In the large cells of many eukaryotic organisms, especially those of aquatic and terrestrial plants, the fluid contents are observed to be in constant motion. This "cytoplasmic streaming" was described first in 1774 by Bonaventura Corti, and while the molecular mechanism that drive it are now rather clear, its role in cellular physiology is still rather mysterious. In this talk I will describe a combination of experimental and theoretical work aimed at solving this mystery. A fundamental feature of such flows is that they can be sufficiently fast that the Peclet number can greatly exceed unity, a very different regime than that usually considered in cellular biophysics. This suggests the possibility that streaming promotes mixing within cell. Indeed, we find that one of archetypal geometries of fluid circulation in plants, rotational streaming driven by helical arrangements of molecular motors at the cell periphery, can enhance mixing in much the same way as certain geometries that have been explored in the field of microfluidics. Cytoplasmic streaming also interacts very strongly with the tonoplast, the lipid membrane that encloses the vacuole, raising a whole host of fascinating issues in the hydrodynamics of membranes that will be outlined.

We present experimental and numerical studies of the behavior of the Belousov-Zhabotinsky (BZ) chemical reaction in flows with chaotic mixing. Experiments with the excitable BZ reaction are used to determine the effects of fluid mixing on the motion of reaction fronts. Moving vortices in the flow tend to pin and drag the reaction front, a result that leads to mode-locking of fronts in a flow with periodically-oscillating vortices. An extension to theoretical treatment of chaotic mixing involving .burning lobes. is used to explain the measured front propagation behavior. Manifolds characterizing chaotic mixing also help explain the patterns that form for oscillatory reactions in both two- and three-dimensional flows with chaotic mixing. We also discuss pinning (freezing) of reaction fronts by vortices (both regular and random patterns) when propagating against an imposed"wind," similar in some respects to pinning of charge-density waves in the presence of an imposed electric field. Time permitting, we will also discuss experiments conducted on synchronization of extended advection-reaction-diffusion systems via superdiffusive transport and Levy flights.

In the bicentennial year of the birth of Charles Darwin, we are reminded that he invoked the incompleteness of the geological record to reconcile paleontological observations with his expectations. As early as 1860, however, Darwin's pessimistic view of the quality of the geological record had already been questioned. Rather than trying to resolve the issue based on the data available 150 years ago, let us jump forward to the 21st century and consider some of the major advances that allow us to cope with paleontological incompleteness. First, geologists simply know more of the fossil record than they did then. Whole new regions have been explored, for example, and the record of pre-Cambrian and Cambrian strata has been documented in great detail. Second, geological and paleontological data have been archived in ways that enable systematic retrieval and analysis. Finally, paleontologists and stratigraphers have developed tools for quantifying the degree of completeness of the record and for circumventing many of the biasing effects of incompleteness. I will focus on this last development, using models of incomplete sampling and computational approaches to show how paleontologists have turned the raw data from the imperfect record into estimates of parameters of evolutionary interest, such as rates of taxonomic origination and extinction.

A variety of natural growth processes, including viscous fingering, electrodeposition, and solidification can be modeled in terms of Laplacian growth. Laplacian growth patterns are formed when the boundary of a domain moves with a velocity proportional to the gradient of a harmonic field, which satisfies the Laplace equation outside of the domain. A simple model of Laplacian growth is considered, in which the growth takes place only at the tips of long, thin fingers. Following Carleson and Makarov (J. Anal. Math. 87, 103, 2002), the evolution of the fingers is studied with use of the deterministic Loewner equation. The method is then extended to study the growth in a linear channel with reflecting sidewalls. One- and two-finger solutions are found and analyzed. It turns out that the presence of the walls has a significant influence on the shapes of the fingers and the dynamics of the screening process, in which longer fingers suppress the growth of the shorter ones. Possible experimental realizations of the model are discussed, including combustion in Hele-Shaw cell and channel formation in dissolving rock fractures.

