Publications

The excitonic insulator is an electronically driven phase of matter that emerges upon the spontaneous formation and Bose condensation of excitons. Detecting this exotic order in candidate materials is a subject of paramount importance, as the size of the excitonic gap in the band structure establishes the potential of this collective state for superfluid energy transport. However, the identification of this phase in real solids is hindered by the coexistence of a structural order parameter with the same symmetry as the excitonic order. Only a few materials are currently believed to host a dominant excitonic phase, Ta2NiSe5 being the most promising. Here, we test this scenario by using an ultrashort laser pulse to quench the broken-symmetry phase of this transition metal chalcogenide. Tracking the dynamics of the material's electronic and crystal structure after light excitation reveals spectroscopic fingerprints that are compatible only with a primary order parameter of phononic nature. We rationalize our findings through state-of-the-art calculations, confirming that the structural order accounts for most of the gap opening. Our results suggest that the spontaneous symmetry breaking in Ta2NiSe5 is mostly of structural character, hampering the possibility to realize quasi-dissipationless energy transport.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system’s total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system’s total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule’s shape, the membrane’s hydrophobic environment, and the lipid’s orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

Transmembrane helix folding and self-association play important roles in biological signaling and transportation pathways across biomembranes. With molecular simulations, studies to explore the structural biochemistry of this process have been limited to focusing on individual fragments of this process – either helix formation or dimerization. While at an atomistic resolution, it can be prohibitive to access long spatio-temporal scales, at the coarse grained (CG) level, current methods either employ additional constraints to prevent spontaneous unfolding or have a low resolution on sidechain beads that restricts the study of dimer disruption caused by mutations. To address these research gaps, in this work, we apply our recent, in-house developed CG model (ProMPT) to study the folding and dimerization of Glycophorin A (GpA) and its mutants in the presence of Dodecyl-phosphocholine (DPC) micelles. Our results first validate the two-stage model that folding and dimerization are independent events for transmembrane helices and found a positive correlation between helix folding and DPC-peptide contacts. The wild type (WT) GpA is observed to be a right-handed dimer with specific GxxxG contacts, which agrees with experimental findings. Specific point mutations reveal several features responsible for the structural stability of GpA. While the T87L mutant forms anti-parallel dimers due to an absence of T87 interhelical hydrogen bonds, a slight loss in helicity and a hinge-like feature at the GxxxG region develops for the G79L mutant. We note that the local changes in the hydrophobic environment, affected by the point mutation, contribute to the development of this helical bend. This work presents a holistic overview of the structural stability of GpA in a micellar environment, while taking secondary structural fluctuations into account. Moreover, it presents opportunities for applications of computationally efficient CG models to study conformational alterations of transmembrane proteins that have physiological relevance.

Many active particles, such as swimming micro-organisms or motor proteins, do work on their environment by going though a periodic sequence of shapes. Interactions between particles can lead to synchronization of their duty cycles. Here, we study the collective dynamics of a suspension of active particles coupled through hydrodynamics. We find that at high enough density the system transitions to a state of collective motion by a mechanism that is distinct from other instabilities in active matter systems. Second, we demonstrate that the emergent nonequilibrium states feature stationary chimera patterns in which synchronized and phase-isotropic regions coexist. Third, we show that in confinement, oscillatory flows and robust unidirectional pumping states exist, and can be selected by choice of alignment boundary conditions. These results point toward a new route to collective motion and pattern formation and could guide the design of new active materials.

Short-period double white dwarf (DWD) binaries will be the most prolific source of gravitational waves (GWs) for the Laser Interferometer Space Antenna (LISA). DWDs with GW frequencies below ∼1 mHz will be the dominant contributor to a stochastic foreground caused by overlapping GW signals. Population modeling of Galactic DWDs typically assumes a binary fraction of 50% and a log-uniform Zero Age Main Sequence (ZAMS) orbital period distribution. However, recent observations have shown that the binary fraction of close, solar-type stars exhibits a strong anti-correlation with metallicity which modulates the ZAMS orbital period distribution below 104 days. In this study we perform the first simulation of the Galactic DWD population observable by LISA which incorporates an empirically-derived metallicity-dependent binary fraction, using the binary population synthesis suite COSMIC and a metallicity-dependent star formation history. We compare two models: one which assumes a metallicity-dependent binary fraction, and one with a binary fraction of 50%. We repeat our analysis for three different assumptions for Roche-lobe overflow interactions. We find that while metallicity impacts the evolution and intrinsic properties of our simulated DWD progenitor binaries, the LISA-resolvable populations of the two models remain roughly indistinguishable. However, the size of the total Galactic DWD population orbiting in the LISA frequency band is reduced by more than half when accounting for a metallicity-dependent binary fraction for two of our four variations, which also lowers the effective foreground. The LISA population remains unchanged in number for two variations, highlighting the sensitivity of the population to binary evolution prescriptions.

Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a highly non-linear pressure-flow relationship. Although we understand how these elements work individually, their collective behavior remains poorly understood. In this work, we combine experiments, theory, and numerical simulations to understand the main mechanisms underlying the collective behavior of soft flow networks with elements that exhibit negative differential resistance. Strikingly, our theoretical analysis and experiments reveal that a minimal network of nonlinear resistors, which we have termed a `fluidic memristor', displays history-dependent resistance. This new class of element can be understood as a collection of hysteresis loops that allows this fluidic system to store information. Our work provides insights that may inform new applications of fluid flow networks in soft materials science, biomedical settings, and soft robotics, and may also motivate new understanding of the flow networks involved in animal and plant physiology

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux−cluster mass relation (YSZ−M), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines YSZ and concentration of ionized gas (cgas): M∝Y3/5conc≡Y3/5SZ(1−Acgas). Yconc reduces the scatter in the predicted M by ∼20−30\% for large clusters (M≳1014h−1M⊙), as compared to using just YSZ. We show that the dependence on cgas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Yconc on clusters from CAMELS simulations and show that Yconc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

Temperature is a major environmental variable influencing the distribution and behavior of plants. Recent advances have led to the identification of a role for the circadian clock in sensing temperature in Arabidopsis thaliana. Elongation growth and flowering are accelerated at warmer temperatures, and these effects are mediated by the circadian clock gene EARLY FLOWERING 3 (ELF3). ELF3 exists with a tripartite protein complex called the Evening Complex (EC) that functions as a DNA transcription repressor targeting growth-related genes. ELF3, a large scaffold protein with disordered domains, binds to the transcription factor LUX ARRYTHMO (LUX) and ELF4 to form the EC. A crucial feature of ELF3 is that it acts as a highly sensitive thermosensor that responds directly and rapidly to small increases of temperature of about 5 ºC and is rapidly reversible. At temperatures of about 22 ºC and below, the EC is active, binding and repressing the promoters of multiple growth promoting genes, reducing their expression and cell elongation. At around 27 ºC and above ELF3 undergoes rapid and reversible phase change and protein condensate formation. This temperature-dependent activity causes EC occupancy on target genes to decrease at 27 ºC, allowing their increased expression. A C-terminal prion-like domain (PrD) is sufficient for ELF3 phase change and temperature responsiveness. The PrD region contains a polyglutamine (polyQ) repeat of variable length, the size of which has been found to modulate the thermal responsiveness as measured by hypocotyl (stem) elongation and condensate formation. How the PrD is able to respond to temperature is however poorly understood. To understand the underlying biophysical basis for ELF3 thermal responsiveness, we use a polymer chain growth approach to build large ensembles and characterize monomeric ELF3-PrD at a range of polyQ lengths and temperatures. We then explore temperature-dependent dynamics of wild-type ELF3-PrD, ELF3-PrD with the variable polyQ tract removed, and a mutant (F527A) using chain growth structures as initial conformations for replica exchange (REST2) simulations. In addition to different mechanisms of temperature sensing with and without the variable polyQ tract, we find increased solvent accessibility of expanded polyQ tracts, promotion of temperature-sensitive helices adjacent to polyQ tracts, and exposure of a cluster of aromatic residues at increased temperature, all three of which promote inter-protein interaction. These results suggest a set of potential design principles for the engineering of temperature dependent molecular interactions. This has considerable potential for biotechnological application in medicine and agriculture.

We propose a new randomized method for solving systems of nonlinear equations, which can find sparse solutions or solutions under certain simple constraints. The scheme only takes gradients of component functions and uses Bregman projections onto the solution space of a Newton equation. In the special case of euclidean projections, the method is known as nonlinear Kaczmarz method. Furthermore, if the component functions are nonnegative, we are in the setting of optimization under the interpolation assumption and the method reduces to SGD with the recently proposed stochastic Polyak step size. For general Bregman projections, our method is a stochastic mirror descent with a novel adaptive step size. We prove that in the convex setting each iteration of our method results in a smaller Bregman distance to exact solutions as compared to the standard Polyak step. Our generalization to Bregman projections comes with the price that a convex one-dimensional optimization problem needs to be solved in each iteration. This can typically be done with globalized Newton iterations. Convergence is proved in two classical settings of nonlinearity: for convex nonnegative functions and locally for functions which fulfill the tangential cone condition. Finally, we show examples in which the proposed method outperforms similar methods with the same memory requirements.