Publications

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.

The Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy, and, as such, it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and space-born instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed; ultracompact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help making progress in the different areas. New research avenues that LISA itself, or its joint exploitation with upcoming studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe.

Germline mutations upregulating RAS signaling are associated with multiple developmental disorders. A hallmark of these conditions is that the same mutation may present vastly different phenotypes in different individuals, even in monozygotic twins. Here, we demonstrate how the origins of such largely unexplained phenotypic variations may be dissected using highly controlled studies in Drosophila that have been gene edited to carry activating variants of MEK, a core enzyme in the RAS pathway. This allowed us to measure the small but consistent increase in signaling output of such alleles in vivo. The fraction of mutation carriers reaching adulthood was strongly reduced, but most surviving animals had normal RAS-dependent structures. We rationalize these results using a stochastic signaling model and support it by quantifying cell fate specification errors in bilaterally symmetric larval trachea, a RAS-dependent structure that allows us to isolate the effects of mutations from potential contributions of genetic modifiers and environmental differences. We propose that the small increase in signaling output shifts the distribution of phenotypes into a regime, where stochastic variation causes defects in some individuals, but not in others. Our findings shed light on phenotypic heterogeneity of developmental disorders caused by deregulated RAS signaling and offer a framework for investigating causal effects of other pathogenic alleles and mild mutations in general.

Positional information in developing tissues often takes the form of stripes of gene expression that mark the boundaries of a particular cell type or morphogenetic process. How stripes form is still in many cases poorly understood. Here we use optogenetics and live-cell biosensors to investigate one such pattern: the posterior stripe of brachyenteron (byn) expression in the early Drosophila embryo. This byn stripe depends on interpretation of an upstream signal – a gradient of ERK kinase activity – and the expression of two target genes tailless (tll) and huckebein (hkb) that exert antagonistic control over byn. We find that high or low doses of ERK signaling produce either transient or sustained byn expression, respectively. These ERK stimuli also regulate tll and hkb expression with distinct dynamics: tll transcription is rapidly induced under both low and high stimuli, whereas hkb transcription converts graded ERK inputs into an output switch with a variable time delay. Antagonistic regulatory paths acting on different timescales are hallmarks of an incoherent feedforward loop architecture, which is sufficient to explain transient or sustained byn dynamics and adds temporal complexity to the steady-state model of byn stripe formation. We further show that an all-or-none stimulus can be ‘blurred’ through intracellular diffusion to non-locally produce a stripe of byn gene expression. Overall, our study provides a blueprint for using optogenetic inputs to dissect developmental signal interpretation in space and time.

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the Cardiofaciocutaneous Syndrome and Arteriovenous Malformation. Evaluating variants associated with a disease is a challenge and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1 we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We used this dataset to match known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions. We found that all known and the vast majority of suspected disease-causing mutations are evolutionarily intolerable. We selected several MEK1 mutations that cannot be unambiguously assessed by automated variant prediction tools, but that are confidently identified as evolutionary intolerant and thus “damaging” by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature predicted by out computational strategy. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

During cell division, the mitotic spindle forms inside cells and segregates chromosomes. The spindle's position sets the division plane, which is essential for proper growth and development. Force mechanisms regulating the position of the spindle are not yet understood. Here, we develop a coarse-grained model of spindles in cells, which accounts for microtubule dynamics, pulling forces from cortically bounded motor proteins, and fluid drag. We show that the spindle's resistance to rotation is largely driven by pulling forces from the motor proteins rather than the drag imposed by the cytoplasm. We also show that the arrangement of motor proteins affects the spindle's resistance to rotation for configurations where multiple motors stack at the same region, the spindle's resistance to rotation significantly reduces. Our findings are consistent with measurements in human tissue culture cells, where the spindle resistance to the rotation has been quantified.