Waveguide QED: Many-body bound-state effects in coherent and Fock-state scattering from a two-level system

Strong coupling between a two-level system (TLS) and bosonic modes produces dramatic quantum optics effects. We consider a one-dimensional continuum of bosons coupled to a single localized TLS, a system which may be realized in a variety of plasmonic, photonic, or electronic contexts. We present the exact many-body scattering eigenstate obtained by imposing open boundary conditions. Multiphoton bound states appear in the scattering of two or more photons due to the coupling between the photons and the TLS. Such bound states are shown to have a large effect on scattering of both Fock- and coherent-state wave packets, especially in the intermediate coupling-strength regime. We compare the statistics of the transmitted light with a coherent state having the same mean photon number: as the interaction strength increases, the one-photon probability is suppressed rapidly, and the two- and three-photon probabilities are greatly enhanced due to the many-body bound states. This results in non-Poissonian light. © 2010 The American Physical Society.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Indication of electron neutrino appearance from an accelerator-produced off-axis muon neutrino beam.

The T2K experiment observes indications of ν(μ) → ν(e) appearance in data accumulated with 1.43×10(20) protons on target. Six events pass all selection criteria at the far detector. In a three-flavor neutrino oscillation scenario with |Δm(23)(2)| = 2.4×10(-3)  eV(2), sin(2)2θ(23) = 1 and sin(2)2θ(13) = 0, the expected number of such events is 1.5±0.3(syst). Under this hypothesis, the probability to observe six or more candidate events is 7×10(-3), equivalent to 2.5σ significance. At 90% C.L., the data are consistent with 0.03(0.04) < sin(2)2θ(13) < 0.28(0.34) for δ(CP) = 0 and a normal (inverted) hierarchy.

HDAC inhibitors cause site-specific chromatin remodeling at PU.1-bound enhancers in K562 cells

BACKGROUND: Small molecule inhibitors of histone deacetylases (HDACi) hold promise as anticancer agents for particular malignancies. However, clinical use is often confounded by toxicity, perhaps due to indiscriminate hyperacetylation of cellular proteins. Therefore, elucidating the mechanisms by which HDACi trigger differentiation, cell cycle arrest, or apoptosis of cancer cells could inform development of more targeted therapies. We used the myelogenous leukemia line K562 as a model of HDACi-induced differentiation to investigate chromatin accessibility (DNase-seq) and expression (RNA-seq) changes associated with this process. RESULTS: We identified several thousand specific regulatory elements [~10 % of total DNase I-hypersensitive (DHS) sites] that become significantly more or less accessible with sodium butyrate or suberanilohydroxamic acid treatment. Most of the differential DHS sites display hallmarks of enhancers, including being enriched for non-promoter regions, associating with nearby gene expression changes, and increasing luciferase reporter expression in K562 cells. Differential DHS sites were enriched for key hematopoietic lineage transcription factor motifs, including SPI1 (PU.1), a known pioneer factor. We found PU.1 increases binding at opened DHS sites with HDACi treatment by ChIP-seq, but PU.1 knockdown by shRNA fails to block the chromatin accessibility and expression changes. A machine-learning approach indicates H3K27me3 initially marks PU.1-bound sites that open with HDACi treatment, suggesting these sites are epigenetically poised. CONCLUSIONS: We find HDACi treatment of K562 cells results in site-specific chromatin remodeling at epigenetically poised regulatory elements. PU.1 shows evidence of a pioneer role in this process by marking poised enhancers but is not required for transcriptional activation.