Eric Sembrat's Test Bonanza

DNA is an iconic molecule that forms a double helical structure, providing the basis for genetic inheritance, and its physical properties have been studied for decades. In this talk, I will present evidence that sequence and methylation dependent physical properties of DNA such as flexibility and self-association may be important for biological functions [1,2]. In addition, I will present a new application of DNA where mechanical modulations of cell behavior can be studied at the single molecule level using rupturable DNA tethers [3]. We found that cells can change their behavior dramatically in response to just two molecules strongly tugging on them [4].

References.

[1] R. Vafabakhsh and T. Ha, “Extreme bendability of DNA less than 100 base pairs long revealed by single molecule cyclization”, Science 337, 1097-1101 (2012).

[2] T. Ngo, Q. Zhang, R. Zhou, J. G. Yodh and T. Ha, “Asymmetric unwrapping of nucleosomes under tension directed by DNA local flexibility”, Cell 160, 1135-1144 (2015).

[3] X. Wang and T. Ha, “Defining Single Molecular Forces Required to Activate Integrin and Notch Signaling”, Science 340, 991-994 (2013).

[4] M. Roein-Peikar, Q. Xu, X. Wang and T. Ha, “Ultrasensitivity of cell adhesion to the presence of mechanically strong ligands,” Physical Review X (2016).

Abstract

In this talk, I will provide background on foundational concepts of disease dynamics and perspectives on near- and long-term challenges related to the pandemic spread and potential control of COVID19.

Abstract

The transport and mixing of passive tracers in a fluid can be understood in terms of a passive tracer’s phase-space geometry: the invariant solutions and invariant manifolds of the passive tracer equations of motion.  In this talk, I will describe our ongoing work on extending this phase-space perspective to explain the transport of a particular type of active tracer: the rigid ellipsoidal microswimmer. In our model, microswimmers swim at a fixed speed in the local fluid frame and rotate due to flow gradients at a rate determined by their shape.  We determine the phase-space structures governing transport in two model fluid flows and examine the influence of swimmer speed and shape.  In the first example, a linear hyperbolic flow, we find that the fixed points and their invariant manifolds form one-way barriers to the swimmers, while the swimmer parameters play a minor role.  In the second example, a spatially periodic vortex array, we focus on the trapping of swimmers in vortices.  We identify a stable periodic orbit surrounded by invariant tori as the main cause of trapping for a wide range of swimmer parameters. We show that this periodic orbit undergoes a sequence of bifurcations, both local and global, which sheds light on the sensitive dependence of trapping probability on swimmer speed and shape and accurately predicts the parameters at which the trapping probability vanishes.

Abstract

 Natural systems often inspire new methods by which to control fluids, or conversely use fluids to control systems. Three fluid mechanic systems that might come to mind include: the jetting of microdroplets when peeling an orange, the droplet ejection off of a mosquito wing, and the splash suppression provided by floating toilet paper during defecation. These phenomena at the interface of biology and fluid mechanics can provide engineers with useful, inspired information for future designs. Here we study the explosive dispersal of oil emitted from ‘cracked’ glands in the peels of citrus fruit. The jetting oil undergoes an extreme acceleration to reach velocities in excess of 10 m/s. On a slightly larger scale, the method mosquitoes use to ‘buzz’ their wings free of deposited water drops prompts a look into how flexible surfaces may be vibrated to self-dry. We find drop ejection is dependent on drop and wing properties, and wing motion in this highly-coupled system.  On yet a larger scale, the dynamics of the entry of solids into liquid baths, such as the ‘plop’ of restroom use, is heavily influenced by free surface conditions.

Bio:

Andrew Dickerson is a fluid dynamicist with expertise in the mechanics of interfaces, and explores problems in which the dynamics fluids and their solid boundaries are highly coupled. His work is often inspired by problems stemming from biology, aimed at uncovering the physics of living systems from antifouling and insect flight to pine tree interactions with rainfall. Dr. Dickerson is an 2019 NSF CAREER award recipient to study the tuning of jet and splash characteristics with compliant and heterogenous boundaries. He is currently an Assistant Professor of Mechanical and Aerospace Engineering at the University of Central Florida (UCF), and obtained his PhD in Mechanical Engineering from the Georgia Institute of Technology.

Abstract

A variety of naturally evolved systems—from highly integrated neural networks inside the animal brain to loosely integrated collective behaviors in an ant colony—show hallmarks of cognition; these systems appear to make decisions based on information accumulated from the surrounding environment. Prior investigations have shown how cognitive processes can manipulate the surrounding environment while making a decision, but little focus has been placed on understanding the latent information-processing ability in the environment itself. The earliest information-processing systems emerged out of entities interacting within physical spaces, and so understanding the evolution of information processing requires understanding the exploitable information-processing opportunities afforded by background structures and constraints. In this talk, I describe our recent findings that decision-making performance curves taken from real ants that were once thought to be a product of a deliberative, cognitive process within the ant brain can also be explained as primarily a property of the cavities that constrain the motion of stochastically interacting particles. This idea that constrained randomness can be a module for cognition can be extended to very different contexts, such as the design of deep neural networks. I demonstrate this by showing that deep networks designed for sophisticated knowledge representation and reasoning tasks can have increased performance by counterintuitively replacing training with random weighting, thereby showing that a network's representational strengths can be more a property of macroscopic structure of a dense network and not any particular "optimal" pattern of network weights. As these examples demonstrate, a true science of cognitive ecology is necessarily a physics of living systems as it requires marrying the non-living physical world with the out-of-equilibrium behaviors of those agents both constrained by and, in turn, enabled by it.

