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Engineering Conditional Guide RNAs for Cell-Selective Regulation of CRISPR/Cas9
Get PDFCRISPR/Cas9 is a versatile platform for implementing diverse modes of genetic perturbation such as gene silencing, induction, deletion, or replacement. This technology is popularly used in developmental biology to probe genetic circuitry via constitutive gene knockdown. Global gene silencing could introduce artifacts in the study of developmental regulatory pathways, and this motivates the development of cell-selective gene editing. Our lab has recently created conditional guide RNAs (cgRNA) that enable CRISPR/Cas9 systems to silence a desired gene Y conditioned on the detection of an RNA transcript X inside of a cell. cgRNA systems were discovered via insertion and deletion mutations that systematically explored the structure function of the guide RNA. Nucleic acid engineering software (NUPACK) was used to generate orthogonal libraries of cgRNA molecules that executed both ON → OFF logic (conditional inactivation by an RNA trigger) and OFF → ON logic (conditional activation by an RNA trigger). A dCas9-based RFP silencing assay in bacteria was developed and used to show these cgRNA sequences were functional and could detect short exogenous trigger sequences in an orthogonal and doseresponsive manner. Subsequent studies on cgRNA structure and function enabled us to engineer next-generation systems that have fewer constraints on the trigger sequence or structure. These next-generation cgRNAs were tested against short synthetic mRNA transcripts, truncated sub-sequences of endogenous mRNAs, and full-length endogenous mRNAs. Synthetic mRNA transcripts were used to study the effect of protein translation on trigger RNA binding. cgRNAs were capable of detecting synthetic sequences embedded in the 3′ UTR of fluorescent protein mRNAs. cgRNAs could also detect short synthetic mRNAs or truncated subsequences from endogenous mRNAs. However, the detection of native full-length endogenous mRNAs remained challenging because we cannot reliably predict the local structure of sub-sequences within a long RNA transcript. High-throughput cgRNAscreening may prove necessary for finding accessible binding sites onmRNA transcripts. Nevertheless, cgRNA functionalities could be useful in developmental biology by enabling precision perturbation of regulatory events, linking guide RNA activity to an RNA marker X correlated to a specific cell type or temporal expression pattern. This work opens the possibility for future applications such as cell-selective gene therapies.</p
Electro-Optic Excitations in van der Waals Materials for Active Nanophotonics
Get PDFvan der Waals materials are emerging due to their unique properties such as atomic thickness, diverse quasiparticle optical resonances, and no requirement for lattice matching. While there is a vast variety of materials, semiconductors hold a special place for opto-electronic and linear/non-linear optical studies. Black phosphorus (BP), a 2D quantum-well with direct bandgap and puckered crystal structure, is a compelling platform for this research direction. In this thesis, we investigate fundamental optical excitations in novel low-dimensional quantum materials to achieve strong light-matter interaction and integrate with nanophotonic motifs for low-footprint, reconfigurable optical technology, focusing primarily on black phosphorus and transition metal dichalcogenides.
The thesis begins with the 'thin film limit' of van der Waals materials, between 5 and 20 nm thickness range. Chapters 2 and 3 explore how few-layer black phosphorus hosts interband and intraband optical excitations that can be strongly modified with gate-controlled doping and electric field, displaying epsilon near zero and hyperbolic behavior in the mid and far-infrared. In atomic thickness, strongly bound excitonic quasiparticles dominate the optical response. In Chapter 4, we investigate electrically tunable excitons in tri-layer black phosphorus, demonstrating a reconfigurable birefringent material that, when coupled with a Fabry-Perot cavity, enables the realization of a versatile and broadband polarization modulator. In Chapter 5, we examine the ultimate limit of a monolayer, studying MoTe2 via photoluminescence measurements and first-principles GW+BSE calculations, highlighting the Rydberg series associated with the exciton and its gate-tunability to understand strong electron-exciton interactions. In Chapter 6, we show how such excitons in monolayer black phosphorus can be strongly quantum confined at natural edges of exfoliated flakes, leading to highly temporally coherent emission. This emission is gate-tunable and understood via transmission electron microscopy and first-principles GW+BSE calculations of phosphorene nanoribbons to be originating from atomic reconstructions of the edge coupled with strain and screening effects.
Overall, our work highlights the potential of van der Waals materials for various electro-optical excitations and their applications in active nanophotonics.
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The Discovery and Biological Mechanisms of a Widespread Phenazine's Oxidation
Get PDFDuring the 2017 Microbial Diversity course at the Marine Biological Laboratory in Woods Hole, MA, Scott Saunders and Yinon Bar-On started enrichment cultures in hopes of dis-covering biological oxidation of phenazine-1-carboxylic acid (PCA). I took these enrich-ment cultures and described their PCA oxidation activity. From one of the mixed cultures, I isolated a bacterial strain that recapitulated the behavior of the enrichment. I identified it as a strain of Citrobacter portucalensis via a whole-genome analysis and called the strain "MBL" in reference to the Marine Biological Laboratory. Using a combination of analytical chemistry, quantitative fluorescence measurements, and genetic engineering, I showed that C. portucalensis MBL couples PCA oxidation to each mode of anaerobic respiration it employs with nitrate, fumarate, dimethyl sulfoxide (DMSO), and trimethylamine-N-oxide (TMAO) as terminal electron acceptors (TEAs). I further found that most of the PCA oxidation activi-ty depends on electron flux through the quinone/quinol pool but can be driven by certain terminal reductase complexes when no quinones are available, particularly in the case of ni-trate reductases. Every bacterial strain I tested catalyzed PCA oxidation when provided the appropriate TEA. My described mechanism for bacterial PCA oxidation is generalizable and implies that this previously undocumented phenomenon should occur wherever PCA is produced in rhizosphere environments.</p
The Identification of Discrete Mixture Models
Get PDFIn this thesis we discuss a variety of results on learning and identifying discrete mixture models, i.e., distributions that are a convex combination of k from a known class C of distributions. We first consider the case where C is the class of binomial distributions, before generalizing to the case of product distributions. We provide a necessary condition for identifiability of mixture of products distributions as well as a generalization to structured mixtures over multiple latent variables
Causal Sampling, Compressing, and Channel Coding of Streaming Data
Get PDFWith the emergence of the Internet of Things, communication systems, such as those employed in distributed control and tracking scenarios, are becoming increasingly dynamic, interactive, and delay-sensitive. The data in such real-time systems arrive at the encoder progressively in a streaming fashion. An intriguing question is: what codes can transmit streaming data with both high reliability and low latency? Classical non-causal (block) encoding schemes can transmit data reliably but under the assumption that the encoder knows the entire data block before the transmission. While this is a realistic assumption in delay-tolerant systems, it is ill-suited to real-time systems due to the delay introduced by collecting data into a block. This thesis studies causal encoding: the encoder transmits information based on the causally received data while the data is still streaming in and immediately incorporates the newly received data into a continuing transmission on the fly.
This thesis investigates causal encoding of streaming data in three scenarios: causal sampling, causal lossy compressing, and causal joint source-channel coding (JSCC). In the causal sampling scenario, a sampler observes a continuous-time source process and causally decides when to transmit real-valued samples of it under a constraint on the average number of samples per second; an estimator uses the causally received samples to approximate the source process in real time. We propose a causal sampling policy that achieves the best tradeoff between the sampling frequency and the end-to-end real-time estimation distortion for a class of continuous Markov processes. In the causal lossy compressing scenario, the sampling frequency constraint in the causal sampling scenario is replaced by a rate constraint on the average number of bits per second. We propose a causal code that achieves the best causal distortion-rate tradeoff for the same class of processes. In the causal JSCC scenario, the noiseless channel and the continuous-time process in the previous scenarios are replaced by a discrete memoryless channel with feedback and a sequence of streaming symbols, respectively. We propose a causal joint sourcechannel code that achieves the maximum exponentially decaying rate of the error probability compatible with a given rate. Remarkably, the fundamental limits in the causal lossy compressing and the causal JSCC scenarios achieved by our causal codes are no worse than those achieved by the best non-causal codes. In addition to deriving the fundamental limits and presenting the causal codes that achieve the limits, we also show that our codes apply to control systems, are resilient to system deficiencies such as channel delay and noise, and have low complexities.</p
Risk-Aware Planning and Control in Extreme Environments
Get PDFSafety-critical control and planning for autonomous systems operating in unstructured environments is a challenging problem must be addressed as autonomous vehicles, surgical robots, and autonomous industrial robots become more pervasive. This thesis addresses some of the issues in safety critical autonomy by introducing new techniques for computationally tractable and efficient safety-critical control. The approach developed in this thesis arises from taking a deeper look at two questions: 1) How can we obtain better uncertainty quantification of the disturbances that affect autonomous systems either as a result of unmodeled changes in the environment or due to sensor imperfections? 2) Given richer uncertainty quantification techniques, how do incorporate the diverse uncertainty descriptions into the control and planning framework without sacrificing the tractability and efficiency of existing approaches?
I address the above two questions by developing risk-aware control and planning techniques for traversal of a mobile robot over static but extreme terrain and in the presence of dynamic obstacles. We first look at algorithms for risk-aware terrain assessment, and extensively test them on wheeled and legged robots that were deployed in subterranean tunnel, urban, and cave environments for search and rescue operations in the DARPA Subterranean Challenge. I then present a theory for risk-aware model predictive control in static environments and in the presence of dynamic obstacles. Coherent risk measures are applied to this planning and control framework in order to account for diverse uncertainty descriptions. Computationally tractable reformulations of the optimal control problem are realized through constraint tightening techniques.
I then investigate algorithms for uncertainty assessment and prediction of apriori unknown, dynamic obstacles using data-driven techniques. We use a technique from signal processing literature called Singular Spectrum Analysis for making linear predictions of dynamic obstacles. The obstacle motion predictions are equipped with error predictions to account for the uncertainty in the sensing heuristically using bootstrapping techniques. We use a statistical tool, Adaptive Conformal Inference, to further calibrate the heuristic error prediction online to obtain true uncertainty prediction while using nonstationary data to analyze the performance of the data-driven predictor. These techniques provide reactive, real-time, risk-aware obstacle avoidance in dynamic environments.</p
Remotely Evaluating the Seasonality of Gross Primary Production at High Latitudes
Get PDFA warming trend larger than the global average is changing high-latitude terrestrial ecosystems. The impact of climate change at high latitudes is especially notable on the seasonality of vegetation photosynthesis, such as the Arctic greening, lengthened growing season, and increased peak production in the growing season. As a critical component of the global carbon cycle and land carbon sink, continuously monitoring the seasonal trajectory of ecosystem-level photosynthesis, Gross Primary Production (GPP), is much needed to better understand the climate change impacts and the sensitivity of high-latitude plant communities under global climate change. GPP has been estimated from both ground and space. However, sparsely distributed ground-level measurements are not representative of heterogeneous land cover and complex terrain in high latitudes. Remote sensing techniques provide extensive spatial coverage for comparing GPP at the regional scale. In this thesis, I carefully examine the advances in remote sensing for monitoring GPP at high latitudes, including using hyperspectral reflectance and Solar-Induced chlorophyll Fluorescence (SIF). We show that reflectance near 531 nm can track the seasonality of Light Use Efficiency (LUE), complementing conventional normalized difference vegetation index which is only a proxy of Absorbed Photosynthetic Active Radiation (APAR). Tracking both LUE and APAR is critical for improving GPP estimation, especially in evergreen forests with photosynthetic phenology but sustained canopy color -- a typical land cover type at high latitudes. Satellite-measured SIF can also track both LUE and APAR. Here, it is shown that the empirical model predicting GPP using SIF is land cover dependent. The presence of snow and surface, heterogeneous land cover, and complex terrains in the high latitudes further complicate the interpretation of the SIF-GPP relationship. To improve the accuracy of interpreting SIF in complex terrain, a geometric model is developed to account for variations in APAR on tilted slopes. The results of this thesis enhance the use of both reflectance and SIF to help improve terrestrial biosphere models simulating GPP and cope with model-data uncertainties. The results are also a useful reference for future satellite missions designing instruments and correcting topographic impacts. Overall, this thesis contributes to better evaluating GPP and constraining climate projection uncertainties.</p
Optimal Receptivity and the Generalization of the One-Way Navier-Stokes (OWNS) Equations to Complex High-Speed Boundary Layers and Jets
