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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 OSTI2022

Permeability-Engineered Compartmentalization Enables In Vitro Reconstitution of Sustained Synthetic Biology Systems

In nature, biological compartments such as cells rely on dynamically controlled permeability for matter exchange and complex cellular activities. Likewise, the ability to engineer compartment permeability is crucial for in vitro systems to gain sustainability, robustness, and complexity. However, rendering in vitro compartments such a capability is challenging. Here, a facile strategy is presented to build permeability-configurable compartments, and marked advantages of such compartmentalization are shown in reconstituting sustained synthetic biology systems in vitro. Through microfluidics, the strategy produces micrometer-sized layered microgels whose shell layer serves as a sieving structure for biomolecules and particles. In this configuration, the transport of DNAs, proteins, and bacteriophages across the compartments can be controlled an guided by a physical model. Through permeability engineering, a compartmentalized cell-free protein synthesis system sustains multicycle protein production; ≈100 000 compartments are repeatedly used in a five-cycle synthesis, featuring a yield of 2.2 mg mL -1 . Further, the engineered bacteria-enclosing compartments possess near-perfect phage resistance and enhanced environmental fitness. In a complex river silt environment, compartmentalized whole-cell biosensors show maintained activity throughout the 32 h pollutant monitoring. It is anticipated that permeability-engineered compartmentalization should pave the way for practical synthetic biology applications such as green bioproduction, environmental sensing, and bacteria-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants

We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

3D seismic imaging of a fracture damage zone controlling reservoir compartmentalization at the Raft River EGS using multi-azimuth walkaway VSP

Accurate imaging of steeply dipping fracture zones in crystalline enhanced geothermal systems (EGS) is critical for constraining permeability architecture and guiding stimulation design. However, such structures remain poorly resolved by conventional surface seismic methods. We present a fully three-dimensional (3D) elastic-waveform inversion-migration workflow applied to multi-azimuth walkaway vertical seismic profiling (VSP) data acquired in a deviated borehole at the Raft River EGS. The workflow integrates first-arrival traveltime tomography, multi-scale elastic waveform inversion (EWI), and elastic least-squares reverse-time migration (ELSRTM) to recover high-resolution compressional-and shear-wave velocity models and to image structural discontinuities in the crystalline basement. The results reveal a laterally continuous low-velocity anomaly, with shear-wave velocity reductions of 25-30%, consistent with fractureinduced mechanical weakening. Two steeply dipping discontinuities bound a 50-80 m wide fracture damage zone. Independent constraints from microseismic clustering and geochemical compartmentalization corroborate the geometry and structural significance of this feature. Synthetic modeling further confirms that structures of this scale are resolvable (~30 m). These findings indicate that the Narrows structure is a distributed fracture damage zone rather than a discrete fault plane. By resolving fracture-zone geometry at the tens-of-meters scale using a single borehole, this workflow provides a practical and transferable approach for improving structural characterization, reducing uncertainty in permeability architecture, and supporting reservoir modeling and stimulation design in fractured crystalline EGS reservoirs.

58 GEOSCIENCES↗
DOE OSTI2026

Compartmental Control of UDP-Araf Supply: Golgi Lumen UDP-Arabinopyranose Mutase Depletes Plant Cell Wall Araf Linkages

L-Arabinofuranose (Araf) is a major constituent of plant cell wall polysaccharides. UDP-Araf is generated from UDP-L-arabinopyranose (UDP-Arap) by a UDP-Arap mutase (UAM) on the cytosolic face of the Golgi and then imported into the Golgi lumen for utilization by arabinosyltransferases (AraTs). Yet, a substantial fraction of UDP-Arap is synthesized in the Golgi lumen by the UDP-xylose 4-epimerase (UXE), creating a topological paradox where luminal UDP-Arap must be exported to the cytosol for conversion to UDP-Araf by UAM and then re-imported into the Golgi lumen. To test the functional significance of this compartmentation, we mislocalized Arabidopsis (Arabidopsis thaliana) UAM1 to the Golgi lumen by fusing it to the UXE1 transmembrane domain. The fusion protein was present in the Golgi, and protease-protection assays on microsomes supported its luminal orientation. In wild-type and uam1 plants, Golgi-targeted UAM1 reduced total cell wall Arabinose without consistent changes in other monosaccharides. Linkage analyses showed that this decrease reflects selective loss of Araf residues, whereas Arap remain largely unchanged, consistent with reduced UDP-Araf availability for luminal AraTs. Under salt stress, expression of Golgi-targeted UAM1 in uam1 caused root-growth inhibition and maturation-zone swelling that exogenous L-Ara failed to rescue. These results support a hypothesis that the strong thermodynamic bias of the UAM reaction toward UDP-Arap requires spatial separation of UDP-Arap production from UDP-Araf formation to sustain UDP-Araf flux into Golgi arabinosylation and avoid futile intraluminal back-conversion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sequential membrane- and protein-bound organelles compartmentalize genomes during phage infection

