Plasmid Copy Number Engineering Accelerates Fungal Polyketide Discovery upon Unnatural Polyketide Biosynthesis
Not provided.
Biochemistry & Molecular Biology↗
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Results for “Engineering - Particle accelerators”
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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Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets [Abstract]
The Agile Biofoundry (ABF) is a multi-national lab consortium funded by the DOE Bioenergy Technologies Office that has developed a biofoundry enabling the rapid deployment of bioproducts into the market. The ABF is a flexible platform that can adjust to the needs of numerous government, academic and industrial partners, thus enabling them to rapidly develop and optimize the production of a wide range of bioproducts. To demonstrate this capability, three ABF labs (NTESS, LBNL, and PNNL) have collaborated with the biomanufacturer Lygos, Inc. to use the ABF to demonstrate a high-throughput Design-Build-Test-Learn (DBTL) engineering cycle incorporating multi-omics analysis and machine learning with best in industry cycle times.
60 APPLIED LIFE SCIENCES↗
Accelerating engineered microbe optimization through machine learning and multi-omics datasets
This project demonstrated the use of a combination of multi-omics data with deep learning and a high-throughput Design- Build-Test-Learn (DBTL) cycle to improve the production of malonic acid, a versatile product with a large market. The project leveraged the unique capabilities of both Lygos and the Agile BioFoundry (ABF): Lygos provided its expertise efficiently designing, building, and cultivating P. kudriavzevii strains; LBNL, PNNL, and NTESS provided multi-omics analysis in the Test phase, LBNL provided machine learning techniques in the Learn phase to analyze the -omics datasets and make recommendations so as to increase malonic acid production in the next DBTL cycle. This project is the first to use large amounts of multi-omics time-series data to feed deep learning models, creating around 80,000 data points in a single DBTL cycle. This project has 1) demonstrated the utility of combining deep learning and multi-omics data sets by improving the production of malonic acid two fold, 2) created a large time-series datasets to be released publicly for external development of new machine learning algorithms, and 3) shown that supply chain problems, strain building bottlenecks, and adaptation times for new ML approaches are key obstacles for fast DBTL cycle times.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerating engineered microbe optimization through machine learning and multi-omics datasets
Summary presentation for 2021 peer review session
60 APPLIED LIFE SCIENCES↗
Accelerating engineered microbe optimization through machine learning and multi-omics datasets
Summary presentation for 2023 BETO peer review session.
Lennen, Rebecca↗
Designing an Energy-Efficient Fully-Asynchronous Deep Learning Accelerator Engine
Explore the source record for details and available documents.
Computer science↗
Rapid Flux Phenotyping to Accelerate Metabolic Engineering of Cyanobacteria
This project established a rapid flux phenotyping platform to accelerate metabolic engineering of cyanobacterial hosts. As proof of principle, we applied this platform to optimize cyanobacterial production of isobutyraldehyde and ethylene in Synechococcus PCC 7942, as well as lysine and fatty acids in the fast-growing strain S ynechococcus PCC 7002. By matching the throughput of strain characterization to the rate of strain generation, this research provided the foundation for a rational “design-build-test-learn” cycle for metabolic engineering of cyanobacteria and other bioenergy-relevant microbes.
60 APPLIED LIFE SCIENCES↗
Computationally efficient evaluation of optimum homogeneous charge compression ignition operating range with accelerated multizone engine cycle simulation
The very intensive calculations necessary to define a performance map requiring evaluation of over a hundred individual operating points can be efficiently conducted with accelerated multizone for engine cycle simulation, leading to a definition of regions of acceptable and optimum homogeneous charge compression ignition operation. Accelerated multizone for engine cycle simulation has the virtue of enabling accurate evaluation of many operating conditions based on thermal stratification data from a single fluid mechanics run at motored conditions. This is possible because thermal stratification is more sensitive to engine geometry than to operating conditions. In this article, accuracy of accelerated multizone for engine cycle simulation is demonstrated by comparison with experimental data for iso-octane homogeneous charge compression ignition operation over a broad range of lean equivalence ratios (0.14–0.28). The validated accelerated multizone for engine cycle simulation model is then applied to generating a performance map for an engine controlled by appropriately adjusting equivalence ratio and internal exhaust gas recirculation. Regions of acceptable and optimum combustion are identified. It is finally demonstrated that while indicated mean effective pressure remains low for optimum homogeneous charge compression ignition operation (1–4 bar), this is sufficient for a large fraction of typical driving in light-duty vehicles. Much driving including idle can therefore be done in homogeneous charge compression ignition mode at high efficiency and low (essentially zero) NOx and particulate matter emissions.
30 DIRECT ENERGY CONVERSION↗
GDCI engine with accelerated oil warm-up
A piston oil squirter is selectively opened when an oil temperature is below a threshold oil temperature to transfer heat from the combustion chamber and heat the oil more rapidly when the engine is cold.
Roth, Gregory Thomas↗
Impact of Boron-Containing Lubricant Additive on Three-Way Catalyst Reactivity and Physicochemical Properties
Boron-containing compounds are one of the lubricant additive options due to their suitable properties for additives and have been used as commercial lubricant additives. In the present study, the impact of a boron-containing lubricant oil additive, AR9100 (BR), on Pd/Rh-based three-way catalyst (TWC) performance is investigated, and the results are compared with the baseline no-additive (NA) case and the industry standard zinc dialkyl-dithiophosphate (ZDDP) results. Accelerated engine aging is performed using a genset to expose the catalysts to lubricant additives at high temperatures. All aged TWC samples are investigated for reactivity in a bench-flow reactor and characterized using a variety of analytical techniques. Compared with the no-additive case, the temperatures of 90% conversion (T 90 ) of NO, CO, C 3 H 6 , and C 3 H 8 for the ZDDP-aged TWC sample increased by 34, 30, 37, and 48 °C. However, the T 90 of all gas species for the BR-aged TWC sample are similar to the NA-aged TWC sample. Additionally, a significant decrease in water–gas shift reactivity and oxygen storage capacity is observed in the ZDDP-aged sample, but not in the BR-aged sample. Inductively coupled plasma-optical emission spectrometry (ICP-OES) analysis and electron probe microanalysis (EPMA) maps of accelerated engine aging samples show the presence of phosphorus and boron in ZDDP- and BR-aged TWC samples, respectively. However, no boron-related peaks are observed in the X-ray diffraction (XRD) pattern of the BR-aged TWC sample, which may exist in the form of an amorphous phase.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerated enzyme engineering by machine-learning guided cell-free expression
Not Available
Landwehr, Grant M.↗
Microstructural Engineering and Accelerated Test Method Development to Achieve Low Cost, High Performance Solutions for Hydrogen Storage and Delivery
This project made advancements in developing lower cost steel alloys with novel microstructural design for use in hydrogen refueling infrastructure such as storage, compressors, and dispensing components, and utilizing accelerated test methods to efficiently evaluate variations in alloy and microstructure design. The project specifically sought to design alloys with lower nickel contents to reduce alloy cost, which was accomplished through substituting manganese for nickel along with other alloy additions to control deformation characteristics known to be important for hydrogen embrittlement resistance. Through Mn substitutions for nickel, austenitic and duplex austenite-ferrite steels were successfully developed with lower cost than currently available commercial stainless steel products that are employed for hydrogen refueling infrastructure. The steels were processed to achieve comparable strength and toughness in hydrogen environments as the commercially available steels containing high Ni contents. To evaluate mechanical performance in hydrogen, a testing methodology was employed to compare ubiquitous laboratory testing using electrochemical hydrogen charging in a liquid electrolyte to less accessible high pressure gaseous testing. While the application of these steels is in high pressure gaseous environments, the electrochemical hydrogen charging tests produced comparable results. Additionally, the Los Alamos Neutron Scatting Center enabled characterization of deformation mechanisms of the steel alloys in the presence of hydrogen, which has been associated with steel alloy characteristics associated with hydrogen embrittlement. Finally, a fracture mechanics based test bed model was developed to predict the influence of hydrogen gas pressure and fatigue conditions on fatigue lifetimes of pressure vessel steels. Together, these developments can be employed to enable lower cost hydrogen fueling infrastructure and more reliable prediction of steel alloy components in hydrogen service conditions. In particular, the newly alloys have the potential to replace stainless steels, having demonstrated comparable performance at substantially reduced cost.
08 HYDROGEN↗
Digital Engineering for Accelerated Component Design
Explore the source record for details and available documents.
Hall, Taylor Howard [Sandia National Laboratories ↗
Solutions in microbiome engineering: prioritizing barriers to organism establishment
Abstract Microbiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.
59 BASIC BIOLOGICAL SCIENCES↗
Phylogenomic discovery and engineering of nitrogen fixation into the bioenergy woody crop poplar
Biological nitrogen fixation (BNF) is a key process enabling plants in specific lineages to convert atmospheric dinitrogen (N₂) into bioavailable ammonia through symbioses with diazotrophic microbes. Expanding this capability beyond native nitrogen-fixing clades into non-nodulating crops would reduce synthetic fertilizer use, lowering energy inputs and environmental impacts in agriculture. Supported by DOE Funding Award DE-SC0018247, the NitFix project advanced foundational knowledge required to engineer root-nodule symbioses in new host species. The team generated the most comprehensive phylogenomic analysis to date of all known nodulating lineages, resolving the evolutionary history of nitrogen-fixing symbiosis and identifying core gene suites retained across nodulating taxa. Through multimodal genomics, transcriptomics, and functional analyses in Medicago truncatula and related species, the project mapped regulatory networks underlying nodule organogenesis, bacterial infection, and nitrogen-fixation efficiency. Key discoveries include the identification of conserved signaling modules for rhizobial recognition, transcription factors controlling nodule differentiation, and metabolic pathways integrating fixed nitrogen into plant growth. The project also developed enabling tools—including optimized transformation pipelines, gene-editing workflows, and imaging-based phenotyping—to accelerate engineering efforts in emerging models. Together, these results refine the mechanistic framework of symbiotic nitrogen fixation and highlight transferable components essential for rewiring these traits into non-nodulating crops.
59 BASIC BIOLOGICAL SCIENCES↗
Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products
Lignin is an abundant and sustainable source of aromatic compounds that can be converted to value-added products. However, lignin is underutilized, since depolymerization produces a complex mixture of aromatic compounds that is difficult to convert to a single product. Microbial conversion of mixed aromatic substrates provides a potential solution to this conversion challenge. Recent advances have expanded the range of lignin-derived aromatic substrates that can be assimilated and demonstrated efficient conversion via central metabolism to new potential products. Here, the development of additional non-model microbial hosts and genetic tools for these hosts have accelerated engineering efforts. However, yields with real depolymerized lignin are still low, and additional work will be required to achieve viable conversion processes.
59 BASIC BIOLOGICAL SCIENCES↗
Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning
Although Transformer-based language representations achieve state-of-the-art accuracy on various natural language processing (NLP) tasks, the large model size has been challenging the resource constrained computing platforms. Weight pruning, as a popular and effective technique in reducing the number of weight parameters and accelerating the Transformer, has been investigated on GPUs. However, the Transformer acceleration using weight pruning on field-programmable gate array (FPGAs) remains unexplored. This paper investigates the column balanced block-wise pruning on Transformer and designs an FPGA acceleration engine to customize the balanced blockwise matrix multiplication. We implement the Transformer model with proper hardware scheduling, and the experiments show that the Transformer inference on FPGA achieves 10.35 ms latency with the batch size of 32, which is 10.96 × speed up comparing to CPU platform and 2.08 × speed up comparing to GPU platform.
Peng, Hongwu↗