradigm by allowing for holistic analysis of data in its natural hyperdimensionality with thousands or millions of features instead of up to tens of features. These tools are not yet broadly used for all areas of data analysis because of the traditional dependence on simulations. In this talk, I will discuss how we can change this paradigm in order to exploit the new features of deep learning to explore nature at sub-nuclear distance scales. In particular, I will show how neural networks can be used to (1) overcome the challenge of intractable hypvervariate probability density modeling and (2) learn directly from (unlabeled) data to perform hypothesis tests that go beyond any existing analysis methods. The talk will end with a brief discussion of challenges for hypervariate deep learning analysis. While my examples will be from particle physics, it is likely that these tools have a much broader applicability across fundamental physics and beyond. I will keep the particle physics jargon minimal in order to facilitate discussions about connections to your area of science!

The lecture will review some of the seminal contributions of James Clerk Maxwell, William John Macquorn Rankine and George Biddell Airy to the theory of structures and how those theories can be applied to modern structural engineering design.

William F. Baker is a consulting structural engineering partner at Skidmore, Owings and Merrill LLP where he has led the structural engineering practice for more than 20 years.

Bill is best known for the development of the “buttressed core” structural system for the Burj Khalifa, the world’s tallest manmade structure. In addition to his work on supertall buildings, Bill’s expertise also extends to long-span roof structures and specialty structures. He has also collaborated with numerous artists, including Jamie Carpenter, Iñigo Manglano-Ovalle, James Turrell, and Jaume Plensa.

Bill is an Honorary Professor at the University of Cambridge; he has received honorary doctorates from the University of Stuttgart, Heriot-Watt University, the Illinois Institute of Technology and the University of Missouri; the Gold Medal from the Institution of Structural Engineers (IStructE), the American Society of Civil Engineers (ASCE) Lifetime Award for Design; the Gustav Magnel Gold Medal from the University of Ghent; the Fazlur Rahman Khan Medal from the Council on Tall Buildings and Urban Habitat; and the Fritz Leonhardt Preis (Germany). He is a Fellow of both the ASCE and the IStructE, and a member of the National Academy of Engineering (USA) and an International Fellow of the Royal Academy of Engineering (United Kingdom).

Bill is currently collaborating with faculty members from MIT, Cambridge, ETH/Zurich, and EPFL/Lausanne on a book intended to make Maxwell’s structural engineering work accessible to the modern engineer.

Collective behavior of organisms creates environmental micro-niches that buffer them from environmental fluctuations e.g. temperature, humidity, mechanical perturbations etc., thus coupling organismal physiology, environmental physics and population ecology. This talk will focus on a combination of biological experiments, theory and computation to understand how a collective of bees can integrate physical and behavioral cues to attain a non-equilibrium steady state that allows them to resist and respond to environmental fluctuations of forces and flows. We analyze how honeybee clusters (Apis mellifera L.) change their shape and connectivity and gain stability by spread-eagling themselves in response to mechanical perturbations. Similarly, we study how bees in a colony respond to environmental thermal perturbations by deploying a fanning strategy at the entrance that they use to create a forced ventilation stream that allows the bees to collectively maintain a constant hive temperature. When combined with quantitative analysis and computations in both systems, we integrate the sensing of the environmental cues (acceleration, temperature, flow) and convert them to behavioral outputs that allow the swarms to achieve a dynamic homeostasis.

Machine learning, at its heart, is the process of learning from examples. However, in many scientific domains, we not only have training data or examples from which to learn, but also physical models of either the data collection mechanism or the underlying physical phenomenon. In this talk, I will describe two settings in which physical models can be incorporated within a machine learning framework to yield improved predictive performance. First, we will consider using training data to help solve ill-posed linear inverse problem such as deblurring, deconvolution, inpainting, compressed sensing, and superresolution. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from training data that can outperform more traditional regularizers. We will see that whether or how a forward model is leveraged can significantly impact how many training samples are needed to achieve a target accuracy. Second, we will examine using a combination of observational data and simulated data to improve subseasonal climate forecasts. Treating both types of data as co-equal training samples can bias many learning methods and yield misleading results. I will describe an alternative framework that combines observational data with a correlation graph that can be estimated from large ensemble climate model outputs, and we will see how this approach leads to more accurate forecasts. Finally, we will discuss open problems and future directions at the intersection of machine learning and the physical sciences.

The invasion of one fluid into another of higher viscosity is unstable in a quasi-two dimensional geometry. This viscous-fingering instability typically produces complex patterns that are characterized by repeated branching of the evolving structure. When one of the fluids is replaced by a complex fluid, the system still displays a wide range of morphologies, but their underlying mechanisms can be fundamentally altered. We explore the formation of these new patterns by considering colloidal suspensions of different concentration. (i) We sandwich a colloidal gel between two parallel plates and induce an instability at the air/gel interface by lifting the upper plate at a constant velocity. Remarkably, the viscous-fingering instability resulting from the invasion of air fingers into the gel is suppressed below a critical lift velocity and above a critical initial gap thickness. We show that the onset of the instability is determined by a critical rate of viscous energy dissipation in the gel that is proportional to the gel’s yield stress, providing a quantitative criterion for instabilities in colloidal gels. (ii) Expanding our studies to dense suspensions that exhibit discontinuous shear-thickening behavior as a response to an applied stress allows us to probe transitions from flow instabilities to fractures. Displacing a cornstarch suspension by a pressure-controlled injection of air, we observe smooth fingering in the fluid regime and different modes of fractures, ranging from slow branched cracks to single fast fractures. We discuss strategies to predict and control these different failure modes in dense suspensions.

DNA is not just the stuff of our genetic code; it is also a means to build new materials. For instance, grafting DNA onto small particles can, in principle, 'program' the particles with information that tells them exactly how to put themselves together--they 'self-assemble.' Recent advances in our understanding of how this information is compiled into specific interparticle forces have enabled the assembly of crystalline phases. However, programmable assembly of other user-prescribed structures, such as aperiodic solids, liquids, or other mesophases remains elusive. Furthermore, the dynamic pathways by which DNA-based materials self-assemble are largely unknown. In this talk, I will present experiments showing that: (1) combining DNA-grafted particles with free DNA oligomers dispersed in solution can create suspensions with new types of assembly pathways; and (2) we can quantify the dynamic pathways to self-assembly, such as nucleation and growth, using a combination of microfluidics, video microscopy, and image analysis. Whenever possible, I will describe attempts to understand and model our observations using simple physical arguments.