I will present a theoretical and numerical investigation of the role of air cushioning droplet impacts on dry rigid surfaces. This study is motivated by the observations that reducing the ambient air pressure can suppress splashing (Xu, Zhang and Nagel, Phys. Rev. Lett., v94(18), 184505, 2005). This cushioning occurs during a microsecond, when the droplet is within 1 micron distance from the surface. We hypothesize that contact with the surface determines whether the droplet splashes or not. The dominant forces acting during the impact are the inertia of the drop, and the viscous and compressible resistance to draining the air cushion. Due to the air cushion, the droplet deforms during impact and attempts contact on a rim trapping a bubble. However, the dominance of the aforementioned forces is not asymptotically consistent; initially negligible forces like surface tension, etc. become important and change the dynamics. As a result, before contacting the surface, the droplet interface overturn on itself indicating the ejection of a liquid lamella. We predict that for an ethanol droplet of radius 1.7 mm moving at 3.7 m/s in 1 atm ambient pressure, the lamella is ejected on a rim at a radial distance of about 100 microns, when the center of the drop is 0.5 micron from the surface. We believe that this lamella subsequently forms the crown of the splash.

When an unstable iceberg tips over, it makes waves in the surrounding water that can (a) stimulate other icebergs to capsize and (b) cause calving of new unstable icebergs from surrounding glacier termini. This fact causes Wendy Zhang and I to wonder if the dynamics of ice-shelf disintegration in Antarctica can be understood as a .mass capsize catastrophe. applicable to arrays of orientation-changeable objects that interact via waves in an intervening medium. My presentation will outline the computational approaches to one of the most reduced sub-questions of the ice-shelf disintegration problem: What is the amplitude and frequency distribution of waves created by capsize of a single iceberg in otherwise quiet, undisturbed water? I discuss the strengths and weaknesses of three numerical approaches: "meshed" finite-element and finite-difference methods, "meshless",

smooth particle hydrodynamics (SPH) methods, and a "contour integral",

method based on Green's second identity.

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We can treat the behavior of many-atom systems in terms of how they move on an effective potential surface of 3N-6 independent variables and one dependent variable, the internal energy, for a system of N atoms. This is adequate for most dielectric systems; collections of metal atoms are more complex and sometimes require considering multiple potential surfaces. However, restricting ourselves to dielectrics, at least to get into the subject, we recognize that we face a problem of trying to understand what goes on in a space of very many dimensions, if we want to treat anything beyond the very simplest systems. We find ourselves confronting such questions as, "Why can some systems, cooled from their liquid state, invariably find their way to special, often well-ordered structures, while others 'get lost' in any of a large number of amorphous structures?" This is obviously related to the issue of protein folding, among others. Can we characterize such surfaces in terms of their stationary points? Can we use master equations to describe what happens on these surfaces, in terms of their well-to-well kinetics? How is the topography of a multidimensional potential related to the interparticle forces? These are some of the issues we face in trying to address a challenge of true complexity.

State estimation techniques are used in weather and climate prediction, hydrogeology, seismology, as a way to blend model output and real data in order to improve on predictions from the exclusive use of the model or the data alone. Techniques that are based upon least-squares ideas, such as the family of Kalman Filter/Smoothers, or Variational Data Assimilation, are optimal in linear/Gaussian problems. However, they often fail in problems in which nonlinearities are important and/or when Gaussianity in the statistics cannot be assumed. Even linearization may fail, and so do ensemble techniques that make nonlinear predictions but rely on linear analyses. These comprise the practical state of the art, at least in weather forecasting and in hydrogeology. I will describe these as well as how failures arise in these methods. We have created a number of nonlinear/non-Gaussian data assimilation techniques. Our present efforts are to make them computationally practical as well as to use of these to do problems that are otherwise intractable using conventional means. One such application is in Lagrangian data assimilation: here we tackle the problem of blending data that has been sampled along paths, which when blended in traditional ways on Eulerian grids will lead to loss of critical features even though the estimates may be variance-minimizing.

