Squids and octopuses occupy every optical niche of the ocean, from mud flats to the midwater abyss. In each of these disparate environmental radiances, camouflage is generated by a layer of skin cells containing sub-visible-wavelength-scale arrays of a high-refractive-index protein called reflectin. In shallow contexts, the reflectance of this layer matches the albedo of a given species' background, while in open ocean contexts, animal-generated light scattering through this layer matches the radiance of the surrounding water. These feats of photonic engineering are achieved via self-assembly of the enigmatic reflectin protein.

After about two decades of work, we still do not have a clear physical picture of reflectin protein-protein interactions to know how or why some mixtures of these proteins make mirrors in vivo, while other mixtures of similar proteins make light guides. This talk explores and weighs the evidence for several novel hypotheses of reflectin assembly mechanisms generated by my group, including the free energy of the proteins' association with lipid bilayers and the bilayers' corresponding physical phases; the evidence for true patchy-colloid physics in reflectins; the possibility of a surface-induced phase transition; and the possible role of a novel, flexible metal coordination. None of these mechanistic possibilities are mutually exclusive. While we do not yet have a complete experimental picture of this system, it is increasingly clear that squid are leveraging assembly constructs at the cutting edge of current theoretical understanding in the fields of soft matter and self-assembly.

Proteins can fold spontaneously into well-defined three-dimensional structures and can carry out complex biochemical reactions such as binding, catalysis, and long-range information transfer. The precision required for these properties is achieved while also preserving evolvability – the capacity to adapt in response to fluctuating selection pressures in the environment. What is the basic design of proteins that supports all of these properties? Going beyond direct physical analysis, statistical analysis of genome sequences have, in recent years, provided a powerful and general approach to this problem. Using different methodologies, this approach has revealed both direct structural contacts as well as collective functional modes within protein structures. In this talk, I will present approaches for probing the physical mechanisms implied by the evolution-based models and present ideas for how such mechanisms may be constrained by and originate from the dynamics of the evolutionary process. This work represents a step towards a theory for the physics of proteins that is consistent with evolution.

The sediment-dwelling bacterium \textit{Thiovulum majus}, which swims at a speed of $600\,\mu$m/s, is one of the fastest known bacteria. When such a cell collides with a hard surface it either escapes rapidly into the bulk fluid or else becomes hydrodynamically bound to the wall. We first show that these dynamics preserve a memory of the cell's trajectory before the collision, which is gradually erased by contact with the surface. This erasure of information is consistent with a first-passage problem. Next, we investigate the two-dimensional motion of cells that are hydrodynamically bound to the surface. These cells diffuse laterally over the surface. When two cells diffuse within a critical distance of one another, they form a stable dimer of co-rotating cells. These dimers grow into two-dimensional active crystals composed of hundreds of cells. We analyze the large-scale motion of these crystals and their stability.

As we interact with the world around us, we experience a constant stream of sensory inputs, and generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state—alertness, hunger, level of stress—to produce behavior in a manner that is flexible and adaptive. While some experimental work has examined the effect of motivational states such as alertness on neuronal population dynamics, a key theoretical question is how motivational states might be maintained by the brain, and how they might interact with each other to collectively shape behavior in an adaptive manner. Here, we contrast neural population dynamics in two hypothalamic nuclei involved in control of social behavior—the ventrolateral part of ventromedial hypothalamus (VMHvl) and medial preoptic area (MPOA)—and find pronounced differences in how actions and motivational states are encoded among these cells. We hypothesize that this reflects a more general distributed framework by which the interacting nuclei of the hypothalamus shape animal behavior.

For many animals, survival depends on the ability to navigate odor plumes to their sources. This task is complicated by turbulent air motions, which break continuous odor streams emanating from sources into disconnected odor patches swept by the wind. Animal studies have revealed a general strategy to navigate odor plumes: reorient upwind when the odor is present, but go crosswind or downwind when signals become sparse to regain contact with the plume. In this strategy, the olfactory system is used to detect the identity, intensity and arrival time of odor packets, while the main directional cue is wind direction. This is because gradients of odors, which can be detected by comparing odor intensity between the two antennae, tend to fluctuate in many directions.

We have discovered that besides detecting the identity and intensity of odor packets, the Drosophila olfactory system also detects the direction of motion of odor packets. Fluid simulations and theory shows that odor motion provides a secondary directional cue, which points towards the center of the odor plume and therefore is complementary to the wind direction. Using a virtual reality setup to decouple wind from odor signal, we find that flies detect odor motion from the temporal correlations of the odor signal between its two antennae, in a computation similar to motion detection in vision. Manipulating spatio-temporal correlations in the virtual odor signal demonstrates that flies indeed exploit odor motion when navigating odor plumes. In sum, our results show that Drosophila can compute the direction of motion of odors independent of the wind, and that they use this capability in natural plume navigation. This work suggests a novel role for previously observed bilateral signal processing in the olfactory circuit.

The large-scale evolution of the influenza and SARS-CoV-2 viruses is marked by rapid turnover of genetic clades. Influenza evolution is primarily driven by selection pressure for immune escape, while SARS-Cov-2 underwent rapid post-zoonotic adaptation to human hosts. We will compare the evolutionary modes of these viruses and discuss fitness models for predictive analysis. For influenza, models informed by time-resolved sequence data, epidemiological records, and cross-neutralisation assays serve to predict evolution from one year to the next and to inform vaccine selection. For SARS-CoV-2, recent results show the impact of vaccination on viral evolution and predict selection hotspots that favor the spread of new variants. We will discuss upcoming challenges in pushing the prediction horizon of evolutionary models.

