Whole-Mouse Brain Vascular Analysis Framework: Synthetic Model-Based Validation, Informatics Platform, and Queryable Database

The past decade has seen innovative advancements in light microscopy instrumentation that have afforded the acquisition of whole-brain datasets at micrometer resolution. As the hardware and software used to automate the traditional neuroanatomical workflow become more accessible to researchers around the globe, so will the tools needed to analyze whole-brain datasets. Only recently has the focus begun to shift from the development of instrumentation towards platforms for data-driven quantitative analyses. As a consequence of this, the tools required for large-scale quantitative studies across the whole brain are few and far between. In this dissertation, we aim to change this through the development of a standardized, quantitative approach to the study of whole-brain, cerebrovasculature datasets. Our standardized and quantitative approach has four components. The first is the construction of synthetic cerebrovasculature models that can be used in conjunction with the second component, a model-based validation system. Any cerebrovasculature study conducted using imaging data must first extract the filaments embedded within that dataset. The segmentation algorithms that are commonly used to do this are frequently validated on small-scale datasets that represent only a small selection of cerebrovasculature variability. The question is how do these algorithms perform when applied to large-scale datasets. Our model-based validation system uses biologically inspired, large-scale datasets that asses the accuracy of the segmentation algorithm output against ground truth data. Once the data is segmented, we have implemented an informatics platform that calculates descriptive statistics across the entire volume. Attributes describing each vascular filament are also calculated. These include measures of vascular radius, length, surface area, volume, tortuosity, and others. The result is a massive amount of data for the cerebrovasculature segments. The question becomes how can this be analyzed sensibly. Given that both cerebrovasculature topology and geometry can be capture in graph form, we construct the fourth component of our system: a graph database that stores the cerebrovasculature. The graph model of cerebrovasculature that we have developed allows segments to be searched across the whole-brain based on their attributes and/or location. We also implemented a means to reconstruct the segments returned by a specific query for visualizations. This means that a simple text-based query can retrieve cerebrovasculature geometry and topology of the specified vasculature. For example, a query can return all vessels within the frontal cortex, those with specific attribute(s) value range(s), or any combination of attribute and location. Complex graph algorithms can also be applied, such as the shortest path between two bifurcation points or measures of centrality that are important in determining the robust and fragile aspects of blood flow through the cerebrovasculature system. To illustrate the utility of our system, we construct a whole-brain database of vascular connectivity from the Knife-Edge Scanning Microscope India Ink dataset. Using our cerebrovasculature database, we were able to study the cerebrovasculature system by issuing text-based queries to extract the vessel segments that we were interested in. The outcome of our investigation was a wealth of information about the cerebrovasculature system as a whole, and about the different classifications of vessels comprising it. The results returned from these simple queries even generated some interesting and biologically relevant questions. For instance, the profound spikes in radius distribution for some classes of vessels that did not present in other classes. We expect that the methods described in this dissertation will open the door for data-driven, quantitative investigation across the whole-brain. At the time of writing – and to the best of our knowledge that prior to this work – there was not a systemic way to assess segmentation algorithm performance, calculate attributes for each segment of vasculature extracted across the whole brain, and store those results in a queryable database that also stores geometry and topology of the entire cerebrovasculature system. We believe that our method can and will set the standard for largescale cerebrovasculature research. Therefore, in conclusion, we state that our methods contribute a standardized, quantitative approach to the study of cerebrovasculature datasets acquired using modern imaging techniques

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Structured clustering representations and methods

Rather than designing focused experiments to test individual hypotheses, scientists now commonly acquire measurements using massively parallel techniques, for post hoc interrogation. The resulting data is both high-dimensional and structured, in that observed variables are grouped and ordered into related subspaces, reflecting both natural physical organization and factorial experimental designs. Such structure encodes critical constraints and clues to interpretation, but typical unsupervised learning methods assume exchangeability and fail to account adequately for the structure of data in a flexible and interpretable way. In this thesis, I develop computational methods for exploratory analysis of structured high-dimensional data, and apply them to study gene expression regulation in Parkinson’s (PD) and Huntington’s diseases (HD). BOMBASTIC (Block-Organized, Model-Based, Tree-Indexed Clustering) is a methodology to cluster and visualize data organized in pre-specified subspaces, by combining independent clusterings of blocks into hierarchies. BOMBASTIC provides a formal specification of the block-clustering problem and a modular implementation that facilitates integration, visualization, and comparison of diverse datasets and rapid exploration of alternative analyses. These tools, along with standard methods, were applied to study gene expression in mouse models of neurodegenerative diseases, in collaboration with Dr. Myriam Heiman and Dr. Robert Fenster. In PD, I analyzed cell-type-specific expression following levodopa treatment to study mechanisms underlying levodopa-induced dyskinesia (LID). I identified likely regulators of the transcriptional changes leading to LID and implicated signaling pathways amenable to pharmacological modulation (Heiman, Heilbut et al, 2014). In HD, I analyzed multiple mouse models (Kuhn, 2007), cell-type specific profiles of medium spiny neurons (Fenster, 2011), and an RNA-Seq dataset profiling multiple tissue types over time and across an mHTT allelic series (CHDI, 2015). I found evidence suggesting that altered activity of the PRC2 complex significantly contributes to the transcriptional dysregulation observed in striatal neurons in HD