The Swift parallel scripting language lets users apply parallel composition constructs to existing sequential or parallel programs to express highly parallel scripts. Swift scripts are flexible and portable, and can run efficiently on platforms ranging from multicore workstations to petascale supercomputers. For performing parameter sweeps and data analysis with exiting application programs, parallel scripting is typically easier and more productive than tightly-coupled parallel programming. This talk will give an overview of Swift and how its used to run scientific applications in parallel on clusters, grids, clouds, and petascale systems. The architectural challenges of scripting on large-scale systems will be covered, and case studies will be presented.

The carbon cycle describes the transformations of carbon as it cycles through living organisms and the physical environment. Understanding its rates of change is fundamental to problems ranging from the evolution of life to modern climate change. A key aspect of these problems concerns the rates at which organic detritus is converted to carbon dioxide. Observations---in forests, in the oceans, and in sediments at time scales ranging from days to millions of years---show that apparent rates systematically slow down with increasing detrital age. We show how these observations can be quantitatively related to biological and environmental heterogeneity, and suggest ways in which our results may prove useful for understanding past and present changes in carbon dioxide levels.

About 252 million years ago, as the Permian period gave way to the Triassic, roughly 90% of all living species disappeared from the fossil record. This event, the most severe extinction in Earth history, was accompanied by a rapid (~10,000 year) change in the carbon isotopic composition of seawater. By transforming these chemical changes to physical fluxes, we show that the isotopic event is consistent with an incipient singularity in the growth of the oceans' reservoir of dissolved inorganic carbon. The singular influx of carbon dioxide into the oceans indicates a fundamental nonlinearity in Earth's carbon cycle. Its identification suggests that any hypothesis for the extinction's cause should predict such a blow-up. We identify a biological mechanism with this property and discuss its relevance to observations.

While information processing takes place across many levels of biological organization, the coordination of interaction specificities within cellular networks plays a central role. The problem of molecular discrimination is a key element in many regulatory and signaling pathways, but is compounded by the coexistence of many homologous molecules that interact with similar sets of substrates, leaving systems vulnerable to potential crosstalk between pathways. Interpreting such phenomena in the context of constraint satisfaction problems that arise at the interface of computer science and statistical physics reveals unusual geometric structures in high-dimensional genotype spaces that might be used to negotiate tradeoffs between system robustness and fragility.

Biological membranes define the outer boundary of living systems and mediate transport and communication between the cell and its surroundings. They defend the cell against invasive agents, and most present day drugs interact with membrane components. While biological membranes are critical features of functioning cells, their complexity renders many of their characteristics impenetrable to fundamental physical studies. In my presentation I will demonstrate how the neutron reflectometry can be applied to study model lipid membranes in bulk water. In particular I will discuss how neutron reflectometry can be used to probe the stability of mixed sphingomyelin/cholesterol (SM/Chol) bilayers during their interactions with b-cyclodextrin. The SM/Chol complexes are thought to be major constituents of so called .lipid rafts. which have been implicated in many cell functions such as endocytosis, signaling, and lipid regulation.

Super-cooling a liquid often produces glass -- a solid with no apparent structural order. Unlike crystallization, a glass transition is not accompanied by a thermodynamic singularity. Nevertheless, a phase transition can underlie the formation of glass. Unlike equilibrium order-disorder phenomena, this transition appears as a singularity in a partition function of dynamical histories. I describe this transition -- its order parameters and phase diagrams.

The statistical physics of trajectory space has a distinguished history -- from Onsager's formulation of non-equilibrium thermodynamics, to Martin-Siggia-Rose theory, and so on. In recent years, it has provided principles that, among other things, facilitate computer simulations of rare events, and numerical studies of non-equilibrium phase transitions. I describe this development and its applications in a few illustrative cases.

