VictorJuiz/thesis huggingface dataset:
Mechanistic studies of CRISPR proteins in living cells

The discovery of the CRISPR-Cas9 protein has enabled facile genome editing in living cells and organisms. Structural and biochemical studies of Cas9 endonucleases have provided critical information about the core molecular requirements of the RNA-guided DNA cleavage reaction, but questions remain about how this bacterial protein navigates chromatin to identify DNA targets within living eukaryotic cells. In particular, the in vivo kinetics of on- versus off-target binding and Cas9 dependence on chromatin environment remain largely unknown. Here we present a single-molecule analysis of Cas9 searching in living mammalian cells. We provide evidence for a diffusion- dominated search mechanism and show that off-target binding at PAMs and short seed sequences is, on average, short-lived (milleseconds to seconds) by both single- molecule and bulk imaging techniques. The differential behavior of Cas9 in closed-off (heterochromatic) versus open (euchromatic) regions of the genome is also explored via heterochromatin protein 1 (HP1) staining and nuclear masking of single-particle trajectories. Comparative analysis of trajectories from these two regions suggests that Cas9 undersamples heterochromatin and moves more compactly within these regions. These data provide the first direct visualization of Cas9 searching in living cells and offer quantitative insights into how Cas9 navigates hierarchical organization of DNA within a eukaryotic nucleus. We additionally present mechanistic investigations of unrelated CRISPR-C2c2 proteins and identify two distinct enzymatic activities– pre- CRISPR RNA (pre-crRNA) processing and crRNA-stimulated general RNase activity. Collectively, this work expands our mechanistic understanding of CRISPR biology and highlights the utility of these enzymatically diverse proteins to be harnessed for biotechnological applications.

Advisor: Doudna, Jennifer A
Tjian, Robert
Lords of All They Survey: The social and economic origins of the English state, c. 1520-1620

The argument of this dissertation is that the origins of the modern English state lie not with government agents or institutions but rather with the emergence of novel responses among private members of society to the unprecedented convergence of economic and social change experienced in the period of roughly 1520 to 1620. Large-scale economic instability and social restructuring created incentives, particularly for property owners, to find methods for cultivating financial and social predictability among increasingly mobile and anonymous populations. The result was a culture of impersonality and homogeneity that constructed both capitalism and the modern state in a unified but diffuse historical process. This dissertation studies the development of the state through five interdependent aspects. First is the widespread use of written financial accounts and increasingly hierarchic organization of managerial labor on landed estates that became bureaucratization. Second, a close analysis of early mapmaking shows that cartography was initially a technology of real property litigation and estate management, and that the government scrambled to catch up with the process of territorialization. Third, territorial conceptions of real property put pressure on courts of law to protect changing types of claims to property, which resulted in a theoretically universalizable legal system based in artificial reason rather than custom, allowing for the expansive monopolization of justice. Fourth, popular perception of vagrancy as a national crisis challenged the traditional practice of mobility as a trait of rulership, eventually motivating the idea of spatially centralized governance in relation to increasingly peripatetic subjects. Fifth, and last, cultural anxiety caused by awareness of these social and economic behaviors becoming political practice is demonstrated in the literature of the time culminating in the utter finality of Hobbes’s Leviathan as a political philosophy of sovereignty grounded in a spatial theory of human sense perception. Each study shows that these technologies had social and economic origins to which the government was forced to adapt to, and eventually adopt, thus becoming a modern state.

Advisor: Shagan, Ethan H
Structural investigation of RNA-guided nuclease proteins

RNA-guided nuclease proteins exist in all three domains of life and perform a diverse array of essential cellular functions. In eukaryotes, RNA-guided Argonaute (Ago) proteins have garnered significant interest due to their role as the core protein in RNA interference (RNAi). Although the function of prokaryotic Argonautes (pAgos) remains elusive, recent studies suggest that pAgos may function as a novel form of host-defense through cleavage of foreign genetic elements. Prokaryotes have also evolved a diverse set of CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated) systems as a form of adaptive immunity. Similar to Ago proteins, CRISPR-Cas systems utilize noncoding RNA-directed nuclease proteins for recognition and cleavage of invasive nucleic acid. Structural investigation of Argonaute and CRISPR-Cas nuclease proteins provides insight into their targeting mechanisms, assists in comparing these two immune systems, and aids in repurposing these enzymes for technological applications./nWe show here that the CRISPR-associated Marinitoga piezophila Argonaute (MpAgo) protein cleaves single-stranded target sequences using 5′-hydroxylated guide RNAs rather than the 5′-phosphorylated guides used by all known Argonautes. A 2.0-Å resolution crystal structure of an MpAgo–RNA complex reveals a guide strand binding site comprising residues that block 5′ phosphate interactions. A 3.2-Å resolution crystal structure of MpAgo bound to a 21-nucleotide RNA guide and a complementary 21-nucleotide ssDNA target advances our understanding of the diversity of target recognition mechanisms by Argonaute proteins./nSmall bacteriophage-encoded anti-CRISPR proteins (Acrs) can inactivate Cas9 of type II CRISPR-Cas systems, providing an efficient off-switch for Cas9-based applications. However, the mechanisms and specificities of these Acrs remain unknown. Here we show that AcrIIC1, the most diverse of the known Acr proteins, blocks DNA cutting by multiple divergent Cas9 homologs. A 1.5-Å resolution crystal structure of an AcrIIC1-Cas9 HNH domain complex revealed that AcrIIC1 binding occludes the HNH active site and blocks Cas9 conformational changes required for enzyme activity, halting cleavage on both strands of the target DNA. This mechanism functions in cell-based assays to block the genome editing activity of distinct Cas9 proteins and explains how a single protein has evolved broad inhibitory activity of CRISPR-Cas9.

Advisor: Doudna, Jennifer A
Two-dimensional van der Waals Structures: Emergent Properties and Applications

Since the discovery of graphene in 2004, there has been a tremendous effort in materials science, physics, and chemistry to isolate and research the properties of other two-dimensional compounds. From a historical view, these pursuits and interests in these materials may seem frivolous, as the entire field of nanomaterials has moved from zero-dimensional quantum dots, to one-dimensional carbon nanotubes, to present day two-dimensional materials. However, the uniform and large scale growth of these materials is definitely a factor which has contributed to this field's longevity in research and driven interest in future device integration. In addition, the dangling bond free surface and chemical stability has played an important role in the fabrication of such devices and observation of unique physical and chemical phenomena./nIn particular, the lack of bonding in the out-of-plane direction means that these bulk crystals are only held together by van der Waals forces. Thus, the material's layer geometry has a significant effect on the van der Waals crystal's observable properties. We demonstrate how the layer stacking geometry can significantly alter the nonlinear optical properties of the crystal, as shown with second harmonic generation. Furthermore, these two-dimensional crystals allows for a unique opportunity to artificially create nanomaterials composed of distinct crystals, so-called van der Waals heterostructures. These heterostructures may be assembled using chemical means, or via manual transfer, both of which are extensively covered in this thesis. Chemically assembled heterostructures of single layer graphene and the transition metal dichalcogenide molybdenum disulfide form all-two-dimensional transistors, capable of demonstrating logic. Manually transferring exfoliated crystals can lead to both the demonstration of devices, as well as the observation of new physical phenomena, such as the Coulomb drag of transition metal dichalcogenides.

Advisor: Zhang, Xiang
Affective Citizenship: Gender and Narratives of Affiliation in Multi-Ethnic British Literature

This dissertation explores the vexed or thwarted longing for community shaping multi-ethnic British fiction from the 1970s onwards. I examine the work of five authors—Buchi Emecheta, Zadie Smith, Monica Ali, Alan Hollinghurst, and Jackie Kay—whose writings foreground the affective dimensions of British citizenship and the complex processes of affiliation and disaffiliation by which black, Asian, and queer Britons claim belonging in the imagined community of the nation. At a time when postcolonial literary studies increasingly privileges, without really interrogating, an idealized rhetoric of “conviviality,” the negative, anti-relational, and disaggregating emotions circulating in their works bring into uncomfortable focus the factors that undermine the forging of politicized identities and communities in black Britain. Their writings also converge in a discursive concern with what I call, after Edward Said, “affiliation,” a process in which citizenship claims are facilitated by prosthetic or proxy figures hailing from often remote cultural quarters, ranging from the English ice dancers Torvill and Dean to the American blues singer Bessie Smith./nThe alternative kinship structures explored by these writers register the shifting meanings of family in postwar Britain, beginning with the pathologization of the black family in the 1950s and 1960s and coalescing in recent years around a similar stigmatization of the Asian family. The literary treatment of male and female communities of contest in multi-racial Britain also registers authors’ attempts to resolve a crisis of self-definition caused by the rise and fall of “black” as a political identity in the years since the “Windrush” generation of Afro-Caribbean migrants in the 1950s. Drawing on the works of queer and affect theorists, my analysis especially foregrounds the gendering of affiliative communities. While narratives of cross-cultural and cross-historical affiliation have the potential to disrupt normative ideologies of gender and domesticity, I also show how they indulge a sentimental narrative of women’s community predicated on cultural assumptions about women’s exemplary capacity for feeling.

