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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 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 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 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 OSTI2025

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identification and characterization of a skin microbiome on Caenorhabditis elegans suggests environmental microbes confer cuticle protection

ABSTRACT In the wild, C. elegans are emersed in environments teeming with a veritable menagerie of microorganisms. The C. elegans cuticular surface serves as a barrier and first point of contact with their microbial environments. In this study, we identify microbes from C. elegans natural habitats that associate with its cuticle, constituting a simple “skin microbiome.” We rear our animals on a modified CeMbio, mCeMbio, a consortium of ecologically relevant microbes. We first combine standard microbiological methods with an adapted micro skin-swabbing tool to describe the skin-resident bacteria on the C. elegans surface. Furthermore, we conduct 16S rRNA gene sequencing studies to identify relative shifts in the proportion of mCeMbio bacteria upon surface-sterilization, implying distinct skin- and gut-microbiomes. We find that some strains of bacteria, including Enterobacter sp. JUb101 , are primarily found on the nematode skin, while others like Stenotrophomonas indicatrix JUb19 and Ochrobactrum vermis MYb71 are predominantly found in the animal’s gut. Finally, we show that this skin microbiome promotes host cuticle integrity in harsh environments. Together, we identify a skin microbiome for the well-studied nematode model and propose its value in conferring host fitness advantages in naturalized contexts. IMPORTANCE The genetic model organism C. elegans has recently emerged as a tool for understanding host–microbiome interactions. Nearly all of these studies either focus on pathogenic or gut-resident microbes. Little is known about the existence of native, nonpathogenic skin microbes or their function. We demonstrate that members of a modified C. elegans model microbiome, mCeMbio, can adhere to the animal's cuticle and confer protection from noxious environments. We combine a novel micro-swab tool, the first 16S microbial sequencing data from relatively unperturbed C. elegans , and physiological assays to demonstrate microbially mediated protection of the skin. This work serves as a foundation to explore wild C. elegans skin microbiomes and use C. elegans as a model for skin research.

16S RNA↗
DOE OSTI2026

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

Abstract The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness and genotype-by-environment interactions hinder our understanding of the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different genetic variant types for a Saccharomyces cerevisiae population. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation showed that different variant types identified distinct gene sets associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to predictions were both isolate- and environment-dependent, pointing to gene-by-gene and gene-by-environment interactions. Furthermore, models uncovered experimentally validated and novel candidate genetic interactions for a well-characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different genetic variant types and offer novel targets for future functional analysis.

DNA copy number variations↗
DOE OSTI2025

Unveiling shared genetic regulators of plant architectural and biomass yield traits in the Sorghum Association Panel

Abstract Sorghum is emerging as an ideal genetic model for designing high-biomass bioenergy crops. Biomass yield, a complex trait influenced by various plant architectural characteristics, is typically regulated by numerous genes. This study aimed to dissect the genetic regulators underlying 14 plant architectural traits and 10 biomass yield traits in the Sorghum Association Panel across two growing seasons. We identified 321 associated loci through genome-wide association studies (GWAS), involving 234 264 single nucleotide polymorphisms (SNPs). These loci include genes with known associations to biomass traits, such as maturity, dwarfing (Dw), and leafbladeless1, as well as several uncharacterized loci not previously linked to these traits. We also identified 22 pleiotropic loci associated with variation in multiple phenotypes. Three of these loci, located on chromosomes 3 (S03_15463061), 6 (S06_42790178; Dw2), and 9 (S09_57005346; Dw1), exerted significant and consistent effects on multiple traits across both growing seasons. Additionally, we identified three genomic hotspots on chromosomes 6, 7, and 9, each containing multiple SNPs associated with variation in plant architecture and biomass yield traits. Chromosome-wise correlation analyses revealed multiple blocks of positively associated SNPs located near or within the same genomic regions. Finally, genome-wide correlation-based network analysis showed that loci associated with flowering, plant height, leaf traits, plant density, and tiller number per plant were highly interconnected with other genetic loci influencing plant architectural and biomass yield traits. The pyramiding of favorable alleles related to these traits holds promise for enhancing the future development of bioenergy sorghum crops.

Singh, Anuradha (ORCID:0000000197149095)↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2025

Developing a robust strength model using physically-informed genetic programming

The strength of materials is influenced by a range of external conditions, such as temperature and deformation rate. Consequently, materials that demonstrate substantial variations in their mechanical behavior due to fluctuations in temperature and strain rate require complex strength models to accurately predict material performance in real-world applications. To predict such complex behavior, a robust and flexible strength model is necessary. In this work, we utilize genetic programming-based symbolic regression (GPSR) to develop data-driven strength models that accurately represent the measured stress–strain responses of tin across a wide range of strain, strain rate and temperature regimes. The GPSR models are constrained by physically-informed conditions, which leads to significant improvement in extrapolation. The best model is integrated into a multi-physics code to perform Taylor impact simulations, validating the model’s accuracy and robustness. In conclusion, the model predictions showed excellent agreement with experimental results, particularly when compared to predictions using traditional strength models.

Genetic programming↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2024

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Tailoring Microbial Fitness Through Computational Steering and CRISPRi-Driven Robustness Regulation

The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Mechanisms of Polyethylene Terephthalate Pellet Fragmentation into Nanoplastics and Assimilable Carbons by Wastewater Comamonas

Comamonadaceae bacteria are enriched on poly(ethylene terephthalate) (PET) microplastics in wastewaters and urban rivers, but the PET-degrading mechanisms remain unclear. Here, we investigated these mechanisms with Comamonas testosteroniKF-1, a wastewater isolate, by combining microscopy, spectroscopy, proteomics, protein modeling, and genetic engineering. Compared to minor dents on PET films, scanning electron microscopy revealed significant fragmentation of PET pellets, resulting in a 3.5-fold increase in the abundance of small nanoparticles (<100 nm) during 30-day cultivation. Infrared spectroscopy captured primarily hydrolytic cleavage in the fragmented pellet particles. Solution analysis further demonstrated double hydrolysis of a PET oligomer, bis(2-hydroxyethyl) terephthalate, to the bioavailable monomer terephthalate. Supplementation with acetate, a common wastewater co-substrate, promoted cell growth and PET fragmentation. Of the multiple hydrolases encoded in the genome, intracellular proteomics detected only one, which was found in both acetate-only and PET-only conditions. Homology modeling of this hydrolase structure illustrated substrate binding analogous to reported PET hydrolases, despite dissimilar sequences. Mutants lacking this hydrolase gene were incapable of PET oligomer hydrolysis and had a 21% decrease in PET fragmentation; re-insertion of the gene restored both functions. Thus, we have identified constitutive production of a key PET-degrading hydrolase in wastewater Comamonas, which could be exploited for plastic bioconversion.

54 ENVIRONMENTAL SCIENCES↗