Measuring Stability of 3D Chromatin Conformations and Identifying Neuron Specific Chromatin Loops Associated with Schizophrenia Risk

The 23 pairs of chromosomes comprising the human genome are intricately folded within the nucleus of each cell in a manner that promotes efficient gene regulation and cell function. Consequently, active gene rich regions are compartmentally segregated from inactive gene poor regions of the genome. To better understand the mechanisms driving compartmentalization we investigated what would occur if this system was disrupted. By digesting the genome to varying sizes and analyzing the fragmented 3D structure over time, our work revealed essential laws governing nuclear compartmentalization. At a finer resolution within compartments, chromatin forms loop structures capable of regulating gene expression. Genome wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with the neuropsychiatric disease schizophrenia. When these SNPs are not located within a gene it is difficult to gain insight into disease pathology; however, in some cases chromatin loops may link these noncoding schizophrenia risk variants to their pathological gene targets. By generating 3D genome maps, we identified and analyzed loops of glial cells, neural progenitor cells, and neurons thereby expanding the set of genes conferring schizophrenia risk. The binding of T-cell receptors (TCRs) to foreign peptides on the surface of diseased cells triggers an immune response against the foreign invader. Utilizing available structural information of the TCR antigen interface, we developed computational methods for successful prediction of TCR-antigen binding. As this binding is a prerequisite for immune response, such improvements in binding prediction could lead to important advancements in the fields of autoimmunity and TCR design for cancer therapeutics

INTEROPERABILITY IN TOXICOLOGY: CONNECTING CHEMICAL, BIOLOGICAL, AND COMPLEX DISEASE DATA

The current regulatory framework in toxicology is expanding beyond traditional animal toxicity testing to include new approach methodologies (NAMs) like computational models built using rapidly generated dose-response information like US Environmental Protection Agency’s Toxicity Forecaster (ToxCast) and the interagency collaborative Tox21 initiative. These programs have provided new opportunities for research but also introduced challenges in application of this information to current regulatory needs. One such challenge is linking in vitro chemical bioactivity to adverse outcomes like cancer or other complex diseases. To utilize NAMs in prediction of complex disease, information from traditional and new sources must be interoperable for easy integration. The work presented here describes the development of a bioinformatic tool, a database of traditional toxicity information with improved interoperability, and efforts to use these new tools together to inform prediction of cancer and complex disease. First, a bioinformatic tool was developed to provide a ranked list of Medical Subject Heading (MeSH) to gene associations based on literature support, enabling connection of complex diseases to genes potentially involved. Second, a seminal resource of traditional toxicity information, Toxicity Reference Database (ToxRefDB), was redeveloped, including a controlled vocabulary for adverse events used to map identifiers in the Unified Medical Language System (UMLS), thus enabling a connection to MeSH terms. Finally, gene to MeSH associations were used to evaluate the biological coverage of ToxCast for cancer to understand the capacity to use ToxCast to identify chemical hazard potential. ToxCast covers many gene targets putatively linked to cancer; however, more information on pathways in cancer progression is needed to identify robust associations between chemical exposure and risk of complex disease. The findings herein demonstrate that increased interoperability between data resources is necessary to leverage the large amount of data currently available to understand the role environmental exposures play in etiologies of complex diseases.Doctor of Philosoph

EXPLORATION OF DOMAIN-SPECIFIC KNOWLEDGE GRAPHS FOR TESTABLE HYPOTHESIS GENERATION

In the span of a decade, we have brought about a fundamental shift in the way we structure, organize, store, and conceptualize biomedical datasets. Data which had previously been siloed has been gathered, organized, and aggregated into central repositories, interlinked with each other by categorizing these vast sums of knowledge into well defined ontologies. These interlinked databases, better known as knowledge graphs, have come to redefine our ability to explore the current state of our knowledge, answer complex questions about how objects relate to each other, and invent novel connections in vastly different research disciplines. With these knowledge graphs, new ideas can be quickly formulated, instead of relying upon the insight of a single scientist or small team of experts, these ideas can be made leveraging the vast historical catalog of research progress that has been captured in biomedical databases. Knowledge graphs can be used to propose hypotheses which narrow the nearly infinite array of possible explorations which can link any pair of ideas to only those which have some historical and practical considerations. In this way, we hope to utilize these knowledge graphs to produce hypotheses, promote those which are viable, and provide them to biomedical experts. In this work, we aim to develop methodologies to produce meaningful hypotheses using these graphs as inputs. We approach this problem by (i) utilizing intrinsic mathematical properties of the intermediate nodes along a pathways, (ii) translating existing biomedical ideas into graphical structures, and (iii) incorporating niche domain-specific biomedical datasets to explore domain problems. We have shown the ability of these methods to produce practical and useful hypotheses and pathways which can be utilized by experts for immediate exploration.Doctor of Philosoph

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal