The engineering of cells to be capable of detecting and responding to multiple input signals has major applications in the area of diagnostics, cancer therapy and biological computation. Current biological signal processing methods rely on single input single output computations. This proves ineffective when detecting and responding to multiple inputs such as determining a pathology based on combinations of different microscopic cues. The aim is to build non-linear computation in S.cerevisiae (baker yeast). A well-known example of nonlinear computation is neural networks, a complex framework of parallel computations talking together to come to a common output signal. The goal is to build several yeast strains that work together to perform these parallel computations. Each individual strain would process multiple inputs and release a singular output signal to another yeast strain. Combining many strains together would create a complex decision tree capable of higher-order computation. With this goal in mind, we decided to edit the native alpha-factor pathway within the yeast cells. Alpha-factor, a signaling molecule involved in yeast mating, is secreted by yeast and detected with surface protein STE2 (a protein in the GPCR family). We also inserted, a non-native version of STE2 from C.albicans yeast cells. This allowed our engineered strains to detect two different alpha-factor inputs (S. cerevisiae alpha-factor and C.albicans alpha-factor). When detected both alpha-factors trigger the pFUS1-yeGFP pathway causing the yeast cells to fluoresce. We investigated how different concentrations of STE2 and C.A.STE2 on the cell surface influenced the output fluorescents of the yeast strain. Mathematical models were used to interpret the data and predict signal response. These models can be used to design and predict more complex networks.

Hybrid dynamical systems (HDS) are time dependent systems that have both discrete and continuous behavior, such as a bouncing ball or jumping leg. The BioRobotics Lab studies HDS in order to better understand locomotion and develop controls for robotics. As controllers are developed, they need to be tested in the hybrid dynamical system (HDS) either through simulation or implementing in physical robotics. While simulated system environments are useful in early controller development, they are unable to capture all the intricacies of a physical system. Thus, testing controllers in a physical system can provide better insights for future development. We developed a hopping robot for the purpose of easily implementing controllers through software. The hopping robot is designed to be modular and easily configured for different dynamics. Simulations of the robot were also developed so that researchers can use both simulated and physical environments. This tool will help researchers develop new controllers and could bring new insights to studying hybrid dynamical systems.

Recent studies have shown just how important microbiomes are for individual health, population health, and environmental health. Unfortunately, these studies are often limited by the costs of meta-Genomics. Furthermore, meta-Genomic data itself is limiting by only providing information on population characteristics, but not the functional contributions of members within the population. Single cell transcriptomic sequencing aims to lessen the latter issue by providing information on the gene expression of each individual cell within a sample. Even so, current single cell sequencing technologies are costly and require specialized equipment. SPLiT-seq is a single cell transcriptomic technology developed by the Seelig lab at the University of Washington that uses split-pool ligation to create uniquely barcoded cDNA for each cell using every-day laboratory bench tools and techniques and costs only one cent per cell. Currently, SPLiT-seq is well-optimized for mammalian cells. However, using this method on bacteria requires its own set of optimized procedures given the morphological and biochemical differences between eukaryotes and prokaryotes. The aims of this project are to deal with these biological differences to increase the information obtained from messenger RNA and decrease the amount received from ribosomal RNA, as well as reduce the amount of cells that get the same cDNA barcodes. By optimizing this single-cell transcriptomic technique for bacteria, future studies involving microbial communities will be able to obtain more robust information on the individuals within those populations.

Since 1982, with the introduction of insulin as the first recombinant protein therapeutic, peptide and protein drugs have grown to encompass 10% of the pharmaceutical market and are the fastest expanding class of drugs. Advantages of using peptides over small-molecule drugs include high potency, selectivity, and capability to be engineered for a diverse range of targets, most commonly to disrupt or facilitate key protein-protein interactions (PPIs) in the human body for a therapeutic effect. To search for strong binders for a therapeutic target, combinatorial peptide libraries of up to billions of different sequences are synthesized and screened against promising targets. Due to the enormous library size, screening for high affinity binders often requires multiple rounds of enrichment in order to isolate the most potent molecules, a laborious and potentially bottlenecking step in developing protein drugs. Current approaches that have strategies for enrichment, such as phage display and yeast surface display, are limited to screening a library of binders against one target instead of multiple targets (library-on-library). This proposal describes the development of a peptide binder screen utilizing a simple workflow of repeated mating and sporulation of genetically engineered S. cerevisiae, or baker’s yeast. This technology improves the throughput of established screening methods through a library-on-library format that efficiently isolates high-affinity peptide binding interactions.

This project aims to influence the next generation of flying robots by first studying how moths use multi-sensory information to increase their agility. These different types of multi-sensory information include visual- and touch-based feedback, which are influenced by forces such as those from flapping wings and changes in body posture (which affect a moth’s inertial distribution). In addition, flying insects are known to control orientation via torques arising from at least three distinct affordances: by varying aerodynamic center of pressure, by changing body posture to alter center of mass location, and by swinging body segments or limbs to harness inertial torques. We hypothesize that these sources of control are integrated in parallel to increase robustness and agility, are weighted according to behavioral context, and are tuned to body morphological parameters. To investigate this flight behavior, we first built a test apparatus to apply rapid pitch movements to a tethered flying moth, Manduca sexta, while having a minimal effect on a moth’s inertia and natural flight patterns. This system allows us to measure the torque exerted by the moth’s flight forces and body movement. Closing the loop between measured torque and applied movement enables control of the forces experienced by the moth. Projected video is used to simulate a changing environment and create a haptic-type interface for the moths. The video can provide a visual experience to either match the mechanosensory experience or provide sensory conflict (i.e. a mixed reality environment). The next engineering challenge is to design and build a system to electronically control and monitor the motion of the moth. After this, cameras and sensors will be used to record data that will contribute to a more realistic understanding of how the principles of animal flight can be used in robotics.

Exposure to particulate matter (PM) is associated with adverse health effects including asthma, cardiopulmonary diseases, and lung cancer. These adverse health effects are responsible for the premature death of 7 million people worldwide each year. To reduce the consequences of air pollution, real-time individual exposure data is essential for health risk assessment and tailored intervention strategies. Portability and cost are significant barriers for consistent and personalized tracking. Unfortunately, the only accurate data regarding levels and types of air pollutants come from static monitoring stations that do not allow personalized analysis. Aero-Spec was developed to provide real-time analysis of air quality particles while also delivering a compact and low-cost approach. It is a wearable exposure monitor that combines real-time measurements of PM using an optical particle sizer (OPS). The OPS sensor allows for individual particle sizing and error reduction associated with the particles’ optical properties. Personal PM exposure data is provided to the user through a smartphone application such that intervention actions could be triggered given the detection of poor air quality. For personalized analysis, indoor localization is used over the global positioning system (GPS) for a more accurate and localized diagnosis. Aero-Spec utilizes this network technology to track the wearable sensors and report back localized air particle data. In conclusion, using a portable and low-cost approach to real-time air particle analysis, Aero-Spec delivers an effective and personalized solution for air quality monitoring. Recently, in a movement to monitor their employees’ exposure to particle data in real-time, Boeing has collaborated with the Sensors, Energy, and Automation Laboratory (SEAL) for further research.

Whispering Gallery Mode (WGM) sensors have unprecedented sensitivity in the optical detection of label-free biomolecules. These sensors can detect surface adsorption and have been used to detect single molecule adsorption and interaction processes. By observing resonance shifts during molecular interactions, WGM sensors can characterize a molecule’s surface adsorption. The goal of this project is to develop a robust WGM dip sensor array controlled by a three-axis stage in order to perform high-throughput characterization of peptide binding and adsorption within a 96-well plate format. The peak of spectral absorbance is the WGM resonance, and as this changes with surface adsorption we measured a spectral shift. Using this spectral shift in combination with the known concentration of our peptide species, we determined binding kinetics. The WGM sensor was used to characterize different peptide sequences to further understand the effects of peptide mutations on binding kinetics. A single microsphere resonator was used as proof of principle and will eventually be adapted to an array of eight WGM microsphere resonators to generate large amounts of data. This high throughput approach will provide the much needed large amount of quality data that is necessary for the development and adaptation of machine learning and applied statistical analysis algorithms toward the eventual development of artificial intelligence platforms in material science. The project is supported by NSF-DMREF through the Materials Genome Initiative.

Natural disasters pose a great threat to power systems. They can cause major damage to power lines and substation equipment, resulting in widespread outages. It can then take days to weeks and millions of dollars to restore power to customers. Power system resilience is concerned with reducing the impact of disasters on power systems. The purpose of this study is to evaluate the resilience of one power utility in southwest Washington, the Grays Harbor PUD (GHPUD), to earthquakes, earthquake liquefaction, and tsunamis. This study will consider the likelihood of these disasters, potential effects on the GHPUD’s electrical system and its customers, and possible mitigation techniques. I have collected seismic hazard curves, response spectra, and liquefaction susceptibility data at GHPUD substation sites from US Geological Survey maps and the M9 project. I have also collected data on tsunami inundation depth at substation sites from Washington Geological Survey maps. With this information I have determined which of their substations are most at risk from these disasters. This data will be used to determine possible impacts on substation equipment and to estimate the probability of equipment failure. When completed, the results of this study can allow the Grays Harbor PUD and other power utilities to be more prepared for future disasters and to be more informed of potential resilience improvements.

The plant hormone Jasmonic acid (JA) interacts with JAZ proteins, proteins that play a crucial role in the regulation of signal cascades triggered by jasmonates, and controls the activity of MYCs, a family of genes that code for transcription factors. In the absence of JA, JAZ proteins bind to downstream MYCs and limit their activity. JA is produced in plant cells in response to tissue damage, and is crucial to plant fitness. The genome of model plant Arabidopsis thaliana encodes 13 JAZ and 5 MYC isoforms, but as of now, it is unclear why this apparent redundancy exists. Such redundancy is a common feature of plant genomes. In the case of JA-signaling, it may be that interaction strengths differ between JAZ and MYC isoforms and that this is important for system behavior. In A. thaliana, there are 65 pairwise combinations alone, which provides many opportunities for engineering incredibly fine-tuned and controlled gene expression. Our past work has been to clone and create a library of JAZ and MYC proteins. Our goal now is to create yeast mini-circuits, which are yeast cells that contain plasmids that carry certain selected JAZ and MYC proteins. After constructing these yeast mini-circuits, we will study the JA-response dynamics of these circuits to better understand the role of each of these proteins and study differences in strengths of interaction between the different proteins. We suspect that the absence of certain JAZ and MYC proteins will lead to different phenotypes in defense mechanisms. Additionally, by investigating this relatively familiar pathway through the construction of yeast mini-circuits, we are attempting to develop an efficient technique which can be used to characterize other unexplored plant systems.

The nitrogen vacancy (NV) center is a point defect in diamond with quantum mechanical spin properties. These properties enable optical nano-scale magnetic field sensing at room temperature, making them highly bio-compatible. To conduct wide-field magnetic field imaging, we require a nano-scale thin surface layer of high NV center density in our diamond samples. We demonstrate optimized sensing capability in our diamonds through growth and processing methods, which create an ensemble of NV centers. With these sensitive samples we use quantum control protocols called double quantum and spin bath driving to reach sensitivities of 60nT/Hz^1/2 over a 0.1 cubic micron pixel. These protocols take advantage of inherent characteristics of the NV center and improve the imaging capability for biological systems.

Quantitative PCR (qPCR) is a commonly-used method to analyze gene expression. However, carrying out a qPCR experiment is often daunting to a beginner such as an undergraduate student, as it involves a lot of advance planning, precise pipetting, and is susceptible to contamination. Our goal is to automate laboratory and data analysis protocols so that (1) experiments can be carried out more quickly and more efficiently; (2) data are carefully tracked and recorded; and (3) results are reproducible. Automation involves repeated testing, writing and integrating protocols onto Aquarium, the laboratory operating system developed by our lab. Since Aquarium can keep track of every data point that it is asked to collect, we can define metrics of protocol success (like product quality, time-to-completion and ease of execution) and compare them throughout the automation process to make sure that the final protocols satisfy the three criteria above. For the 2018-2019 school year, we are automating protocols to cultivate Arabidopsis thaliana and study the gene expression of its Jasmonate (JA) signaling pathway. Since the JA pathway in Arabidopsis thaliana involves many genes and has been well-studied by plant biologists, we reason that it would be an appropriate case-study for our automation project. As we apply our qPCR workflow to study the expression of these genes in response to application of exogenous JA, we expect to see results that are consistent between different sets of technical replicates performed by different student technicians, and results that are consistent with those in existing literature.

Quantum systems are building blocks for promising new technologies such as fundamental secure communication, quantum computing, and quantum sensing. One quantum system, the diamond nitrogen-vacancy center, presents many of the best traits among scalable qubit candidates. Preferential lattice orientation of nitrogen-vacancy centers, while technically difficult, would improve signal to noise ratios in quantum sensing and efficiency of quantum repeaters. I report defect orientation changes in diamond through week-long diamond anneals at high temperatures. I discuss my techniques to measure, characterize and spatially track thousands of nitrogen-vacancy defects through confocal microscopy to observe orientation changes as a function of annealing temperature. I show conclusive data supporting a critical temperature for achieving nitrogen-vacancy orientation changes and discuss follow on applications.

Crops are constantly exposed to pathogens and pests, but being sessile, they cannot physically move away from danger. In order to defend themselves, plants have mechanisms to recognize and respond to threats. One way that plants do this is through the Jasmonic acid pathway, which is specifically activated in response to the wounding of plant tissue. Surprisingly, there are many functionally similar Jasmonate-ZIM domain (JAZ) proteins involved in this response, and the reason for this redundancy is unknown. Studying this pathway in depth presents a challenge when using low-throughput methods. In order to better understand this complex system, we built a workflow, which will be integrated into an automated system using the Aquarium Laboratory Operating System, designed by the Klavins Lab. This will help us track data from high throughput experiments. By developing automated methods to track maintenance of cell cultures, production of protoplasts, and delivery of dCAS9 transcription regulators, we hope to produce sufficient data to better understand the JA-signaling pathway. We anticipate that these experiments will illustrate a relationship between the manipulated activity of JAZ gene regulators and subsequent immune response activity, and have potential implications in the agricultural and food industries, where pests and stress conditions are a significant concern.

Quantum sensing applications require diamonds with high concentrations of high-fidelity NV centers. Here we find we can significantly increase the NV center density in high-purity diamond by over a factor of ten by simple annealing. We perform fifteen anneals starting with 800 °C up to 1100 °C. Throughout each anneal, we track thousands of individual NV centers in a large experiment volume (350x350x500 um³) using a custom confocal microscope. Peak NV density was observed to occur at 980 °C. Spectroscopy measurements also show near ideal NV quantum characteristics for the newly formed centers. With this process, we can optimize NV formation for magnetic field sensing and quantum entanglement applications.

The study of the behavior of multiple agents, specifically human and machine, in a dynamic environment is challenging due to the unpredictable individual behaviors. Humans will naturally formulate beliefs about the machine’s behavior, which would directly affect their future decisions. Our research aims to develop a framework for the study of human-machine dynamic interactions. With the imperfect information humans and machines have about each other and their environment, a game-theoretic approach was done to study the natural model of their interactions. We derive theoretical models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines). We also design experiments to validate our theory. A haptic testbench, in the shape of a robotic arm, is used as a dynamic simulation platform for studying the trajectories of the human/machine interaction, allowing us to study both theoretically and experimentally. The robotic arm has a position control system that supports a wide variety of human/machine experiments. The user is provided with visual and haptic feedback, which allows for experiments to be designed to study the sensorimotor learning processes. The robotic arm is built using direct-drive brushless motors, force sensors, an open-source ODrive motor controller, and an arm lever. The motor firmware is designed in C/C++, and integrated with a user-interface in Python. With the wide variety of potential applications, we hope our research will give insights into the different natures of human motion and be a fundamental platform for technological breakthroughs in the medical field.

Cerebral palsy is a congenital disorder which impacts movement, muscle tone, and cognitive ability. This disorder affects 2-3 people per 1,000 births annually. My lab is currently developing a technology to assess motor learning. We’re working on quantifying motor planning deficits in cerebral palsy to aid in targeted therapy. However, the small test population combined with the nature of cerebral palsy means it can be difficult to bring these subjects into the lab to verify our technology. I developed a “simulation” of the controller we use to interface with our technology that can be downloaded onto android devices and can connect to a subject’s bluetooth enabled computer wirelessly. This allows subjects to test our technology remotely in a setting which best suits their needs. Upon testing this virtual controller against the currently used controller, the virtual controller offered a lower mean-squared error to the normal controller, and was proved a viable option for remote testing. This development allows us to expand our testing pool to those who may not be able to physically come to the laboratory for testing, and can be expanded to future developments which require unique controllers.

Roborun is a simulation game that utilizes crowdsourcing to identify legged movement patterns for land-based robotic movement. By using a game controller, keyboard, or by providing a set of instructions to execute in order (e.g. “rotate front leg X degrees”, “move back leg forward Y meters”, etc.), players can control joint torques and leg movements in order to navigate a virtual two-legged robot through several 2D obstacle courses that contain varied terrain and movable boxes. The game can be played through a web browser on either a computer or smartphone, making the game accessible to players across multiple platforms. In future development, we intend to implement a scoring system based on efficiency and speed of course solutions and will replicate the best scoring solutions from players on a commercially-available robot in our testing laboratory. It is our hope that their solutions will help develop reliable robotic movement algorithms and shed some insight into the dynamics of land-based movement.