Get PDFPrediction of the linear amplification of disturbances in hypersonic boundary layers is challenging due to the presence and interactions of discrete modes (e.g. Tollmien-Schlichting and Mack) and continuous modes (entropic, vortical, and acoustic). While direct numerical simulations (DNS) and global analysis can be used, the large grids required make the stability calculations expensive, particularly when a large parameter space is required. At the same time, parabolized stability equations are non-convergent and unreliable for problems involving multi-modal and non-modal interactions. We therefore apply the One-Way Navier-Stokes (OWNS) Equations to study transitional hypersonic boundary layers. OWNS is based on a rigorous, approximate parabolization of the equations of motion that removes disturbances with upstream group velocity using a higher-order recursive filter. We extend the original algorithm by considering non-orthogonal curvilinear coordinates and incorporate full compressibility with temperature-dependent fluid properties. The generalized OWNS methodology is validated by comparing to DNS data for flat plates and a sharp cone, and to linear stability theory results for local disturbances on the centerline of the Mach 6 HIFiRE-5 elliptic cone. OWNS provides DNS-quality results for the former flows at a small fraction of the computational expense. We further demonstrate the capability of OWNS to track fully 3D instabilities by applying the algorithm to a complex Mach 6 finned-cone geometry as well as a 3D Mach 1.5 turbulent jet.
It is often desirable, especially for design purposes, to compute worst-case disturbances, i.e. solving the inverse problem, otherwise known as resolvent or input-output analysis. While DNS and global analysis can be used to compute optimal forced responses, their large computational expense render these tools less practical for large design parameter spaces. We address this issue by modifying the original OWNS framework to find the optimal forcing and responses using Lagrangian multipliers via an iterative, adjoint-based, space-marching technique that appreciably reduces the computational burden compared to the global approach that uses singular value decomposition without sacrificing accuracy. The input-output OWNS model is validated against optimal forcings and responses of a Mach 4.5 flat-plate boundary layer from literature and a Mach 1.5 turbulent jet. We then apply these equations to study worst-case disturbances on the centerline of the Mach 6 HIFiRE-5 elliptic cone and on a highly cooled Mach 6 flat-plate boundary layer.
Although the worst-case forcings are theoretically informative, they are not physically realizable. In natural receptivity analysis, disturbances are forced by matching local solutions within the boundary layer to outer solutions consisting of free-stream vortical, entropic, and acoustic disturbances. We pose a scattering formalism to restrict the input forcing to a set of realizable disturbances associated with plane-wave solutions of the outer problem. The formulation is validated by comparing with DNS of a Mach 4.5 flat-plate boundary layer. We show that the method provides insight into transition mechanisms by identifying those linear combinations of plane-wave disturbances that maximize energy amplification over a range of frequencies. We also discuss how the framework can be extended to accommodate scattering from shocks and in shock layers for supersonic flow.</p
Applications of Dynamic Nucleic Acid Nanotechnology in Closed-Loop Genetic Circuits and Detection of Viral Pathogens
Get PDFNucleic acid nanotechnologies have provided a platform where biologically relevant molecules can be engineered to perform programmable functions. Relative to proteins, complex nucleic acid-based systems can be designed more readily due to the countable nature of base-pairing interactions and readily available physical models. These features of nucleic acids enable us to design novel interaction pathways and functions by providing well-behaved molecular mechanisms. Two examples of these mechanisms are the conditional guide RNA (cgRNA) and the hybridization chain reaction (HCR). A cgRNA is a conditional programmable regulator where an expressed RNA trigger can conditionally turn on or off transcriptional regulation. HCR is a molecular mechanism for in vitro and in situ amplification of signals to spatially identify proteins, RNA, or DNA in a sample. This thesis will first demonstrate the use of these nucleic acid molecular mechanisms in closed-loop genetic circuits and infectious disease testing using cgRNAs and HCR, respectively, then provide updated tools for the nucleic acid design community to exploit the programmable nature of nucleic acids.