A function traditionally attributed to articular cartilage, the soft connective tissue covering bones in joints, is to dissipate energy from impact loading. This idea, however, came into question in the 1970's with the recognition that articular cartilage is no more effective than trabecular bone at reducing the peak impact force of a sudden compressive load. On the other hand, articular cartilage is exposed to shear loading as well as compressive loading during joint articulation. In fact, shear stresses in articular cartilage are known to be a major cause of joint damage and disease. The energy absorbing capacity of this tissue under shear has received little attention. Furthermore, the inhomogeneous structure and composition of this tissue suggest a heterogeneous capacity to dissipate energy that remains largely unstudied. In this talk I will show that using a Tissue Deformation Imaging Stage in conjunction with fast confocal microscopy allows for determining the depth dependence of the shear mechanical properties of articular cartilage. Using these novel techniques we have been able to determine that nearly all of the shear energy is dissipated in a 300 micron thick region located 100 microns below the articular surface. Finally, I will comment on the relevance of this finding to diseases such as Osteoarthritis as well as some of the medical procedures that are currently being practiced.

Ideas about evolution were first formulated before biology and genetics developed into the sophisticated sciences they are today. Molecular biology has had a profound impact on our understanding of how organisms are related to each other and how they change over time. Genome sequencing reveals the evolutionary record as it remains in the DNA of living organisms and constitutes a test of theories about how evolution has occurred. Discoveries about the molecular and cellular nature of evolutionary changes show that this remains a vital and exciting area of science with many new theoretical and experimental possibilities. In particular, genome sequences teach us that many key events in evolution have been accompanied by major and rapid changes in the content and organization of cell DNA that affected numerous characters at the same time. These kinds of changes were unknowable to the pioneers of evolutionary thinking and have not yet been included in conventional statements about how the evolutionary process operates. Incorporating lessons from the DNA record, recent observations of evolution in action, and experiments about the biological processes of genome change make it possible to formulate a 21st Century view that is consistent with other developments in the molecular life sciences.

The crucial feature of neuronal ensembles is their high complexity and variability. This makes modelling and computation very difficult, in particular for detailed models based on first principles. The problem starts with modelling geometry, which has to extract the essential features from those highly complex and variable phenotypes and at the same time has to take in to account the stochastic variability. Moreover, models of the highly complex processes which are living on these geometries are far from being well established, since those are highly complex too and couple on a hierarchy of scales in space and time. Simulating such systems always puts the whole approach to test, including modeling, numerical methods and software implementations. In combination with validation based on experimental data, all components have to be enhanced to reach a reliable solving strategy. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. In the lecture we present a three dimensional model of signaling in neurons. First we show a method for the reconstruction of the geomety of cells and subcellular structures as three dimensional objects. With this tool, NeuRA, complex geometries of neuron nuclei were reconstructed. We present the results and discuss reasons for the complicated shapes. We further present a tool for the automatic generation of realistic networks of neurons (NeuGen). We then present a model of calcium signaling to the nucleus and show simulation results on reconstructed nuclear geometries. We discuss the implications of these simulations. We further show reconstructed cell geometries and simulations with a three dimensional active model of signal transduction in the cell which is derived from the Maxwell equations and uses generalized Hodgkin-Huxley fluxes for the description of the ion channels.

The phase behavior of hard, spherical particles is, by now, well understood. However, the dense phases of soft particles can be surprisingly different from the simple crystal structures that are formed by atoms or colloids. In my talk I will discuss two unusual phases that can be formed by soft particles: one such phase is observed in the case of very "soft" nano-colloids. Such particles may form a completely novel class of crystals: so-called "cluster solids". The second system that I will discuss consists of colloids coated with a "corona" of long DNA strands. These systems can undergo a curious condensation transition that has no counterpart in systems of simple atoms or molecules. In my talk I will discuss how computer simulations can be used to gain insight in the unusual physical properties of these materials.