Advisor: Ellis, Nadia
Receding Horizon Control with Sliding Surfaces and its Application to Vehicle Dynamics

In light of the current surge in mobile robotics development, receding horizon control has become of high interest to both academia and industry. This control technique is a systematic way to compute intelligent control actions while accounting for complicated system dynamics, forecasted environment information, and system constraints. This work contributes to the field of receding horizon control by merging sliding surfaces into the controller design process. The resulting methodology is named receding horizon sliding control. It is shown that the invariance properties of sliding surfaces can be exploited for deriving provably stable and persistently feasible controllers for a wide class of constrained nonlinear systems. Furthermore, for linear systems this work reduces the complexity of receding horizon tracking controllers compared to current state-of-the-art methods by exploiting the flatness of sliding hyperplanes. The practicality of the proposed control approach is demonstrated by using applications from vehicle dynamics. The applications include an autonomous underwater robotics problem, an automotive engine control scenario, and a self-driving vehicle control case study. Simulations and real-world experiments confirm the effectiveness of the developed control methodology. The results indicate that the proposed control scheme is typically easy to tune, behaves well under system uncertainty, and has manageable computational requirements that make it amenable to fast systems with sampling times of the order of fractions of seconds.

Advisor: Borrelli, Francesco
Learning and Inferring Representations of Data in Neural Networks

Finding useful representations of data in order to facilitate scientific knowledge generation is a ubiquitous concept across disciplines. Until the development of machine learning and statistical methods with hidden or latent representations, useful representations of data were generated “by hand” through scientific modeling or simple measurement observations. Scientific models often make explicit the underlying structure of a system which generates the data we observe and measure. To test a model, inferences must be made about the free parameters and the distributions of latent or unmeasured variables in the model conditioned on the data collected. At this time, many scientific disciplines such as astronomy, particle physics, wildlife conservation, and neuroscience have been moving towards collecting datasets that are large and complex enough so that no human will ever look at and analyze all measurements by hand. Datasets of this scale present an interesting scientific opportunity: to be able to derive insight into the structure of natural systems by creating models which can adapt themselves to the latent structure of large amounts of data, often called data-driven hypothesis testing. The three topics of this work fall under this umbrella, but are largely independent research directions. First, we show how deep learning can be used to infer representations of neural data which can be used to find the limits of information content in sparsely sampled neural activity and applied to improving the performance of brain-computer interfaces. Second, we derive a circuit model for a network neurons which implements approximate inference in a probabilistic model given the biological constraint of neuron-local computations. Finally, we provide a theoretical and empirical analysis of a family of methods for learning linear representations which have low coherence (cosine-similarity) and show that linear methods have limited applicability as compared to nonlinear, recurrent models which solve the same problem. Together, these results provide insight into how scientists and the brain can learn useful representations of data in deep and single layer networks.

Advisor: DeWeese, Michael R
Models of Competition for Intelligent Transportation Infrastructure: Parking, Ridesharing, and External Factors in Routing Decisions

Competition underlies much of the complexity of modern transportation systems and accurately modeling the incentives transportation users face is critical in designing the smart cities of tomorrow. In this work, we extend traditional non-atomic routing games to model several different scenarios relevant to modern cities. In Chapter 2, we review results on continuous population potential games and routing games and discuss linear programming interpretations of individual drivers’ decisions and their relationship to the Wardrop equilibria. We include discussion of the variable demand case and upper bounds on the price of anarchy. In Chapter 3, we combine an observable queueing game with a routing game to develop a queue-routing game useful for modeling traffic circling looking for parking in urban centers. We use this framework to model several parking situations in downtown Seattle. In Chapter 4, we discuss connections between linear programming formulations of shortest path problems and linear programming formulations of Markov decision processes (MDPs). We use these connections to motivate a stochastic population game we call a Markov decision process routing game where each infinitesimal agent solves an MDP as opposed to a shortest path problem. We develop this game in the infinite-horizon, average-cost case and in the finite-horizon, total-cost case. We comment on connections with traditional routing games as well as other stochastic population game formulations such as mean field games. Paralleling results for traditional routing games, we derive upper bounds on the price of anarchy and comment on the existence of Braess paradox. We apply this framework to model ridesharing drivers competing for fares and to develop a model of circling traffic competing for street parking. Finally, in Chapter 5, we consider a bi-criterion routing game where drivers consider travel time along with some external factor. Preference for this external factor is represented by general distribution over a type parameter that can be supported both above and below zero. We develop the appropriate equilibrium concept and show how this framework can be used to model the transportation data market, drivers’ interest in their location privacy, and commuters comparing two different commuting options. In Chapter 6, we conclude and make final comments on modeling considerations and future research directions.

