Environmentally Controlled Oscillator with Triplex Guided Displacement of DNA Duplexes
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
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Results for “genetic circuits”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Plant synthetic biology innovations for biofuels and bioproducts
Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. In conclusion, this review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.
Yang, Yongil↗
Episome-Based Gene Expression Modulation Platform in the Model Diatom Phaeodactylum tricornutum
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger
High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.
42 ENGINEERING↗
Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence
High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.
42 ENGINEERING↗
Mini‐synplastomes for plastid genetic engineering
Summary In the age of synthetic biology, plastid engineering requires a nimble platform to introduce novel synthetic circuits in plants. While effective for integrating relatively small constructs into the plastome, plastid engineering via homologous recombination of transgenes is over 30 years old. Here we show the design–build–test of a novel synthetic genome structure that does not disturb the native plastome: the ‘mini‐synplastome’. The mini‐synplastome was inspired by dinoflagellate plastome organization, which is comprised of numerous minicircles residing in the plastid instead of a single organellar genome molecule. The first mini‐synplastome in plants was developed in vitro to meet the following criteria: (i) episomal replication in plastids; (ii) facile cloning; (iii) predictable transgene expression in plastids; (iv) non‐integration of vector sequences into the endogenous plastome; and (v) autonomous persistence in the plant over generations in the absence of exogenous selection pressure. Mini‐synplastomes are anticipated to revolutionize chloroplast biotechnology, enable facile marker‐free plastid engineering, and provide an unparalleled platform for one‐step metabolic engineering in plants.
Occhialini, Alessandro↗
Thermodynamic analysis and optimization of supercritical carbon dioxide Brayton cycles for use with low-grade geothermal heat sources
The transcritical carbon dioxide Rankine cycle has emerged as an alternative for power generation in low-grade heat applications. However, the low heat rejection temperatures required for condensing the carbon dioxide is almost prohibitive for many locations. A simple solution is the adoption of a supercritical carbon dioxide Brayton cycle which does not need such low temperature heat sinks. This power cycle has been poorly studied previously in low-grade heat applications. Therefore, with the aim of determining the performance and feasibility of the supercritical carbon dioxide Brayton cycle from thermodynamic viewpoint, using a low-grade geothermal heat source, this paper presents a comparative thermodynamic analysis between four different supercritical carbon dioxide Brayton cycles. For this purpose, detailed models were developed and solved by coupling the thermodynamic model with the thermal-hydraulic model of a Printed Circuit Heat Exchanger. Subsequently, simulations of a base case, a parametric analysis, and an optimization using Genetic Algorithms, were carried out. Results show that there is a combination of operating conditions that maximizes the electrical output of the system. Besides, some values of the minimum pressure of the cycles can increase substantially the precooler cooling water pumping power. At the optimum design point, using a 20 kg/s geothermal brine stream at 150 °C as heat source with a minimum allowable reinjection temperature of 70 °C, the Intercooled Recuperated Brayton Cycle achieved the highest electric power output, energy and exergy efficiencies, obtaining values of 779.99 kW, 11.51%, and 52.49%, respectively. The other feasible alternatives were the Recuperated Brayton Cycle, the Simple Brayton Cycle and the Intercooled Brayton Cycle, ranked, in that order.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Partitioning Quantum Chemistry Simulations with Clifford Circuits
Current quantum computing hardware is restricted by the availability of only few, noisy qubits which limits the investigation of larger, more complex molecules in quantum chemistry calculations on quantum computers in the near term. Here, in this work, we investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver. To this end, we consider naive and physically motivated, classically efficient product ansatz for the parametrized wavefunction adapting the separable-pair ansatz form. We combine it with post-treatment to account for interactions between subsystems originating from this ansatz. The classical treatment is given by another quantum circuit that has support between the enforced subsystems and is folded into the Hamiltonian. To avoid an exponential increase in the number of Hamiltonian terms, the entangling operations are constructed from purely Clifford or near-Clifford circuits. While Clifford circuits can be simulated efficiently classically, they are not universal. In order to account for missing expressibility, near-Clifford circuits with only few, selected non-Clifford gates are employed. The exact circuit structure to achieve this objective is molecule-dependent and is constructed using simulated annealing and genetic algorithms. We demonstrate our approach on a set of molecules of interest and investigate the extent of our methodology’s reach.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications
Agriculture is under pressure to provide food for a growing population and the feedstock required to drive the bioeconomy. Methods to breed and genetically modify plants are inadequate to keep pace. When engineering crops, traits are painstakingly introduced into plants one-at-a-time, combine unpredictably, and are continuously expressed. Synthetic biology is changing these paradigms with new genome construction tools, computer aided design (CAD), and artificial intelligence (AI). “Smart plants” contain circuits that respond to environmental change, alter morphology, or respond to threats. Further, the plant and associated microbes (fungi, bacteria, archaea) are now being viewed by genetic engineers as a holistic system. Historically, plant health has been enhanced by many natural and laboratory-evolved soil microbes marketed to enhance growth, provide nutrients, or confer pest/stress resistance. Synthetic biology has expanded the number of species that can be engineered, increased the complexity of engineered functions, controlled environmental release, and assembled stable consortia. New CAD tools will manage genetic engineering projects spanning multiple plant genomes (nucleus, chloroplast, mitochondrion) and the thousands of genomes of associated bacteria/fungi. Here, this review covers advanced genetic engineering techniques to drive the next agricultural revolution, as well as push plant engineering into new realms for manufacturing, infrastructure, sensing, and remediation.
Clauer, Phillip [Massachusetts Inst. of Technology↗
Wireless Optogenetic Modulation of Cortical Neurons Enabled by Radioluminescent Nanoparticles
While offering high precision control of neural circuits, optogenetics is hampered by the necessity to implant fiber-optic waveguides in order to deliver photons to genetically engineered light-gated neurons in the brain. Unlike laser light, X-rays freely pass biological barriers. In this work, we show that radioluminescent Gd 2 (WO 4 ) 3 :Eu nanoparticles, which absorb an external X-rays energy and then down-converter it into optical photons with wavelengths of ~610 nm, can be used for the transcranial stimulation of cortical neurons expressing red-shifted, ~590-630 nm, channelrhodopsin ReaChR, thereby promoting optogenetic neural control to the practical implementation of minimally-invasive wireless deep brain stimulation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Biosensor and optogenetics for systems biology of yeast branched-chain alcohol production and tolerance
In this project we combined synthetic biology, systems biology, protein engineering and metabolic engineering to study, control, and improve the production of branched chain alcohols (BCAs), a class of advanced biofuels preferred by the DOE, in the yeast Saccharomyces cerevisiae. This involved the development and application of optogenetic systems as a new modality of dynamic control of native and engineered metabolic pathways, using light as inducible or repressible agent. The optogenetic systems include gene circuits for light control of gene expression, as well as light-assembled synthetic organelles and photo-switchable protein binders to control metabolic and protein function with light at the protein level. In addition, we developed the first genetically encoded biosensor for BCA production in yeast, which we used to design high throughput assays to identify highly productive strains, pathways, and enzymes. We also showed that this biosensor can be functionally co-expressed with optogenetic circuits in the same strain, raising the possibility of establishing, for the first time, computer-interfaced closed-loop controls of engineered metabolic pathways. The yeast gene deletion library was also utilized to conduct the first systems-level study on BCA toxicity in yeast, which uncovered key fundamental principles of yeast sensitivity and tolerance to these alcohols, allowing us to design highly tolerant strains with increased BCA production. This project, thus comprises the development of several new technologies, which we integrated to make new discoveries on the dynamics of BCA production and their mechanisms of cellular toxicity and tolerance, as well as to establish new paradigms to engineer and control metabolic pathways and microbial fermentations with light, for the production of BCAs and other products of interest to the DOE.
2-metyl-1-butanol↗
Smart Microbial Cells Couple Catalysis and Sensing to Provide High-Throughput Selection of an Organophosphate Hydrolase
Enzyme engineering for gain of function requires navigating a large combinatorial sequence space efficiently. Typically, many mutations are needed to get significant improvements, while a single “bad” mutation can inactivate the enzyme. To establish high-throughput screening and achieve enhanced resolution between two variants, genetic libraries of the organophosphate hydrolase enzyme paraoxonase 1 (PON 1 ) were rapidly screened via an engineered positive-feedback circuit: a p-nitrophenol (PNP)-specific transcription factor (TF) regulated expression of PON 1 , which catalyzed paraoxon breakdown and PNP production. Rare active mutant colonies, picked by simple visual fluorescence of a PON 1 –green fluorescent protein (GFP) fusion, were characterized. Furthermore, in a single screening round, high (library-scale) throughput enabled the discovery of enhanced paraoxon degradation activity in PON 1 , including structurally unexpected mutations.
59 BASIC BIOLOGICAL SCIENCES↗
Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis
Mycobacterium tuberculosis (MTB) displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen’s capacity to evade the immune system and exploit susceptible individuals. Uncovering the gene regulatory programs that underlie the phenotypic shifts in MTB during disease latency and reactivation has posed a challenge. We develop an experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding map and insights from network topology analysis, we identify regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.
59 BASIC BIOLOGICAL SCIENCES↗
Disrupting autorepression circuitry generates “open-loop lethality” to yield escape-resistant antiviral agents
Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. In this study, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as “feedback disruptors,” break homeostasis, and increase viral transcription factors to cytotoxic levels (termed “open-loop lethality”). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC 50 , synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.
59 BASIC BIOLOGICAL SCIENCES↗
Smart Microbial Cell Technology: A high-throughput platform to optimize biocatalysts
Los Alamos National Laboratory developed an ultra-high-throughput screening platform to engineer custom biocatalysts that enhance the rate of chemical reactions critical in pharmaceuticals, renewable energy, and environmental cleanup. Current methods to find biocatalysts are slow. This platform scans genetic variations to optimize a single enzyme or microbial cell to generate a product efficiently. It selects rare mutations needed for biocatalyst optimization orders of magnitude faster than current screening methods. A custom sensor reporter gene circuit causes cells to fluoresce when they are making the target product. When coupled to flow cytometry, a million biocatalyst variants can be screened in hours.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration
Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.
42 ENGINEERING↗
Systems and methods for direct estimation of battery parameters using only charge/discharge curves
Electrochemical models for the lithium-ion battery are useful in predicting and controlling its performance. The values of the parameters in these models are vital to their accuracy. However, not all parameters can be measured precisely, especially when destructive methods are prohibited. In some embodiments of the present disclosure, a parameter estimation approach is used to estimate the open circuit potential of the positive electrode (Up) using piecewise linear approximation together with all the other parameters of a single particle model. Up and 10 more parameters may be estimated from a single discharge curve without knowledge of the electrode chemistry using a technique such as a genetic algorithm. Different case studies were presented for estimating Up with different types of parameters of the battery model. The estimated parameters were then validated by comparing simulations at different discharge rates with experimental data.
Qi, Yanbo↗
Nonlinear thermodynamic computing out of equilibrium
We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.
Whitelam, Stephen [Lawrence Berkeley National Labo↗