In this talk I will describe two innovative programs in postgraduate education. The first is the African Institute for Mathematical Sciences (AIMS), in Cape Town, South Africa. AIMS has developed a unique teaching model whose goal is toenable outstanding graduates, recruited from across Africa, to become independent thinkers and problem-solvers across the full spectrum of science and engineering, feeding into research, academia, industry and government. Since 2003 over 300 students, from 30 African countries and including 75 women, have passed through AIMS doors. The vast majority have continued to successful scientific and technical careers, most in Africa. It is now proposed to scale AIMS up to a network of 15 centres continent-wide, over the next decade. This proposal, dubbed the AIMS Next Einstein Initiative, is winning widespread interest andsupport. The second is the Perimeter Scholars International (PSI) program, recently initiated at the Perimeter Institute for Theoretical Physics. Learning from many successful elements of the AIMS program, PSI is an attempt to renew and refresh the teaching of advanced theoretical physics, making this fundamental field more attractive, interesting, exciting and useful for students recruited from around the world. The experience of developing new approaches to the teaching of mathematical science holds many lessons: of the power of mathematical ideas to bridge cultures, and of the need to do more to attract and enable bright students to pursue advanced scientific learning.

Bacteria sense and respond to environmental stimuli, or signals, through a simple class of regulatory circuits consisting of an upstream sensory protein and a down stream response regulator. These .two-component systems. play a central role in regulating adaptive responses to diverse environmental signals and are found in remarkable numbers within individual organisms and across different bacterial species. Two-component systems show considerable variability in their degree of complexity, but even the simplest examples have interesting and subtle design features. After a general introduction to bacterial signal transduction, I will describe modeling and experimental work on E. coli in which we have explored implications of the phosphorylation cycle that is found in many of these circuits. I will primarily focus on mechanisms that maintain signal strength and provide insulation against cross-talk.

Really random solids -- such as those commonly formed via the vulcanization of polymers or the random chemical bonding of small molecules -- are materials that have architectures that are as unsymmetrical as can be. Nevertheless, they exhibit certain surprisingly universal characteristics -- structural and elastic -- that are not exhibited by their apparently simpler cousins, the crystalline solids. In this talk, I shall discuss the main ideas that go into constructing a simple, Landau-style approach to the structure and elasticity of really random solids. I shall focus on how these universal characteristics, and especially their probabilistic nature, can be readily encoded -- and hence computed -- within a framework that involves not one but many copies (or replicas) of the space in which the constituent particles move. By drawing on examples from polymer and liquid-crystal science, I hope to show that this Landau-style approach enables the development of a unified view of the percolative, structural, and elastic characteristics of many types of really random solid.

Animal groups, such as bird flocks, fish schools, or insect swarms, often exhibit complex, coordinated collective dynamics resulting from individual interactions. While there has been a growing interest in this emergent behavior, there are only a handful of experiments that study swarms in a controlled environment. In this talk, I will present theoretical advances stemming from new interactive fish schooling experiments recently set up in collaboration with Prof. I. Couzin and his group at Princeton University. Using results from these experiments and previously gathered data on the collective motion of locusts, I will characterize different swarming states from a non-equilibrium statistical physics perspective, highlighting specific and universal properties that stem from dynamical and evolutionary constraints

Lynn Margulis has studied the role of symbiosis in evolution for the last five decades. Her defense of the symbiogenetic hypothesis was vindicated in the 1980s when organelle DNA sequencing demonstrated that mitochondria and chloroplasts are descendants of endosymbiotic bacteria. She has continued to challenge conventional wisdom and argue for symbiotic events in the evolution of organelles that no longer contain their own DNA. One of the leading non-Darwinian evolutionists, Lynn Margulis always introduces new information and new ways of thinking scientifically about less widely known biological processes in evolutionary change.

Piezo-acoustic inkjet printing has become a mature technique for high performance printing. Nevertheless, there are still various scientific challenges. In this talk I will cover some of them: (i) Coupling between the fluid dynamics and the acoustics, in particular when a disturbing bubble has been entrained in the ink channel. (ii) Optical and acoustical monitoring of the bubble. (iii) Mechanisms of the bubble entrainment. (iv) Droplet formation and pinch-off of droplets. (v) Droplet impact on substrates. The work has been done in close collaboration with Oce.

I present a fundamental theory for the kinetics of systems of classical particles. The theory represents a unification of kinetic theory, Brownian motion, and field theory. It is self-consistent and is the dynamic generalization of the functional theory of fluids in equilibrium. This gives one a powerful tool for investigating the existence of ergodic-nonergodic transitions near the liquid-glass transition. I will discuss recent progress in understanding the nature of the liquid-glass transition.

Understanding how the brain works has remained a formidable challenge that holds the promise for fundamental change in the human condition. Neuroscience has become a truly interdisciplinary field of research where some of the most substantial progress has resulted from the combined application of computational and experimental approaches. In this talk, I am going to show how computational neuroscience embedded in an experimental context has revealed novel mechanisms of how structured brain activity emerges. The overall goals of the talk are to (1) provide an introduction to the brain and in particular to cortex as a fascinating dynamics system, (2) demonstrate the role and benefits of computational neuroscience, and (3) motivate further interdisciplinary work between simulations/theory and experiments.

In contrast to water-in-air break-up, which approaches universal dynamics regardless of initial or boundary conditions, the pinch-off of air-in-water maintains a memory of azimuthal asymmetries until the final moment. For such a memory to persist, the vertical flow in the pinch-off neck region must become negligible as break-up approaches; a significant vertical flow would sweep azimuthal asymmetries out of the minimum and erase memory. Previous studies focused on situations where this requirement can be satisfied easily: As the bubble begins to break apart, its neck profile near the minimum evolves towards two slender cones smoothly joined at the apex. This dynamics, characterized by a logarithmically slow decrease in the cone angle, is vulnerable to azimuthal asymmetries. This leaves open the question of whether a memory-erasing vertical flow can be created by causing the neck profile to evolve towards a shape with large and/or asymmetric cone angles. Using a boundary-integral simulation, we show that this does not in fact happen. When the two initial cone angles are equal, the vertical flow acts to reduce the angles. When the two cone angles are different, the vertical flow is comprised of a translation and a rolling motion. The neck minimum quickly shifts towards the smaller-angle cone, causing a new, symmetric neck to form with cone angle close in value to the smaller initial angle. In all cases, the adjustment towards a slender neck is rapid, occurring as the minimum reduces by only a factor of 10. As a result, all initial states evolve toward a regime where the vertical flow is much weaker than the radial flow. Therefore, all bubble shapes support memory-encoding vibrations.

Suppose that the variability in our movements is caused not by noise in muscle contraction, nor by fluctuations in our intentions or plans, but rather by errors in our sensory estimates of the external parameters that define the appropriate action. For tasks where precision is at a premium, performance would be optimal if no noise were added in movement planning and execution: motor output would be as accurate as possible given the quality of sensory inputs. We have used visually-guided smooth pursuit eye movements in primates as a testing ground for this notion of optimality. In response to repeated presentations of identical target motions, nearly 92% of the variance in eye trajectory can be accounted for as a consequence of errors in sensory estimates of the speed, direction and timing of target motion, plus a small background noise that is observed both during eye movements and during fixations. The magnitudes of the inferred sensory errors agree with the observed thresholds for sensory discrimination by perceptual systems, suggesting that these very different neural processes are limited by the same noise sources. Computing the signal to noise ratio of pursuit movements allows us to estimate a .behavioral threshold. akin to a threshold for reliable perceptual discrimination of a change in target motion. We find that pursuit thresholds agree quite well with perceptual thresholds throughout the sensory-driven period of movement initiation. These results suggest that pursuit can be a reliable read-out of the evolving sensory estimate of target motion.

A gel is a meshwork of cross-linked polymers, permeated by solvent. We model the gel as a stretched elastic network of Lennard-Jones (ELJ) particles in two dimensions and study it by molecular dynamics simulations. When temperature is reduced below a threshold value, the gel undergoes large-scale reorganization and percolating strings (high density clusters) appear in the system and form a super-network. The discontinuous transition exhibits hysteresis and is accompanied by a jump in the elastic modulus. We explore the phase diagram, identify the range of parameters in which micro-phase separation is observed and propose some tentative ideas concerning the physical mechanisms leading to the formation of string-like clusters. Finally, we examine the effects of network heterogeneity and present preliminary results on three dimensional ELJ networks.