We begin by demonstrating the use of conditional programmable cgRNAs in closed-loop genetic circuits. Synthetic genetic circuits allow scientists to engineer arbitrary molecular interactions in living organisms. Feedback circuits in particular are recurrently found in nature and enable useful functionalities. However, protein components of genetic circuits cannot be designed scalably, are often mined from preexisting genomes, and present difficulties in being biologically orthogonal to themselves or the host organism. We are motivated to address these limitations by using orthogonal nucleic acid circuits created de novo. One potential component of these circuits are conditional guide RNAs (cgRNAs). cgRNAs are switchable transcriptional regulators, and this allows gene expression to be modulated through the expression of a small RNA trigger. Here we assess cgRNAs as a component for feedback genetic circuits. As an initial demonstration of cgRNA synthetic circuits, we built and validated a simple threshold circuit and demonstrated its orthogonality and scalability by showing independent circuit functions of two switches in a single cell. We also created a larger toggle switch that is made from the same components as the previous switches. These experiments show the orthogonality and feedback capabilities of cgRNAs will position them as a composable component for scalable synthetic biology.
We then used the hybridization chain reaction mechanism to develop an adaptable and sensitive test for the detection of SARS-CoV-2. The lateral flow assay format enables rapid, instrument-free, at-home testing for SARS-CoV-2. Due to the absence of signal amplification, this simplicity comes at a cost in sensitivity. Here, we enhance sensitivity by developing an amplified lateral flow assay that incorporates isothermal, enzyme-free signal amplification based on the mechanism of hybridization chain reaction (HCR). The simplicity of the user experience after the test begins is maintained by using a disposable 3-channel lateral flow device to automatically deliver reagents to the test region in three successive stages without user interaction. Prior to starting the test, a 15-minute heat step is required. Detecting gamma-irradiated SARS-CoV-2 virions in an extraction buffer, the current amplified HCR lateral flow assay achieves a limit of detection of 200 copies/µL using nucleic acid probes to target the SARS-CoV-2 RNA genome. By comparison, five commercial unamplified lateral flow assays that use proprietary antibodies to target the viral nucleocapsid protein exhibit limits of detection of 500 copies/µL, 1000 copies/µL, 2000 copies/µL, 2000 copies/µL, and 20,000 copies/µL. By swapping out nucleic acid probes to target different pathogens, amplified HCR lateral flow assays offer a platform for adaptable and sensitive at-home testing for emergent diseases.
Components for the previous two projects are designed and analyzed with NUPACK. NUPACK is a growing software suite for the analysis and design of nucleic acid structures, devices, and systems serving the needs of researchers in the fields of nucleic acid nanotechnology, molecular programming, synthetic biology, and across the life sciences. NUPACK algorithms are unique in treating complex and test tube ensembles containing arbitrary numbers of interacting strand species, providing crucial tools for capturing concentration effects essential to analyzing and designing the intermolecular interactions that are a hallmark of these fields. The all-new NUPACK web app (nupack.org) has been re-architected for the cloud, leveraging a cluster that scales dynamically in response to user demand to enable rapid job submission and result inspection even at times of peak user demand. The web app exploits the all-new NUPACK 4 scientific code base as its backend, offering enhanced physical models (coaxial and dangle stacking sub-ensembles), dramatic speedups (20-120× for test tube analysis), and increased scalability for large complexes. </p
Mechanophore Strategies for the Development of Polymers with Mechanically Gated Responsive Behavior
Get PDFThe development of mechanically sensitive molecules has recently enabled mechanical force to induce specific changes in polymeric materials. This document details efforts in the development of such mechanophores in which mechanical activation is used to gate the desired functional responses.
Chapter 1 is an introduction to polymer mechanochemistry and delineates the state of the field as it relates to color changing mechanophores used for damage detection. Special emphasis is placed on a system developed by our lab in which mechanical force gates the photoresponsive behavior of a diarylethene molecular switch.
Chapter 2 details the design of a new mechanically gated photoswitch whose synthesis and activity are improved from the previous iteration. The modular design enables a late-stage synthetic intermediate to be differentiated into a small library of masked photoswitches that produce unique colors following mechanical activation and ultraviolet irradiation. Notably, these mechanophores show activity in solution phase experiments as well as in bulk polymeric materials.</p