Bio

After a decade of working in Software and Systems Engineering, Theodore Pavlic received his Ph.D. in 2010 in Electrical and Computer Engineering at The Ohio State University and has progressed through postdoctoral appointments in both Computer Science and Behavioral Ecology. He currently is an Assistant Professor at Arizona State University jointly appointed in the School of Computing, Informatics, and Decision Systems Engineering and the School of Sustainability, with an adjunct appointment in the School of Life Sciences. He also serves as the Associate Director for Research at The Biomimicry Center and is affiliated with a number of ASU centers related to complexity and trans-disciplinary thinking. His interdisciplinary laboratory includes students from a range of programs stretching across Computer Science, Industrial Engineering, Animal Behavior, Biology, and Applied Math for the Life and Social Sciences. Consequently, work in the lab spans empirical work with natural systems, such as social-insect colonies in both the field and lab, artificial intelligence and machine learning work related to computational sustainability, and multi-robot systems work. Furthermore, his lab participates in conferences, publishing venues, and professional organizations across several more traditional disciplines in biological sciences and engineering. The common thread that goes through all of his lab's work is a better understanding of how autonomous systems can make good decisions especially amongst a background of long-term autonomy in changing environments. 

Abstract

Suppose you find yourself face-to-face with Young-Mills or

Navier-Stokes or a nonlinear PDE or a funky metamaterial or a

cloudy day. And you ask yourself, is this thing "turbulent"? What

does that even mean?

If you were ever taught 'chaos', you must have learned about the

coin toss (Bernoulli map). I'll walk you through this basic example

of deterministic chaos, than through the 'kicked rotor', a neat

physical system that is  chaotic, and then put infinity of

these together to explain what `chaos' or `turbulence' looks like

in the spacetime.

What emerges is a spacetime which is very much like a big spring

mattress that obeys the familiar continuum versions of a harmonic

oscillator, the Helmholtz and Poisson equations, but instead of

being "springy", this metamaterial has an unstable rotor at every

lattice site, that gives, rather than pushes back. We call this

simplest of all chaotic field theories the `spatiotemporal cat'.

That's `turbulence'. And if you don't know, now you know.

No actual cats, graduate or undergraduate, have shown interest in,

or were harmed during this research.

Abstract

I will discuss the adaptive strategies that diverse microbes, including prokaryotic and eukaryotic organisms, use to cope with their mechanical environment and with the mechanical constraints imposed on them by evolution.  First, it is well understood that the peptidoglycan cell wall is an essential mechanical structure for bacteria.  In Gram-negative bacteria, it is widely believed that the outer membrane simply provides an additional permeability barrier.  Conversely, I will show that the outer membrane is at least as stiff as the cell wall and plays a critical role in protecting bacteria from mechanical insults, revising our textbook understanding of bacterial mechanics.  I will discuss ongoing efforts to dissect the biochemical and structural basis for the outer membrane's mechanical properties.  Second, it is well established that fungal and protistan hyphae use turgor pressure to drive cell-wall expansion during cell growth.  I will show how this mechanism, combined with an evolutionary selection for fast growth, provides a tight developmental constraint on the range of possible cell shapes.  Using computational modeling, I will demonstrate that this constraint takes the form of a "tipping-point catastrophe" often seen in dynamical systems theory.  These examples elucidate how the interplay of evolution and physics conspire to determine the ultrastructure and shape of microbial cells.

Abstract

Quantum physics has revolutionized our understanding of nature and led to significant technological advances in the last century. While much focus has been placed on the equilibrium properties, in the far-from-equilibrium regime, many-body systems host diverse and important quantum phenomena to be exploited towards developing next-generation technologies. These range from generating desired quantum entangled states to engineering novel dynamical phases of matter, which yet remain poorly understood. Recent experimental progresses in preparing and probing ensembles of ultracold atoms have opened up unique opportunities for investigating complex dynamical behaviors in quantum many-body systems. In this talk, I will present recent developments both in theory and experiment towards understanding and controlling nonequilibrium quantum matter, focusing on two platforms, magnetic atomic dipoles, and atoms coupled via photon mediated interactions. Both of them feature strong and long-range atomic interactions, which pose challenges for theoretical treatment. I will introduce a new theoretical approach for tackling such challenges and demonstrate its application for benchmarking a high-spin Heisenberg XXZ quantum simulator as well as exploring quantum thermalization. In particular, I will show how the interplay between long-range interactions and quantum fluctuations can result in rich and useful many-body behaviors. I will further discuss prospects offered by these studies for pushing the frontiers of fundamental physics and to generate quantum correlated many-body states for applications in both quantum computation and metrology.