Advisor: Sastry, Shankar
Engineered production of fluorinated polyketides inspired by mechanistic studies of a polyketide synthase

Macrolide natural products have been a rich source of medicinally relevant molecules. Macrolides are assembled by large protein complexes known as polyketide synthases (PKSs) via polymerization reactions of simple acetate and propionate derived building blocks. While these enzymes are capable of generating molecules of impressive structural diversity, the introduction of nonnative and potentially bioactive functional groups into the backbone of these molecules remains challenging and is mostly limited to synthetic methods. The incorporation of fluorine atoms or fluorinated moieties into both synthetic and semi-synthetic medicinally relevant molecules has been shown to improve their bioactivity, bioavailability, and stability. However, the very properties that make fluorine a valuable component of pharmaceuticals also makes it challenging to incorporate via synthetic methods. Seeking to capitalize on the modular nature of PKSs, previous work in our lab has shown that fluorinated polyketides can be synthesized by using fluorinated analogs of PKS building blocks. The work described in this dissertation follows a mechanistic approach to fluorinated extender unit incorporation into polyketides. By examining the nature of fluorinated extender unit incorporation into the 6-deoxyerythronolide B synthase modules, we were able to build a platform showing suitable promise for the site-specific incorporation of fluorine into the backbone of medicinally relevant natural products.

Advisor: Chang, Michelle C. Y.
Phase Transitions of Random Constraints Satisfaction Problem

Constraints satisfaction problem (CSP) is a family of computation problems that are generally hard to solve in the worst case, which motivates the study of average cases by looking at random CSPs. This thesis studies problems related to random constraints satisfaction problems, in particular its different phase transitions in the large system limit as the level of constraints increases./nThe first part of this thesis studies the number of solutions in a typical problem instance. It has long been observed that shortly before the satisfiability phase transition where solutions stop to exist, the number of solutions in a typical instance no longer concentrate around its expectation. Guided by the 1-step replica symmetry breaking heuristics in statistical physics, we prove the correct formula for the typical number of the solutions up to the exponent./nThe second part focus on the clustering threshold around which algorithms have been observed to slow down. Different opinions exist for the reason of this algorithmic barrier. One is the shattering of solution space which is conjectured to happen at the clustering threshold. The other is the onset of frozen variables happening at a nearby rigidity threshold. Previous analysis on the clustering threshold was not strong enough to differentiate the two phase transitions. Using a detailed analysis of certain distributional recursion, we show that the reconstruction threshold on trees, which is conjectured to coincide with the clustering threshold, is strictly smaller than the rigidity threshold, laying ground for further studies./nThe last part of the thesis studied the Glauber dynamics of graph colorings on $d$-regular trees. By comparing the Glauber dynamics to a variant of block dynamics, we show that the mixing time, and hence the speed of the related MCMC algorithm, undergoes a phase transition at the reconstruction threshold.

Advisor: Sly, Allan M
Director Based Finite Element Modeling of Rapidly Deforming Open-Celled Foams

Traumatic brain injury (TBI) has become an serious concern for athletes and soldiers. Helmets used for preventing injury in athletics can borrow information gathered from au- tomobile safety studies. In contrast, TBI threats for soldiers exist at different scales than studied in those works, thus requiring specialized studies to address these threats. Combat helmets consist of a stiff composite shell and a foam liner system. The liners serve to mitigate injuries caused by the impact of the shell upon the head. The aim of this work is to develop a numerical simulation that is capable of recreating the micro-mechanical behavior of the foam liners at deformation rates anticipated in a combat environment./nIn this work a three dimensional nonlinear beam network model and foam geometry creation tool were developed to recreate the microstructure of open-celled foam pads. The model is designed to be stable under high rates of compression and to be able to undergo self- contact to recreate a stiffening response seen at large strains. The beam network model was used to perform rapid compression tests at various impact speeds. Shock wave speeds were calculated for each test. The sock speed data was mapped back to macro-scale parameters using the rigid-perfectly-plastic-locking (RPPL) model of cellular deformation. The model’s prediction of the locking strain aligns with data studied at the macro-scale./nThe work aims to demonstrate a tool that can be used to improve the protection provided foam liners at impact speeds that could be seen in combat.

Advisor: Zohdi, Tarek I