Regulation as Delegation: Private Firms, Decisionmaking, and Accountability in the Administrative State

Administrative agencies increasingly enlist the judgment of private firms they regulate to achieve public ends. Regulation concerning the identification and reduction of risk-from financial, data and homeland security risk to the risk of conflicts of interest-increasingly mandates broad policy outcomes and accords regulated parties wide discretion in deciding how to interpret and achieve them. Yet the dominant paradigm of administrative enforcement, monitoring and threats of punishment, is ill suited to oversee the sound exercise of judgment and discretion

The drivers of regulatory networking: policy learning between homophily and convergence

The literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word “network” only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd

Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma

This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma

Toward Socially Equitable Conditions: Change in Complex Regulatory Systems

The purpose of this qualitative participatory action research was to explore how complexity is engaged and experienced in complex regulatory systems, and to understand how cannabis might be regulated in ways that lead to socially equitable conditions. This was accomplished by studying the lived experiences of governmental leaders charged with the responsibility of establishing regulatory frameworks for legalized cannabis where none previously existed. Using the learning history methodology, the study deeply explores the ways that complex systems coexist by capturing the lived experiences of research participants and enhance theoretical understanding of complex regulatory systems. Data collection occurred through reflective interviews, followed by distillation and thematic analysis. This resulted in the creation of a data table and a learning history artifact that were validated by distribution to research participants and used as both an actionable tool for participants and an analytical tool to distill and categorize research findings. The data table and the artifact established three main findings: complexity is both a property and characteristic of systems; complexity is not a behavior, characteristic or action of “leadership” or “leaders” in complex regulatory systems; and the interplay between social justice and social equity is complex and often oversimplified. Rather than directing, participants brought about change by building interactive trust through dialogue and relationship-building in interactive spaces across and between macro, meso, and micro systems levels. Complexity arose from these participatory human relationships when both the properties and characteristics of these systems were engaged, but the theoretical construct of complexity does not explain the presence of agency within this dynamic. By recognizing agency across all systems, structural barriers may be reduced, resulting in regulatory frameworks that may lead to more socially equitable conditions. This research contributes to leadership and complexity scholarship by empirically describing how complexity is engaged in complex regulatory systems, examining whether complexity has any connection to the practice of leadership, and adding to the emerging area of cannabis scholarship as it relates to social equity and the broader impacts of the war on drugs. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu)