Publications

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus

Understanding the transport properties of microorganisms and self-propelled particles in porous media has important implications for human health as well as microbial ecology. In free space, most microswimmers perform diffusive random walks as a result of the interplay of self-propulsion and orientation decorrelation mechanisms such as run-and-tumble dynamics or rotational diffusion. In an unstructured porous medium, collisions with the microstructure result in a decrease in the effective spatial diffusivity of the particles from its free-space value. Here, we analyze this problem for a simple model system consisting of noninteracting point particles performing run-and-tumble dynamics through a two-dimensional disordered medium composed of a random distribution of circular obstacles, in the absence of Brownian diffusion or hydrodynamic interactions. The particles are assumed to collide with the obstacles as hard spheres and subsequently slide on the obstacle surface with no frictional resistance while maintaining their orientation, until they either escape or tumble. We show that the variations in the long-time diffusivity can be described by a universal dimensionless hindrance function f (φ,Pe) of the obstacle area fraction φ and Péclet number Pe, or ratio of the swimmer run length to the obstacle size. We analytically derive an asymptotic expression for the hindrance function valid for dilute media (Peφ 1), and its extension to denser media is obtained using stochastic simulations. As we explain, the model is also easily generalized to describe dispersion in three dimensions.

When a founder cell and its progeny divide with incomplete cytokinesis, a network forms in which each intercellular bridge corresponds to a past mitotic event. Such networks are required for gamete production in many animals, and different species have evolved diverse final network topologies. Although mechanisms regulating network assembly have been identified in particular organisms, we lack a quantitative framework to understand network assembly and inter-species variability. Motivated by cell networks responsible for oocyte production in invertebrates, where the final topology is typically invariant within each species, we devised a mathematical model for generating cell networks, in which each node is an oscillator and, after a full cycle, the node produces a daughter to which it remains connected. These cell cycle oscillations are transient and coupled via diffusion over the edges of the network. By variation of three biologically motivated parameters, our model generates nearly all such networks currently reported across invertebrates. Furthermore, small parameter variations can rationalize cases of intra-species variation. Because cell networks outside of the ovary often form less deterministically, we propose model generalizations to account for sources of stochasticity.

High wall shear stress (WSS) is associated with risk of atherosclerotic plaque rupture, but there are numerous gaps in validating ultrasound-derived measurements of the parameter. Two major challenges are using simple models of stenosis and only evaluating WSS along a single 2D plane. To overcome these limitations, a novel simulation framework is herein demonstrated. The framework first models volumetric blood flow in actual stenosed human carotid artery geometries (using an immersed interface method (IIM) fluid structure interaction solver) and calculates the associated WSS. Then, the framework projects the modeled blood flow onto scatterers in Field II simulations of its ultrasonic interrogation. Volumetric ultrasound vector Doppler (VD) imaging using an elevationally swept L7-4 linear array was simulated in Field II, with variations in transmit sequences and flow conditions. In a ~55% stenosed human carotid artery under 600 mL/min flow, Bland-Altman analysis showed that a 3-angle plane wave (PW) transmit scheme estimated WSS with 0.04±0.64 Pa error (bias ± 95% CI) relative to the IIM ground truth, whereas transmitting with 5 angles increased accuracy, but decreased precision to -0.01±1.07 Pa, due to aliasing. These findings illustrate that the simulation framework enables direct comparison of data acquisition and processing methods for efficient development, validation, and refinement of WSS estimation methods in realistic clinical environments.

Theranostic materials research is experiencing rapid growth driven by the interest in integrating both therapeutic and diagnostic modalities. These materials offer the unique capability to not only provide treatment but also track the progression of a disease. However, to create an ideal theranostic biomaterial without compromising drug encapsulation, diagnostic imaging must be optimized for improved sensitivity and spatial localization. Herein, we create a protein-engineered fluorinated coiled-coil fiber, Q2TFL, capable of improved sensitivity to 19F magnetic resonance spectroscopy (MRS) detection. Leveraging residue-specific noncanonical amino acid incorporation of trifluoroleucine (TFL) into the coiled-coil, Q2, which self-assembles into nanofibers, we generate Q2TFL. We demonstrate that fluorination results in a greater increase in thermostability and 19F magnetic resonance detection compared to the nonfluorinated parent, Q2. Q2TFL also exhibits linear ratiometric 19F MRS thermoresponsiveness, allowing it to act as a temperature probe. Furthermore, we explore the ability of Q2TFL to encapsulate the anti-inflammatory small molecule, curcumin (CCM), and its impact on the coiled-coil structure. Q2TFL also provides hyposignal contrast in 1H MRI, echogenic signal with high-frequency ultrasound and sensitive detection by 19F MRS in vivo illustrating fluorination of coiled-coils for supramolecular assembly and their use with 1H MRI, 19F MRS and high frequency ultrasound as multimodal theranostic agents.

Transient receptor potential (TRP) ion channels are among the most well-studied classes of temperature-sensing molecules. Yet, the molecular mechanism and thermodynamic basis for the temperature sensitivity of TRP channels remains to this day poorly understood. One hypothesis is that the temperature-sensing mechanism can simply be described by a difference in heat capacity between the closed and open channel states. While such a two-state model may be simplistic it nonetheless has descriptive value, in the sense that it can be used to compare overall temperature sensitivity between different channels and mutants. Here, we introduce a mathematical framework based on the two-state model to reliably extract temperature-dependent thermodynamic potentials and heat capacities from measurements of equilibrium constants at different temperatures. Our framework is implemented in an open-source data analysis package that provides a straightforward way to fit both linear and nonlinear van ’t Hoff plots, thus avoiding some of the previous, potentially erroneous, assumptions when extracting thermodynamic variables from TRP channel electrophysiology data.

The extracellular signal–regulated kinase (ERK) controls multiple critical processes in the cell and is deregulated in human cancers, congenital abnormalities, immune diseases, and neurodevelopmental syndromes. Catalytic activity of ERK requires dual phosphorylation by an upstream kinase, in a mechanism that can be described by two sequential Michaelis-Menten steps. The estimation of individual reaction rate constants from kinetic data in the full mechanism has proved challenging. Here, we present an analytically tractable approach to parameter estimation that is based on the phase plane representation of ERK activation and yields two combinations of six reaction rate constants in the detailed mechanism. These combinations correspond to the ratio of the specificities of two consecutive phosphorylations and the probability that monophosphorylated substrate does not dissociate from the enzyme before the second phosphorylation. The presented approach offers a language for comparing the effects of mutations that disrupt ERK activation and function in vivo. As an illustration, we use phase plane representation to analyze dual phosphorylation under heterozygous conditions, when two enzyme variants compete for the same substrate.

Computers, as well as most neuromorphic hardware systems, use central processing and top-down algorithmic control to train for machine learning tasks. In contrast, brains are ensembles of 100 billion neurons working in tandem, giving them tremendous advantages in power efficiency and speed. Many physical systems `learn' through history dependence, but training a physical system to perform arbitrary nonlinear tasks without a processor has not been possible. Here we demonstrate the successful implementation of such a system - a learning meta-material. This nonlinear analog circuit is comprised of identical copies of a single simple element, each following the same local update rule. By applying voltages to our system (inputs), inference is performed by physics in microseconds. When labels are properly enforced (also via voltages), the system's internal state evolves in time, approximating gradient descent. Our system; it requires no processor. Once trained, it performs inference passively, requiring approximately 100~W of total power dissipation across its edges. We demonstrate the flexibility and power efficiency of our system by solving nonlinear 2D classification tasks. Learning meta-materials have immense potential as fast, efficient, robust learning systems for edge computing, from smart sensors to medical devices to robotic control.

The prospect of substantial reductions in the power consumption of AI is a major motivation for the development of neuromorphic hardware. Less attention has been given to the complementary research of power-efficient learning rules for such systems. Here we study self-learning physical systems trained by local learning rules based on contrastive learning. We show how the physical learning rule can be biased toward finding power-efficient solutions to learning problems, and demonstrate in simulations and laboratory experiments the emergence of a trade-off between power-efficiency and task performance.

Nearly, all dense suspensions undergo dramatic and abrupt thickening transitions in their flow behavior when sheared at high stresses. Such transitions occur when the dominant interactions between the suspended particles shift from hydrodynamic to frictional. Here, we interpret abrupt shear thickening as a precursor to a rigidity transition and give a complete theory of the viscosity in terms of a universal crossover scaling function from the frictionless jamming point to a rigidity transition associated with friction, anisotropy, and shear. Strikingly, we find experimentally that for two different systems—cornstarch in glycerol and silica spheres in glycerol—the viscosity can be collapsed onto a single universal curve over a wide range of stresses and volume fractions. The collapse reveals two separate scaling regimes due to a crossover between frictionless isotropic jamming and frictional shear jamming, with different critical exponents. The material-specific behavior due to the microscale particle interactions is incorporated into a scaling variable governing the proximity to shear jamming, that depends on both stress and volume fraction. This reformulation opens the door to importing the vast theoretical machinery developed to understand equilibrium critical phenomena to elucidate fundamental physical aspects of the shear thickening transition.