Our world is full of living creatures that must share information to survive and reproduce. As humans, we easily forget how hard it is to communicate within natural environments. So how do organisms solve this challenge, using only natural resources? Ideas from computer science, physics and mathematics, such as energetic cost, compression, and detectability, define universal criteria that almost all communication systems must meet. We use insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen’s location.

Granular matter can serve as a prototype for exploring the rich physics of many-body systems driven far from equilibrium. This talk will outline a new direction for granular physics with macroscopic particles, where acoustic levitation compensates the forces due to gravity and eliminates frictional interactions with supporting surfaces in order to focus on particle interactions. Levitating small particles by intense ultrasound fields in air makes it possible to manipulate and control their positions and assemble them into larger aggregates. Furthermore, sound scattered off individual, levitated solid particles gives rise to controllable attractive forces with neighboring particles. The small air viscosity implies that a regime of complex, underdamped dynamics can be explored, where inertial effects are important, in contrast to typical colloids in a liquid, where inertia can be neglected. I will discuss some of the key concepts underlying acoustic levitation, and show how it can be used to measure the transfer of net charge between dielectric particles in individual collisions. I will then describe how detuning an acoustic cavity can introduce active fluctuations that control the assembly statistics of small levitated particles clusters, and give examples of how interactions between neighboring levitated objects can be controlled by their shape.

The synaptic connectivity within the cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics of network activity. Finally, new work shows how the temporal shape of the learning rule determines the amount of assembly overlap that a recurrent network can tolerate - bringing us closer to a calculation of assembly capacity. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.

After 50 years of Moore’s law, it’s now possible to pack nearly 1 million transistors in the space of a paramecium, enabling tiny systems for sensing, communication, and computation. This radical miniaturization has brought with it the opportunity to build robots ten times smaller than the period at the end of this sentence. I’ll show how to merge silicon microelectronics with a new technology for actuation to make sub-100 micron legged robots. Every step in this process can be performed massively in parallel, allowing us to produce over one million robots per 4-inch wafer. I’ll present ongoing work to build autonomous, programmable microrobots, complete with memory, communication systems, sensors, and on-board power. Looking forward, I’ll argue that tiny machines present unique opportunities for both engineering the microword and for investigating the physics of living systems at one of life’s fundamental length scales.

The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, repeated measurements of chaotic systems produce arbitrarily-detailed information about the underlying attractor. Chaotic systems thus pose a unique challenge to modern statistical forecasting models, requiring representations that correctly encode their fractal geometry while also capturing their underlying mathematical properties. I will describe my recent work on representing and forecasting chaotic systems. Using a collection comprising hundreds of known chaotic dynamical systems spanning fields such as astrophysics, climatology, and biochemistry, I show that chaoticity and empirical predictability are only weakly correlated. Instead, the performance of contemporary forecasting algorithms is limited by topological properties of the underlying dynamical systems, which execute transitions among sets of unstable periodic orbits. I will show how tools from chaos can assist in general statistical learning problems, such as time series classification, importance sampling, and symbolic regression.

Our immune system detects and responds rapidly to unexpected challenges from invading pathogens. These responses are collective—orchestrated by a variety of cells throughout the body, and the molecules that they use to communicate. They are also nonlinear, using positive feedback to amplify weak signals—crucial to their function, but dangerous to the host. While we have an ever-growing list of the components of this complex system, understanding the molecular and mathematical drivers of its dynamics remains a vast challenge. This difficulty is due partly to the fact that immune responses involve cells and molecules interacting throughout an organism, where observations remain challenging. However, new tools in optical imaging and molecular biology provide unprecedented opportunities to examine the molecular and cellular underpinnings of immune responses in organisms, and to extract mathematical rules governing these responses. I will describe recent progress using these tools in the zebrafish, which presents unique opportunities for quantitative observations and perturbations in vivo. As a first example of this approach, I will discuss our observations and analysis of the random walk motility of T cells in the live zebrafish, identifying behavioral rules that enable exploration across many length scales. I will also describe ongoing work characterizing spatial patterns of inflammatory gene expression in the organism, and the avenues that this opens for investigating how the collective action of cells and molecules within tissues controls inflammatory responses.

Following its inception in the mid-19th century, our understanding of thermodynamic entropy has undergone many revisions, most notably through the development of microscopic descriptions by Boltzmann and Gibbs, which led to a deep understanding of equilibrium thermodynamics. The role of entropy has since moved beyond the traditional boundaries of equilibrium thermodynamics, towards problems for which the development of a statistical mechanical theory seems plausible but the a-priori probabilities of states are not known, making the definition and calculation of entropy-like quantities challenging. In this talk, we will discuss two new classes of methods that enable these computations: one based on pattern matching ideas from information theory, and the other based on basin volume calculations. These approaches provide us with very general frameworks for computing entropy, density of states, and entropy production in systems far from equilibrium. We will discuss applications of these ideas to a variety of contexts: from granular systems, to absorbing-state models, to active matter, in simulations and in experiments. Throughout the talk, I will highlight challenges and promising future directions for these measurements.

The idea of using light to manipulate individual particles is well known: optical traps are a widely used tool in biology and physics. Far less well known is that light can also create inter-particle forces when two or more objects are placed in an intense, unfocused light field. This force — known as optical binding — has an unusual form and is highly tunable, making it an interesting candidate for studies in self-assembly. I will describe our efforts to create an experimental platform to study the behavior of many optically bound colloids. These experiments revealed an unexpected feature of optical binding forces: many body effects can produce significant non-conservative forces which drive the system out of equilibrium. Finally, I will also describe our preliminary efforts to generate similar forces using sound instead of light.