Publications

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms.

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine-learning and template-fitting methods by building template SEDs directly from the training data. This is made computationally tractable with Gaussian Processes operating in flux--redshift space, encoding the physics of redshift and the projection of galaxy SEDs onto photometric band passes. This method alleviates the need of acquiring representative training data or constructing detailed galaxy SED models; it requires only that the photometric band passes and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i-magnitude-selected, spectroscopically-confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes, or to simulate populations of galaxies with realistic fluxes and redshifts, for example. This method opens a new era in which photometric redshifts for large photometric surveys are derived using a flexible yet physical model of the data trained on all available surveys (spectroscopic and photometric).

We derive the self-energy functional theory for bosonic lattice systems with broken U(1) symmetry by parametrizing the bosonic Baym-Kadanoff effective action in terms of one- and two-point self-energies. The formalism goes beyond other approximate methods such as the pseudoparticle variational cluster approximation, the cluster composite boson mapping, and the Bogoliubov+U theory. It simplifies to bosonic dynamical-mean-field theory when constraining to local fields, whereas when neglecting kinetic contributions of noncondensed bosons, it reduces to the static mean-field approximation. To benchmark the theory, we study the Bose-Hubbard model on the two- and three-dimensional cubic lattice, comparing with exact results from path integral quantum Monte Carlo. We also study the frustrated square lattice with next-nearest-neighbor hopping, which is beyond the reach of Monte Carlo simulations. A reference system comprising a single bosonic state, corresponding to three variational parameters, is sufficient to quantitatively describe phase boundaries and thermodynamical observables, while qualitatively capturing the spectral functions, as well as the enhancement of kinetic fluctuations in the frustrated case. On the basis of these findings, we propose self-energy functional theory as the omnibus framework for treating bosonic lattice models, in particular, in cases where path integral quantum Monte Carlo methods suffer from severe sign problems (e.g., in the presence of nontrivial gauge fields or frustration). Self-energy functional theory enables the construction of diagrammatically sound approximations that are quantitatively precise and controlled in the number of optimization parameters but nevertheless remain computable by modest means.

β-amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating nonnatural amino acids typically requires representing their side chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state of the art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for β3-amino acids.

Among the population of known galactic black hole X-ray binaries, GRS 1915+105 stands out in multiple ways. It has been in continuous outburst since 1992, and has shown a wide range of different states that can be distinguished by their timing and spectral properties. These states, also observed in IGR J17091-3624, have in the past been linked to accretion dynamics. Here, we present the first comprehensive study into the long-term evolution of GRS 1915+105, using the entire data set observed with RXTE over its sixteen-year lifetime. We develop a set of descriptive features allowing for automatic separation of states, and show that supervised machine learning in the form of logistic regression and random forests can be used to efficiently classify the entire data set. For the first time, we explore the duty cycle and time evolution of states over the entire sixteen-year time span, and find that the temporal distribution of states has significantly changed over the span of the observations. We connect the machine classification with physical interpretations of the phenomenology in terms of chaotic and stochastic processes.

Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, and their approach to dealing with the advent of radical changes in hardware architecture. The codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.

The spatial variations of the velocity field of local stars provide direct evidence of Galactic differential rotation. The local divergence, shear, and vorticity of the velocity field---the traditional Oort constants---can be measured based purely on astrometric measurements and in particular depend linearly on proper motion and parallax. I use data for 304,267 main-sequence stars from the Gaia DR1 Tycho-Gaia Astrometric Solution to perform a local, precise measurement of the Oort constants at a typical heliocentric distance of 230 pc. The pattern of proper motions for these stars clearly displays the expected effects from differential rotation. I measure the Oort constants to be: A = 15.3+/-0.4 km/s/kpc, B = -11.9+/-0.4 km/s/kpc, C = -3.2+/-0.4 km/s/kpc and K = -3.3+/-0.6 km/s/kpc, with no color trend over a wide range of stellar populations. These first confident measurements of C and K clearly demonstrate the importance of non-axisymmetry for the velocity field of local stars and they provide strong constraints on non-axisymmetric models of the Milky Way.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design 15–50 residue disulfide-crosslinked and heterochiral N-C backbone-cyclized peptides. These peptides are exceptionally stable to thermal and chemical denaturation, and twelve experimentally-determined X-ray and NMR structures are nearly identical to the computational models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

One of the most demanding tasks in astronomical image processing---in terms of precision---is the centroiding of stars. Upcoming large surveys are going to take images of billions of point sources, including many faint stars, with short exposure times. Real-time estimation of the centroids of stars is crucial for real-time PSF estimation, and maximal precision is required for measurements of proper motion. The fundamental Cram\'{e}r-Rao lower bound sets a limit on the root-mean-squared-error achievable by optimal estimators. In this work, we aim to compare the performance of various centroiding methods, in terms of saturating the bound, when they are applied to relatively low signal-to-noise ratio unsaturated stars assuming zero-mean constant Gaussian noise. In order to make this comparison, we present the ratio of the root-mean-squared-errors of these estimators to their corresponding Cram\'{e}r-Rao bound as a function of the signal-to-noise ratio and the full-width at half-maximum of faint stars. We discuss two general circumstances in centroiding of faint stars: (i) when we have a good estimate of the PSF, (ii) when we do not know the PSF. In the case that we know the PSF, we show that a fast polynomial centroiding after smoothing the image by the PSF can be as efficient as the maximum-likelihood estimator at saturating the bound. In the case that we do not know the PSF, we demonstrate that although polynomial centroiding is not as optimal as PSF profile fitting, it comes very close to saturating the Cram\'{e}r-Rao lower bound in a wide range of conditions. We also show that the moment-based method of center-of-light never comes close to saturating the bound, and thus it does not deliver reliable estimates of centroids.

We introduce a fast algorithm for computing volume potentials - that is, the convolution of a translation invariant, free-space Green's function with a compactly supported source distribution defined on a uniform grid. The algorithm relies on regularizing the Fourier transform of the Green's function by cutting off the interaction in physical space beyond the domain of interest. This permits the straightforward application of trapezoidal quadrature and the standard FFT, with superalgebraic convergence for smooth data. Moreover, the method can be interpreted as employing a Nystrom discretization of the corresponding integral operator, with matrix entries which can be obtained explicitly and rapidly. This is of use in the design of preconditioners or fast direct solvers for a variety of volume integral equations. The method proposed permits the computation of any derivative of the potential, at the cost of an additional FFT.