Active Self-Assembly of Algorithmic Shapes and Patterns in Polylogarithmic Time

We describe a computational model for studying the complexity of self-assembled structures with active molecular components. Our model captures notions of growth and movement ubiquitous in biological systems. The model is inspired by biology's fantastic ability to assemble biomolecules that form systems with complicated structure and dynamics, from molecular motors that walk on rigid tracks and proteins that dynamically alter the structure of the cell during mitosis, to embryonic development where large-scale complicated organisms efficiently grow from a single cell. Using this active self-assembly model, we show how to efficiently self-assemble shapes and patterns from simple monomers. For example, we show how to grow a line of monomers in time and number of monomer states that is merely logarithmic in the length of the line. Our main results show how to grow arbitrary connected two-dimensional geometric shapes and patterns in expected time that is polylogarithmic in the size of the shape, plus roughly the time required to run a Turing machine deciding whether or not a given pixel is in the shape. We do this while keeping the number of monomer types logarithmic in shape size, plus those monomers required by the Kolmogorov complexity of the shape or pattern. This work thus highlights the efficiency advantages of active self-assembly over passive self-assembly and motivates experimental effort to construct general-purpose active molecular self-assembly systems

Functional characterization and annotation of trait-associated genomic regions by transcriptome analysis

In this work, two novel implementations have been presented, which could assist in the design and data analysis of high-throughput genomic experiments. An efficient and flexible tiling probe selection pipeline utilizing the penalized uniqueness score has been implemented, which could be employed in the design of various types and scales of genome tiling task. A novel hidden semi-Markov model (HSMM) implementation is made available within the Bioconductor project, which provides a unified interface for segmenting genomic data in a wide range of research subjects.In dieser Arbeit werden zwei neuartige Implementierungen präsentiert, die im Design und in der Datenanalyse von genomischen Hochdurchsatz-Experiment hilfreich sein könnten. Die erste Implementierung bildet eine effiziente und flexible Auswahl-Pipeline für Tiling-Proben, basierend auf einem Eindeutigkeitsmaß mit einer Maluswertung. Als zweite Implementierung wurde ein neuartiges Hidden-Semi-Markov-Modell (HSMM) im Bioconductor Projekt verfügbar gemacht

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1-6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing

Probing the Structure and Function of Biopolymer-Carbon Nanotube Hybrids with Molecular Dynamics

Nanoscience deals with the characterization and manipulation of matter on the atomic/molecular size scale in order to deepen our understanding of condensed matter and develop revolutionary technology. Meeting the demands of the rapidly advancing nanotechnological frontier requires novel, multifunctional nanoscale materials. Among the most promising nanomaterials to fulfill this need are biopolymer-carbon nanotube hybrids (Bio-CNT). Bio-CNT consists of a single-walled carbon nanotube (CNT) coated with a self-assembled layer of biopolymers such as DNA or protein. Experiments have demonstrated that these nanomaterials possess a wide range of technologically useful properties with applications in nanoelectronics, medicine, homeland security, environmental safety and microbiology. However, a fundamental understanding of the self-assembly mechanics, structure and energetics of Bio-CNT is lacking. The objective of this thesis is to address this deficiency through molecular dynamics (MD) simulation, which provides an atomic-scale window into the behavior of this unique nanomaterial. MD shows that Bio-CNT composed of single-stranded DNA (ssDNA) self-assembles via the formation of high affinity contacts between DNA bases and the CNT sidewall. Calculation of the base-CNT binding free energy by thermodynamic integration reveals that these contacts result from the attractive pi–pi stacking interaction. Binding affinities follow the trend G \u3e A \u3e T \u3e C. MD reveals that long ssDNA sequences are driven into a helical wrapping about CNT with a sub-10 nm pitch by electrostatic and torsional interactions in the backbone. A large-scale replica exchange molecular dynamics simulation reveals that ssDNA-CNT hybrids are disordered. At room temperature, ssDNA can reside in several low-energy conformations that contain a sequence-specific arrangement of bases detached from CNT surface. MD demonstrates that protein-CNT hybrids composed of the Coxsackie-adenovirus receptor are biologically active and function as a nanobiosensor with specific recognition of Knob proteins from the adenovirus capsid. Simulation also shows that the rigid CNT damps structural fluctuations in bound proteins, which may have important ramifications for biosensing devices composed of protein-CNT hybrids. These results expand current knowledge of Bio-CNT and demonstrate the effectiveness of MD for investigations of nano-biomolecular systems

Nature’s Optics and Our Understanding of Light

Optical phenomena visible to everyone abundantly illustrate important ideas in science and mathematics. The phenomena considered include rainbows, sparkling reflections on water, green flashes, earthlight on the moon, glories, daylight, crystals, and the squint moon. The concepts include refraction, wave interference, numerical experiments, asymptotics, Regge poles, polarisation singularities, conical intersections, and visual illusions

Microstructure and Phase Behavior in Colloids and Liquid Crystals

This thesis describes our investigation of microstructure and phase behavior in colloids and liquid crystals. The first set of experiments explores the phase behavior of helical packings of thermoresponsive microspheres inside glass capillaries as a function of volume fraction. Stable helical packings are observed with long-range orientational order. Some of these packings evolve abruptly to disordered states as the volume fraction is reduced. We quantify these transitions using correlation functions and susceptibilities of an orientational order parameter. The emergence of coexisting metastable packings, as well as coexisting ordered and disordered states, is also observed. These findings support the notion of phase-transition-like behavior in quasi-one-dimensional systems. The second set of experiments investigates cross-over behavior from glasses with attractive interactions to sparse gel-like states. In particular, the vibrational modes of quasi-two-dimensional disordered colloidal packings of hard colloidal spheres with short-range attractions are measured as a function of packing fraction. A crossover from glassy to sparse gel-like states is indicated by an excess of low-frequency phonon modes. This change in vibrational mode distribution appears to arise from highly localized vibrations that tend to involve individual and/or small clusters of particles with few local bonds. These mode behaviors and corresponding structural insights may serve as a useful signature for glass-gel transitions in wider classes of attractive packings. A third set of experiments explores the director structures of aqueous lyotropic chromonic liquid crystal (LCLC) films created on square lattice cylindrical-micropost substrates. The structures are manipulated by modulating of the concentration-dependent elastic properties of LCLC s via drying. Nematic LCLC films exhibit preferred bistable alignment along the diagonals of the micropost lattice. Columnar LCLC films form two distinct director and defect configurations: a diagonally aligned director pattern with local squares of defects, and an off-diagonal configuration with zig-zag defects. The formation of these patterns appears to be tied to the relative free energy costs of splay and bend deformations in the precursor nematic films. The observed nematic and columnar configurations are understood numerically using a Landau-de Gennes free energy model. This work provides first examples of quasi-2D micropatterning of LC films in the columnar phase and the first micropatterning of lyotropic LC films in general, as well as demonstrating alignment and configuration switching of typically difficult-to-align LCLC films via bulk elastic properties