DNA damage occurs regularly, and although the cell has numerous repair mechanisms to counteract it, some DNA lesions may persist. Many of these lesions can be bypassed by DNA and RNA polymerase, with replicational mutagenesis leading to permanent mutations in the genomic sequence, and transcriptional mutagenesis leading to mutant transcripts that may direct the production of mutant proteins. We hypothesized that transcriptional mutagenesis is a mechanism for initiating adaptive mutagenesis, which occurs in nondividing cells in direct response to a selective pressure, allowing cells to overcome the selection and resume cell division. Our lab has previously demonstrated that DNA damage leading to adaptive mutagenesis in yeast is biased to the template strand relative to transcription, supporting this hypothesis. Unexpectedly, DNA damage leading to replicational mutations in our system is similarly skewed to this strand, although the sequences surrounding these two classes of mutations seems to differ. We hypothesize that sequence context is important for determining whether certain mutation sites are more likely to contribute to replicational or adaptive mutagenesis. To address this hypothesis, our aim was to create Trp5 mutant yeast strains using CRISPR/Cas9 in which the mutation occurred in a sequence context-specific manner, and the reversion of which could be scored for both replicational and adaptive mutagenesis. The CRISPR/Cas9 system required generation of a plasmid containing the target sgRNA (via molecular cloning techniques) as well as a linear repair template with the mutation of interest (via a two-step PCR protocol). Creating both of these components proved challenging, and optimization of the protocols will be required. Once we have all the components for CRISPR/Cas9-mediated mutagenesis, future research involves creating the mutant strains and assessing whether the sequence context affects the mutation frequency with respect to either replicational or adaptive mutagenesis.

In the US over 100 million people live with diabetes or pre-diabetes. The economic burden of this is approximately $327 billion every year. This study seeks to establish an alternative mode of insulin production using a polyethylene glycol (PEG) transformation of Pleurotus ostreatus. P. ostreatus is a valuable target for genetic transformation due to its lack of endotoxins, rapid growth, and fully sequenced genome. In this study, I transformed P. ostreatus using PEG with a plasmid containing the human insulin gene, a green fluorescent protein (GFP) reporter gene, and a selectable resistance gene. Transformed cells were selected using hygromycin, extracted, and regenerated on growth media. Confocal microscopy confirmed the presence of the GFP and presumably the human insulin gene. An ELISA for insulin and proinsulin will be used in the upcoming months to test for genetic expression, and to determine the efficacy of protein folding in the transgenic fungal cells. This has the potential to not only expand the market for diabetic treatment options, but it initiates a valuable conversation about the importance of diversifying production methods and costs in the treatment of diabetes.

In synthetic chemistry, the direct functionalization of C-H bonds with oxygen-containing groups is a powerful strategy to efficiently synthesize molecules used in materials and therapeutics. However, current methods to accomplish such reactions suffer from limited substrate scope, selectivity, and tolerance towards other functional groups. Iron-dependent enzymes represent a promising solution to this problem, as they are known to mediate a plethora of complex oxygenation reactions in a highly selective fashion while using inexpensive and earth-abundant reagents. In prior work, we found that Fe(II) 2-oxoglutarate dependent hydroxylases (Fe(II)/2OGs) exhibit non-native oxyfunctionalization activity on olefinic amino acids. Here, we explore the ability of Fe(II)/2OGs to catalyze non-native asymmetric oxyfunctionalizations. We plan to evaluate the ability of our library of Fe(II)/2OGs to catalyze oxyfunctionalization of non-native substrates with various functional groups. Subsequently, we will optimize activity using directed evolution to arrive at a highly active and enantioselective enzyme capable of this chemistry.

There are an estimated 300 million people worldwide who are affected by asthma, a respiratory condition characterized by inflammation and swelling of the airways. One key process during an asthmatic event is vasodilation (i.e., the widening of blood vessels) in the lungs which causes increased blood flow and inflammation, making it difficult to breathe. Processes like vasodilation are mediated by the exchange of chemical signals between cells. Crucial to advancing our knowledge and understanding of inflammatory disease progression is gaining more insight into the underlying cellular communication during complex signaling events. Current methods used to study cell signaling include 2D cell culture studies which often lack spatial biological relevance or animal models, which are not adequate for understanding inflammation in humans. We developed a user-friendly method that can be used to measure cytokine response during inflammatory processes, including vasodilation. Our device enables the creation of a model blood vessel structure, offering a simple approach to test potential drug treatments. A 3D printed device is used to form hydrogel rings in a range of sizes. The rings are comprised of cell-laden collagen I to mimic blood vessels; vasodilators and constrictors can then be introduced to the solution housing the free-standing vessels. We then collect and analyze the solution to identify the chemical signals released by the cells during treatment with the drug. By identifying key chemical signals mediating disease pathways, we can begin to target those chemical signals and develop new therapeutic treatments.

