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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Corn Storage Protein - A Molecular Genetic Model

Corn is the highest yielding crop on earth and probably the most valuable agricultural product of the United States. Because it converts sun energy through photosynthesis into starch and proteins, we addressed energy savings by focusing on protein quality. People and animals require essential amino acids derived from the digestion of proteins. If proteins are relatively low in certain essential amino acids, the crop becomes nutritionally defective and has to be supplemented. Such deficiency affects meat and fish production and countries where corn is a staple. Because corn seed proteins have relatively low levels of lysine and methionine, a diet has to be supplemented with soybeans for the missing lysine and with chemically synthesized methionine. We therefore have studied genes expressed during maize seed development and their chromosomal organization. A critical technical requirement for the understanding of the molecular structure of genes and their positional information was DNA sequencing. Because of the length of sequences, DNA sequencing methods themselves were insufficient for this type of analysis. We therefore developed the so-called “DNA shotgun sequencing” strategy, where overlapping DNA fragments were sequenced in parallel and used to reconstruct large DNA molecules via overlaps. Our publications became the most frequently cited ones during the decade of 1981-1990 and former Associate Director of Science for the Office of Basic Energy Sciences Patricia M. Dehmer presented our work as one of the great successes of this program. A major component of the sequencing strategy was the development of bacterial strains and vectors, which were also used to develop the first biotechnology crops. These crops possessed new traits thanks to the expression of foreign genes in plants. To enable such expression, chimeric genes had to be constructed using our materials and methods by the industry. Because we made our materials and methods freely available to academia and industry, progress in plant research and new crop development could accelerate and benefit the public.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Yet despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

An FDA-approved drug structurally and phenotypically corrects the K210del mutation in genetic cardiomyopathy models

Dilated cardiomyopathy (DCM) due to genetic disorders results in decreased myocardial contractility, leading to high morbidity and mortality rates. There are several therapeutic challenges in treating DCM, including poor understanding of the underlying mechanism of impaired myocardial contractility and the difficulty of developing targeted therapies to reverse mutation-specific pathologies. In this report, we focused on K210del, a DCM-causing mutation, due to 3-nucleotide deletion of sarcomeric troponin T (TnnT), resulting in loss of Lysine210. We resolved the crystal structure of the troponin complex carrying the K210del mutation. K210del induced an allosteric shift in the troponin complex resulting in distortion of activation Ca 2+ -binding domain of troponin C (TnnC) at S69, resulting in calcium discoordination. Next, we adopted a structure-based drug repurposing approach to identify bisphosphonate risedronate as a potential structural corrector for the mutant troponin complex. Cocrystallization of risedronate with the mutant troponin complex restored the normal configuration of S69 and calcium coordination. Risedronate normalized force generation in K210del patient-induced pluripotent stem cell–derived (iPSC-derived) cardiomyocytes and improved calcium sensitivity in skinned papillary muscles isolated from K210del mice. Systemic administration of risedronate to K210del mice normalized left ventricular ejection fraction. Collectively, these results identify the structural basis for decreased calcium sensitivity in K210del and highlight structural and phenotypic correction as a potential therapeutic strategy in genetic cardiomyopathies.

Research & Experimental Medicine↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. In this study, we developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits

This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition

Modeling the molecular mechanisms that govern genetic variation can be useful in understanding the dynamics that drive genetic state transition in quasispecies viruses. For example, there is considerable interest in understanding how the relatively benign vaccine strains of poliovirus eventually revert to forms that confer neurovirulence and cause disease (ie, vaccine-derived poliovirus). This report describes a stochastic simulation model, S2M, which can be used to generate hypothetical outcomes based on known mechanisms of genetic diversity. S2M begins with predefined genotypes based on the Sabin-1 and Mahoney wild-type sequences, constructs a set of independent cell-based populations, and performs in-cell replication and cell-to-cell infection cycles while quantifying genetic changes that track the transition from Sabin-1 toward Mahoney. Realism is incorporated into the model by assigning defaults for variables that constrain mechanisms of genetic variability based roughly on metrics reported in the literature, yet these values can be modified at the command line in order to generate hypothetical outcomes driven by these parameters. To demonstrate the utility of S2M, simulations were performed to examine the effects of the rates of replication error and recombination and the presence or absence of defective interfering particles, upon reaching the end states of Mahoney resemblance (semblance of a vaccine-derived state), neurovirulence, genome fitness, and cloud diversity. Simulations provide insight into how modeled biological features may drive hypothetical outcomes, independently or in combination, in ways that are not always intuitively obvious.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Improving performance of two-group interfacial area transport equation models using genetic algorithms and high resolution experimental data

In the two-fluid transport model, the coupling of mass, momentum and energy transfer between phases is highly dependent on interfacial area transfer terms. Several research efforts in the past have been focused on the development of an interfacial area transport equation model (IATE), in order to eliminate the drawbacks of static flow regime maps currently used in best-estimate thermal-hydraulic system codes. The IATE attempts to model the dynamics that are involved in two phase flows by accounting for the different interaction mechanisms affecting bubble transport in the flow. In general, current IATE models perform poorly in high void fraction flow regimes. As both models depend on closure relations that require empirically determined coefficients, a genetic algorithm is implemented to study their optimization. The preliminary study indicates that improvements are achievable by application of the genetic algorithm. Optimization of coefficients for an entire set of high-resolution wire mesh sensor (WMS) data provided a single set of optimal coefficients. The application of WMS-optimized coefficients to external tests improved IATE performance for similar hydraulic diameters and provided a neutral change in performance for significantly smaller/larger hydraulic diameters. This is a crucial outcome as an optimized set of coefficients that provide significant improvement for a subset of experimental data - is shown to benefit performance in external data for which the coefficients are not specifically optimized. (authors)

42 ENGINEERING↗
DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Development of male-sterile lines of Setaria viridis to accelerate C 4 model plant genetics

Setaria viridis is a diploid C 4 grass in the Poaceae family, notable for its rapid life cycle of 6–8 weeks from sowing to seed—much shorter than the 4–5 months required by crops such as Zea mays and Sorghum bicolor . This fast growth makes S. viridis a valuable model for C 4 crop research. Genetic crosses are essential for studying gene function, but manual crossing is labor-intensive and time-consuming. Here, to address this, we developed a male-sterile line by targeting the S. viridis ortholog of Setaria italica NO POLLEN 1 ( SiNP1 ), which encodes a glucose–methanol–choline oxidoreductase required for pollen exine formation. Using Cas9 and TREX2 -mediated genome editing, we generated SiNP1 knockouts in both the S. viridis ME034V and A10.1 backgrounds that were fully male-sterile. Backcrossing T 0 male-sterile plants to ME034V wild-type followed by selfing yielded a stable BC 1 F 2 line homozygous for a 59 bp deletion in the S. viridis NO POLLEN 1 gene, easily genotyped by PCR and maintained by heterozygous siblings. Using this line, we developed a simple and efficient crossing protocol that eliminates the need for emasculation. This method enables a single person to perform up to 100 crosses per day—compared to 15 using traditional methods—and yields 20–32 F 1 hybrid seeds per panicle with 100% genetic purity. We also quantified pollen flow and outcrossing frequencies under greenhouse conditions to develop optimal bagging strategies and prevent unintended pollination. This resource accelerates genetic research in S. viridis , enhancing its utility as a premier C 4 model for mapping and functional genomics.

C4 research↗
DOE OSTI2018

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k cat s) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved k cat s. Diminishing returns epistasis prevents enzymes from developing higher k cat s in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows k cat evolution to be convergent. Predicted k cat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern k cat s and the whole of metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm

Resource management is a hotspot issue in distributed systems like cloud computing (CC). It means how to prepare the computational resources, i.e., servers and virtual machines (VMS), to execute the tasks. This paper offers a new approach based on Group Technology (GT)—known as a powerful philosophy for the resource management in cellular manufacturing systems—to deal with the resource management problem in CC. We develop a mathematical model to optimally consolidate the VMs, servers and tasks simultaneously to control several important factors such as task migrations and server load variation, as well as the number of VMs. To test the validity of our proposed model, several small problems are generated randomly and solved by LINGO 9 software. Furthermore, to cope with larger problems, which cannot be solved optimally, a genetic algorithm is proposed. We, finally, compare our methods with the most well-known algorithms in this context, round robin (RR) and first-come, first-served (FCFS) algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A new perspective on dark energy modeling via genetic algorithms

We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ω{sub m}(a) in the growth rate data, fσ{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function Ω{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ∼ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A dynamic protein interactome drives energy conservation and electron flux in Thermococcus kodakarensis

ABSTRACT Life is supported by energy gains fueled by catabolism of a wide range of substrates, each reliant on the selective partitioning of electrons through redox ( red uction and ox idation) reactions. Electron flux through tunable and regulated protein interactions provides dynamic routes for energy conservation, but how electron flux is regulated in vivo , particularly for archaeal metabolisms that support rapid growth at the thermodynamic limits of life, is poorly understood. Identification of bona fide in vivo protein assemblies and how such assemblies dictate the totality of electron flux is critical to our understanding of the regulation imposed on metabolism, energy production, and energy conservation. Here, 25 key proteins in central metabolic redox pathways in the model, genetically accessible, hyperthermophilic archaeon Thermococcus kodakarensis , were purified to reveal an extensive, dynamic, and tightly interconnected network of protein interactions that responds to environmental cues (such as the availability of various reductive sinks) to direct electron flux to maximize energetic gains. Interactions connecting disparate functions suggest many catabolic and anabolic activities occur in spatial proximity in vivo , and while protein complexes have been historically defined under optimal conditions, many of these complexes appear to maintain alternative partnerships in changing conditions. The totality of the results obtained redefines our understanding of in vivo assemblies driving ancient metabolic strategies supporting the growth of modern Archaea. IMPORTANCE Given the potential for rational genetic manipulations of biofuel- and biotech-promising archaea to yield transformative results for major markets, it is a priority to define how the metabolisms of such species are controlled, at least in part, by in vivo protein assemblies, and from such, define routes of energy flux that can be most efficiently altered toward biofuel or biotechnological gains. Proteinaceous electron carriers (PECs, such as ferredoxins) offer the potential for specific protein–protein interactions to coordinate selective reductive flow. Employing the model, genetically accessible, hyperthermophilic archaeon, Thermococcus kodakarensis , we establish the metabolic protein interactome of 25 key redox proteins, revealing that each redox active protein has a dynamic partnership profile, suggesting catabolic and anabolic activities may occur in concert and in temporal and spatial proximity in vivo . These results reveal critical importance in evaluating the newly identified partnerships and their role and utility in providing regulated redox flux in T. kodakarensis .

Williams, Sere A. (ORCID:0000000235509590)↗
DOE OSTI2018

Broad Genomic Sampling Reveals a Smut Pathogenic Ancestry of the Fungal Clade Ustilaginomycotina

Ustilaginomycotina is home to a broad array of fungi including important plant pathogens collectively called smut fungi. Smuts are biotrophs that produce characteristic perennating propagules called teliospores, one of which, Ustilago maydis , is a model genetic organism. Broad exploration of smut biology has been hampered by limited phylogenetic resolution of Ustilaginiomycotina as well as an overall lack of genomic data for members of this subphylum. In this study, we sequenced eight Ustilaginomycotina genomes from previously unrepresented lineages, deciphered ordinal-level phylogenetic relationships for the subphylum, and performed comparative analyses. Unlike other Basidiomycota subphyla, all sampled Ustilaginomycotina genomes are relatively small and compact. Ancestral state reconstruction analyses indicate that teliospore formation was present at the origin of the subphylum. Divergence time estimation dates the divergence of most extant smut fungi after that of grasses (Poaceae). However, we found limited conservation of well-characterized genes related to smut pathogenesis from U. maydis , indicating dissimilar pathogenic mechanisms exist across other smut lineages. The genomes of Malasseziomycetes are highly diverged from the other sampled Ustilaginomycotina, likely due to their unique history as mammal-associated lipophilic yeasts. Despite extensive genomic data, the phylogenetic placement of this class remains ambiguous. Although the sampled Ustilaginomycotina members lack many core enzymes for plant cell wall decomposition and starch catabolism, we identified several novel carbohydrate active enzymes potentially related to pectin breakdown. Finally, ~50% of Ustilaginomycotina species-specific genes are present in previously undersampled and rare lineages, highlighting the importance of exploring fungal diversity as a resource for novel gene discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