This seminar will present a number of the theoretical techniques we have developed to solve the Schroedinger equation for ground and excited states, including collisions and multiparticle continua. Some of the methods that we have combined in unusual ways include correlated Gaussian basis sets, diffusion quantum Monte Carlo, hyperspherical coordinate formulations, and multichannel quantum defect ideas. These tools, when used appropriately, substantially extend the range of problems amenable to theoretical description. In addition to discussing how these various methods permit the calculation of quantitative observables, I will stress the qualitative and semi-quantitative insights they provide into underlying dynamical mechanisms.

Traumatic Brain Injury (TBI) describes injury to the brain that is not necessarily accompanied by skull fracture and includes the full range of severities from mild concussions to permanent cognitive impairment. Head impacts from motor vehicle and sports injuries are the leading causes of civilian TBI, but TBI is also common among military personnel exposed to blast waves, with estimates placing upwards of 20% of returning US soldiers from recent combat missions affected. Current helmets do not adequately protect against TBI for either impacts or blasts. There are conceptual shortcomings in the measures of protection and inadequate standards even for aspects of impact protection that are understood. For blasts, the problem is even more fundamental as there has been a lack of understanding of the actual injury mechanism. I will discuss results of computations and numerical simulations that study the interaction of blast waves with toy models of the skull and reveal a skull flexure induced injury mechanism distinct from that of the bulk acceleration injury associated with head impacts. I will discuss the long term implications of these distinct mechanisms for helmet design and clinical diagnosis. An underlying theme is to highlight the opportunity for a physics perspective in this subject.

We are interested in developing and using microfluidics for high-throughput studies in development, cancer and immunology. In this talk, I will show a microfluidic system for arraying, aligning/orienting, and imaging embryos to study the signaling in embryonic development. We use a similar principle to manipulate cells and perform long-term imaging of cells under a variety of conditions to study, for example, calcium dynamics in cellular response to oxidative stress. The advantage of this approach is that we have robust particle loading (very high loading occupancy and high single cell/particle/embryo loading efficiency) in a short amount of time just using flow passively. In a parallel example, we use microfluidics to study early signaling event in T cell activation. Cells/particles interact with fluids especially in complex flow (e.g. chaotic flow) and it makes mixing difficult in some instances. On the other hand, these interactions can be exploited to perform separation. I will present a fast and inexpensive method to image fast flowing particles in flow as well as results of some studies of T cell replicative senescence using this system.

Sterols are essential membrane bilayer components in all eukaryotic cells, and their abundance is tightly regulated by manifold feedback pathways. I will present evidence that these homeostatic mechanisms respond to a common signal: active membrane cholesterol. Active cholesterol is that fraction which exceeds the complexing capacity of the polar bilayer lipids. Increments in plasma membrane cholesterol exceeding this threshold have an elevated chemical activity (escape tendency) and redistribute to both extracellular plasma lipoproteins and intracellular organelles. The organelles make several homeostatic responses. They esterify active cholesterol for its storage and use it for the synthesis of side-chain oxysterols that then trigger pathways which reduce cholesterol accumulation. The active fraction also curtails cholesterol biosynthesis and ingestion and increases its export. In this way, the abundance of cholesterol is tightly coupled to that of its polar lipid partners through active cholesterol.

At least twice during the Neoproterozoic era (~635 and ~715 million years ago) there were strange and mysterious glaciations that differ dramatically from the recent (last few million years) glacial periods. For example, geological data suggests the entire ocean may have been covered with ice, which we call the ``Snowball Model!" At the same time, micropaleontological and molecular clock evidence indicates that photosynthetic eukaryotes thrived both before and immediately after these glaciations, which may suggest that some significant fraction of the ocean was ice-free (``Slushball Model"). We present a previously undescribed global climate state, the Jormungand state, that is nearly ice-covered with a narrow (~10-15 degrees of latitude) strip of open ocean near the equator. This state is sustained by internal dynamics of the hydrological cycle and the cryosphere. The Jormungand state is only possible if one accounts for the different albedo of bare and snow-covered sea ice, atmospheric dynamics, and the hydrological cycle. An important aspect of the Jormungand state is that there is a hysteritic bifurcation in global climate climate associated with it, which allows it to explain all of the geological evidence that a full Snowball model can explain, and separates it from the Slushball model.