A mesoporous silica (MPS) membrane is an ultrathin permeable material characterized by numerous and uniform embedded pores whose sizes are on the order of 2-3 nanometers. MPS membranes are widely used in a number of research and industrial applications such as biomedicine for the isolation and characterization of macromolecules including DNAs and proteins. Such membranes can be synthesized in a variety of ways including electrodeposition. In our research, we have been developing an electrochemistry-based method for the preparation of ultrathin MPS membranes ranging from 50 to 150 nm in thickness. These membranes are synthesized on an electrode by a novel pulse deposition process and can be transferred onto other solid supports. A highly sensitive single-molecule analysis platform is being developed based on the use of such MPS membranes. We anticipate that our MPS membranes will find extensive use in future applications ranging from single-molecule analysis to high efficiency purification of macromolecules and other small biomolecules of interest.

New technologies capable of controlling the position-and-spacing of nanostructures have advanced applications such as electronics, sensing, and catalysis. In contrast to conventional top-down approaches such as lithography, biological-macromolecule-templates offer an attractive way to direct the assembly of nanoparticles because their high-information content can be used to direct complex structures over multiple length-scales, just as they encode living structures. Here, we study the use of de novo designed protein-nanofibers to control the assembly of gold nanoparticle chains as a model system. We employ Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, which combines the energy contributions of van-der-Waals-attraction (vdW) and electrostatic-double-layer-repulsion (EDL) to understand the factors governing electrostatic assembly of gold nanoparticles along a protein-nanofiber anchored to a charged substrate, explain observed experimental results, and predict the assembly outcome under varying solution conditions. During the assembly process, as the distance between nanoparticle and protein-functionalized substrate decreases, we expect an increase in the magnitude of vdW and EDL forces. Varying nanoparticle size reveals particle-substrate EDL repulsion limits larger nanoparticles from reaching the protein despite an increase in vdW attraction, while tuning the pH varies EDL particle-protein-attraction and particle-substrate-repulsion, resulting in predictable particle density and binding specificity. We use Python 3.7.2 programming to calculate total system energies at different stages of the assembly process using equations based on the surface-element-integration method. By constructing a virtual representation of the protein-nanofiber as a chain of spheres on a flat plane and a spherical nanoparticle above the fiber at varying distances, we can use DLVO theory to map out the interaction energies for all solution conditions. With the ability to define the energy of a system, we will be able to design new biotemplates, indefinitely predict the solution conditions, and identify potential intervention points that would allow the self-assembly of plasmonic particles for new and/or difficult-to-achieve photonic applications.

Though renewed efforts in tuberculosis (TB) research have facilitated massive strides towards treating Mycobacterium tuberculosis (M.tb), TB remains a global health problem with an estimated 10 million infections and 1.4 million deaths in 2019. The ability of the pathogen to hide itself inside a granuloma, a mass of immune cells whose precise mechanism of regulation is unknown, prevents both the study of M.tb pathogenesis and the development of drugs or treatments. Current in vivo models have been established to study TB infection using animal models or tissues, limiting its biological relevance as it relates to human disease while current in vitro models lack components of the complex lung microenvironment during infection. We present the creation of a novel microscale infection model, which uses open and suspended microfluidic principles to enable spatial and temporal manipulation of cultures in suspended hydrogel plugs. By stacking together two devices, we demonstrate the ability of a suspended model granuloma consisting of M.bovis BCG (Mycobacterium bovis bacille Calmette-Guérin) and monocyte-derived macrophages to interact with a model vasculature layer consisting of endothelial cells. Analysis of soluble factors for proinflammatory cytokines and characterization of infection-dependent angiogenesis in the vasculature layer are used to verify communication between cultures. In the future, we envision this model expanding to contain multiple immune cell types and to incorporate additional aspects of the lung anatomy to approach a more accurate pathophysiological model as a tool for other researchers.

Climate Change is an undeniably major issue in the 21st century. While the effects of climate change and global warming cannot be reversed in one day, with cumulative efforts from individual communities, it is possible to slow down its effects. With the rapid development of major companies in cities, more foot and vehicle traffic are bound to follow, releasing higher levels of carbon dioxide in the air. Taking place in Bellevue, Washington, The research study explored the effect of the density of trees in varying areas, on its carbon dioxide and temperature levels. A series of data was taken at the well-forested Robinswood Park, and the minimally forested Bellevue Downtown Shopping center, over a two-week span, twice a day; once in the afternoon, and once in the evening. The goal was to see if a higher density of trees would lead to lower temperatures and lower carbon dioxide levels. My role in the research project was to format the data in order to put it into data correlation graphs between carbon dioxide levels, a varying density of trees, and temperature. After the study, an analysis of the data correlation graphs, revealed that a higher density of trees did lower carbon dioxide and temperature levels. In fact, even the mere presence of trees lowered these levels. Therefore, with the addition of low impact, low maintenance trees to urban areas, carbon dioxide, and temperature levels can be reduced. This study provides insight into a smaller scale of a very large issue, which can eventually be improved with collaborative efforts.

Standard diagnostic tests for common bacterial and viral infections are invasive and uncomfortable, especially for children. Difficulty performing these tests leads to diseases such as strep throat or pneumonia going undiagnosed in children with significant health risks. There is a need for more accessible diagnostics. Our research uses saliva analysis as a diagnostic medium for Streptococcus pyogenes. This presentation depicts a novel saliva collection platform that is designed and engineered to be child-friendly, effective at pathogen collection, and is suitable for home use. We produced prototype devices using 3D printing, computer numerical control (CNC milling), and molding. Human studies were performed to examine usability and inform further design. In addition to engineering the device, we tested coatings and capture of bacteria. Preliminary data finds success in capturing S. pyogenes. Collection of commensal oral bacteria capture is the next step of our human studies. Our expectation is to find success with our device in a human mouth. Once our device and system are established, we plan to expand to include additional diseases. We will be presenting our innovations in the device as well as our preliminary data on the efficient capture of bacteria. We hope this technology will provide diagnostics with a way to provide care to underserved populations as well as help us to better collect data regarding the bacteria and viruses present in previously inaccessible populations. 

 Alkenes are ubiquitous motifs in organic synthesis and are often found among pharmaceuticals and biologically active compounds. Moreover, a diastereoselective synthesis of the thermodynamically less stable Z-alkene isomer is a highly desirable reaction. However, classic methods of generating these targets remain limited. Because of this, our group is particularly interested in exploring transition metal-catalyzed hydrofunctionalization of a terminal alkyne to produce Z-alkenes. Hydrofunctionalization is an advantageous approach as it promotes the buildup of molecular complexity from simple terminal alkyne starting materials. We previously developed a method to access Z-alkene products through the direct reaction of a terminal alkyne, a primary alkylborane, and a silver triazole catalyst. Although we incorporated a wide variety of functional groups on both the alkyne and alkylborane substrates, the reaction was limited to primary alkylboranes. As a result, the goal of our current project was to overcome this limitation through rigorous screenings of reaction conditions that would incorporate secondary alkylboranes. To support screening efforts, my role is to synthesize various alkyne substrates to continue expanding substrate scope. We were able to accomplish our goal, and couple a secondary alkylborane with a terminal alkyne with moderate yields and selectivity. We are presently working to continue improving yield, selectivity, and expanding the functional group tolerance of this reaction which was otherwise inaccessible with our previous methodology. Through incorporating more sterically complex alkylboranes we provide access to a wide variety of structurally diverse Z-alkenes.

Water clusters play a significant role in a variety of processes such as those pertaining to the atmosphere and biological systems, and studies of water clusters have suggested that they could help us learn more about hydrogen bonding. We first must understand the energetics and trends of isolated water molecules in order to better comprehend the spectroscopic properties of water clusters. Afterwards, we can look at water clusters and observe how the energetics and patterns change due to the interactions with other water molecules. We are studying the coupling among vibrations in water molecules and how they are reflected in the spectra. The discrete variable representation (DVR), a method used to solve the Schrödinger equation, was implemented to generate the water spectrum as well as energies and wave functions. The DVR results show that the theoretical intensities are consistent with the experimental results. These results contribute to our goal of analyzing the spectra of more complicated water cluster systems. Diffusion Monte Carlo (DMC) is a different method that allows us to explore larger systems and is used in the analysis of the coupling in assemblies that contain multiple water molecules.

The theory of quantum mechanics has been well-developed over the last hundred years. However, its application is limited by the computational power of modern computers. With the rise of Big Data and Artificial Intelligence, a new door is opening to us to untangle the fascinating world of quantum mechanics. In our lab, we use Diffusion Monte Carlo (DMC), a statistical simulation to solve molecular vibration and rotation problems. It is remarkably accurate and versatile, making it suited for notoriously difficult systems, like protonated methane (CH₅⁺). Yet, it requires millions of potential energy evaluations before quality results can be acquired, which often takes unrealistic amounts of time. In this work, we use TensorFlow, a neural network training framework developed by Google, with full Graphics processing unit (GPU)-acceleration support, to considerably speed up the evaluation of the potential energies needed for the DMC calculations. We started by running a small-scale conventional DMC simulation to collect a set of molecular configurations and their corresponding potential energies, which are then fed into a 3-layer deep neural network on Tensorflow with carefully-selected parameters. Once finished training, the neural network can replace the conventional potential energy evaluation method used in DMC to greatly expedite the process. We tested this model on water(H₂O), protonated methane(CH₅⁺) and water dimer((H₂O)₂), and was able to achieve a 15-fold acceleration, with less than 0.01% error compared to conventional methods. Our future goal is to further optimize the neural network to make it even faster and more accurate, then apply it to larger systems which were unsolvable before due to their computationally intractable time.

Polymers are commonly seen in our daily lives. Proteins and plastics are both familiar classes of polymeric materials whose utility is heavily relied upon. There are different architectures of polymers, for instance, linear and cyclic, each of which has unique properties. For example, cyclic polymers have a lower viscosity, smaller hydrodynamic volume, and a unique topology as an endless circle. In this project, we are investigating and improving a privileged method to approach cyclic polymers, Ring Expansion-Metathesis Polymerization (REMP), which grows the polymer chain while cyclizing it, using a Ruthenium-based (Ru) system. The goal of the research is to solve the recent major challenge of synthesizing cyclic polymers in a controlled fashion, through systematically modifying the structure of Ru-based initiators. My goal in the research project is to synthesize a precursor ligand, the subsequent initiator, and the monomers(commonly strained alkenes, norbornene). Eventually using the monomers to conduct polymerization reactions and analyze their properties and characteristics with spectroscopic instruments. Preliminary results of this research suggest we can make cyclic polymers that are more evenly distributed in size and weight. Since polymers have played an important role in people’s everyday life, improving the methodology through having better control on making cyclic polymers can make a big contribution to applications in the aspects of biomedicine and energy for our society. For example, cyclic polymers could generate biotherapeutics for the field of medicine; they could also serve as well-behaved and new types of conducting materials for the field of semiconductor.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in the United States in January 2020, altering how most individuals interact in public spaces. Many familiar indoor spaces such as restaurants, planes, and classrooms suddenly posed a significant risk of exposure to SARS-CoV-2. Most transmission of SARS-CoV-2 is airborne through contagious aerosols exhaled with carbon dioxide (CO₂) by infected individuals in indoor and outdoor spaces. Indoor CO₂ levels are impacted by factors including: size of the space, air changes per hour, number of individuals present, the activities of the individuals, humidity, and temperature. Vigorous activities — such as speaking or exercising — increase CO₂ levels just as they increase aerosol production. Thus, CO₂ levels provided a good approximation of the transmission risk of SARS-CoV-2 in a specific location. We investigated how available ventilation impacts the transmission risk of SARS-CoV-2 in different indoor settings. An indoor air quality monitoring system that utilized CO₂ levels was developed to evaluate risk of transmission. CO₂ levels were measured using CO₂ sensors in Seattle, WA from March 2021 to April 2021. This data was collected in a variety of public indoor spaces including public transit, educational buildings, restaurants, gyms, and grocery stores. CO₂ levels were analyzed with a COVID-19 Aerosol Transmission Estimator based on the Wells-Riley equation. This information is critical to understanding the comparative risk of contracting SARS-CoV-2 in various indoor public spaces and highlighting where improvements can be made to mitigate such risk.

Cellulose is an abundant biopolymer that provides much of the structural support for plant cell walls. Its many desirable properties include high tensile strength, biocompatibility, thermal stability, and high water absorption. Cellulose has considerable potential as a component in polymeric composite materials, which combine polymer matrices with fillers to enhanced their mechanical properties for applications in drug delivery, food engineering, packaging, medical implants, and textiles. Even so, the difficulties of processing and manipulating cellulose at industrial scale have been cost prohibitive due to its high energy, chemical, and water usage. Here, we investigate the potential for simultaneous in situ production and incorporation of cellulose within hydrogels based on a photo-curable derivative of Pluronic® F-127, F127-bisurethane methacrylate (F127-BUM). We utilized a “symbiotic culture of bacteria and yeast” (SCOBY), obtained from a commercially available fermented tea beverage (Kombucha) starter kit, for the hydrogel formulation. We show that within cured F127-BUM hydrogel constructs, a SCOBY is viable and its biomass increases over time when maintained with a sucrose and black tea medium. These results will lead to further investigation into the composition of the SCOBY biomass, as well as physical and mechanical properties of the resulting composite material.