We will start with a brief survey, covering the major scientific insights that led to the current discipline of "quantum information processing." We will discuss major promises (algorithmic speedup and information theoretic security) and outstanding challenges.

Then, we turn our attention away from any technological, funding, or citation concerns, and indulge in some fundamentally strange properties of quantum information and quantum correlations. For example, it takes infinitely many bits to describe a 2-level quantum state, but one can only extract one bit from it, and we will see the consequences of this disparity on cryptography, many body physics, and foundations of quantum mechanics. Given enough time, other examples (say, teleportation, entanglement, and channel capacities) will be discussed.

US power industry is facing a modernization stage that is usually referred to as transition to "smart" grid. In this talk I will review the key challenges that motivate this transition as well as the solutions proposed to address them. I will present a theoretical physicist view of the problem focusing on the analogies between power grid dynamics and other well-known statistical physical systems. Original results on decentralized control of reactive power flows and randomized scheduling of PHEV charging jobs will be discussed in the end of the talk.

Have you ever received a shock when you touched a doorknob after shuffling across a carpeted floor? The culprit, known as triboelectric charging, is also responsible for phenomena as innocuous as a rubbed balloon that makes your hair stand on end, or as dramatic as a lightning strike. While it is familiar to every child, fundamental understanding of triboelectric charging is so poor that even the most basic questions are still being debated, such as whether the transferred charge species are electrons or ions. Scientific progress is difficult because triboelectric charging is a non-equilibrium process (separated surfaces are neutral at equilibrium) that involves changes in electron states and occurs at a level of one electron per 100,000 surface atoms (physical and/or chemical defects at this low level likely control the behavior). This talk will describe our experimental and theoretical investigations of triboelectric charging, focusing on the charging that occurs in flowing granular materials.

Turbulent mixing and turbulent combustion are frontier issues that connect themes from mathematics, physics, computational science and engineering applications. Significant recent progress has largely resolved problems in one area of this topic, specifically the determination of the overall size of the turbulent mixing region in Rayleigh-Tauylor mixing. The mixing rate is summarized in a dimensionless coefficient alpha defined at the ratio of the mixing distance to an acceleration distance Agt^2, and is itself the subject of numerous controversies.

We will present results to determine alpha from simulation, often within two significant digits of the experimental values. A number of consensus views are challenged in the process, including the universality of alpha, the reliability of simulations not validated by experimental data, and the relative role of initial conditions in the observed experimental values for alpha.

We formulate a mathematical framework to better understand numerical convergence in the large eddy simulation (LES) regime. Validation, in a broader context, is supported by multiple applications for simulations of this nature, including turbulent combustion in the engine of a scram jet, chemical processing for nuclear fuel rod separation, primary breakup of a high speed jet of diesel fuel, and the design of targets for high energy accelerators.

Looking to the future, microscopic measures of mixing will be an important issue, either to specify a second moment of fluctuating quantities, or to supply a full probability density function (pdf).

Genetic circuits have been studied quite intensively in recent years. We have focused on oscillatory patterns in eucaryotic systems related to negative feed-back loops inside single cells. In many cases it is of interest to study how cells communicate with each other when cells are arranged in certain spatial structures, like biofilms and tissues. We have attacked this problem by means of a repressor-lattice where single repressilators (closed feed-back loops) are placed on a hexagonal lattice. Such systems can be build without any internal frustration and can in most cases exhibit stable, oscillating states. Commensurability effects however play a role and may lead to internal frustration causing breaking of symmetries and solutions of many different phases. Eventually, also chaotic solutions may be present. With bi-directed interactions the tissues locally exhibit switch-like behavior. During growth the tissues may develop 'defects' and we have found that mutations have a larger effect in such cases than in ordered tissues.