Many eukaryotic viruses require membrane-bound compartments for replication, but no such organelles are known to be formed by prokaryotic viruses. Bacteriophages of the Chimalliviridae family sequester their genomes within a phage-generated organelle, the phage nucleus, which is enclosed by a lattice of the viral protein ChmA. We show that inhibiting phage nucleus formation arrests infections at an early stage in which the injected phage genome is enclosed within a membrane-bound early phage infection (EPI) vesicle. Early phage genes are expressed from the EPI vesicle, demonstrating its functionality as a prokaryotic, transcriptionally active, membrane-bound organelle. We also show that the phage nucleus is essential, with genome replication beginning after the injected DNA is transferred from the EPI vesicle to the phage nucleus. Our results show that Chimalliviridae require two sophisticated subcellular compartments of distinct compositions and functions that facilitate successive stages of the viral life cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Photobiological production of high-value pigments via compartmentalized co-cultures using Ca-alginate hydrogels

Abstract Engineered cyanobacterium Synechococcus elongatus can use light and CO 2 to produce sucrose, making it a promising candidate for use in co-cultures with heterotrophic workhorses. However, this process is challenged by the mutual stresses generated from the multispecies microbial culture. Here we demonstrate an ecosystem where S. elongatus is freely grown in a photo-bioreactor (PBR) containing an engineered heterotrophic workhorse (either β-carotene-producing Yarrowia lipolytica or indigoidine-producing Pseudomonas putida ) encapsulated in calcium-alginate hydrogel beads. The encapsulation prevents growth interference, allowing the cyanobacterial culture to produce high sucrose concentrations enabling the production of indigoidine and β-carotene in the heterotroph. Our experimental PBRs yielded an indigoidine titer of 7.5 g/L hydrogel and a β-carotene titer of 1.3 g/L hydrogel, amounts 15–22-fold higher than in a comparable co-culture without encapsulation. Moreover, 13 C-metabolite analysis and protein overexpression tests indicated that the hydrogel beads provided a favorable microenvironment where the cell metabolism inside the hydrogel was comparable to that in a free culture. Finally, the heterotroph-containing hydrogels were easily harvested and dissolved by EDTA for product recovery, while the cyanobacterial culture itself could be reused for the next batch of immobilized heterotrophs. This co-cultivation and hydrogel encapsulation system is a successful demonstration of bioprocess optimization under photobioreactor conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A compartmentalized model of multiphase chemical kinetics

There are significant challenges in predicting multiphase chemical kinetics due to the complex coupling of reaction and mass transport across a phase boundary (i.e., interface). Here, we describe a framework for predicting multiphase kinetics that embeds the elementary kinetic steps of reaction, solvation, and diffusion into a coarse grain spatial description of two phases. The model is constructed to bridge the short-timescale interfacial dynamics observed in molecular simulations with the longer timescales observed in kinetic experiments. A simple set of governing differential equations is derived, which, when solved numerically or analytically, yield accurate predictions of multiphase kinetics in microdroplets. Although the equations are formulated for gas-liquid reactions, the underlying conceptual framework is general and can be applied to transformations in other two-phase systems (solid-liquid, liquid-liquid, etc.).

Chemical kinetics and dynamics↗
DOE OSTI2021

Chromosome compartmentalization alterations in prostate cancer cell lines model disease progression

Prostate cancer aggressiveness and metastatic potential are influenced by gene expression and genomic aberrations, features that can be influenced by the 3D structure of chromosomes inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, from normal epithelium to bone metastasis. We describe spatial compartment identity (A-open versus B-closed) changes with progression in these cell lines and their relation to gene expression changes in both cell lines and patient samples. In particular, 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches are accompanied by changes in the structure, size, and boundaries of topologically associating domains (TADs). Further, compartment changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation. These results suggest that discrete 3D genome structure changes play a deleterious role in prostate cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparative genomic and transcriptomic analyses of trans-kingdom pathogen Fusarium solani species complex reveal degrees of compartmentalization

Background: The Fusarium solani species complex (FSSC) comprises fungal pathogens responsible for mortality in a diverse range of animals and plants, but their genome diversity and transcriptome responses in animal pathogenicity remain to be elucidated. We sequenced, assembled and annotated six chromosome-level FSSC clade 3 genomes of aquatic animal and plant host origins. We established a pathosystem and investigated the expression data of F. falciforme and F. keratoplasticum in Chinese softshell turtle (Pelodiscus sinensis) host. Results: Comparative analyses between the FSSC genomes revealed a spectrum of conservation patterns in chromosomes categorised into three compartments: core, fast-core (FC), and lineage-specific (LS). LS chromosomes contribute to variations in genomes size, with up to 42.2% of variations between F. vanettenii strains. Each chromosome compartment varied in structural architectures, with FC and LS chromosomes contain higher proportions of repetitive elements with genes enriched in functions related to pathogenicity and niche expansion. We identified differences in both selection in the coding sequences and DNA methylation levels between genome features and chromosome compartments which suggest a multi-speed evolution that can be traced back to the last common ancestor of Fusarium. We further demonstrated that F. falciforme and F. keratoplasticum are opportunistic pathogens by inoculating P. sinensis eggs and identified differentially expressed genes also associated with plant pathogenicity. These included the most upregulated genes encoding the CFEM (Common in Fungal Extracellular Membrane) domain. Conclusions: The high-quality genome assemblies provided new insights into the evolution of FSSC chromosomes, which also serve as a resource for studies of fungal genome evolution and pathogenesis. This study also establishes an animal model for fungal pathogens of trans-kingdom hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Design, Synthesis, and Validation: Genome Scale Optimization of Energy Flux through Compartmentalized Metabolic Networks in a Model Photosynthetic Eukaryotic Microbe (Final Report)

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methodology and modeling, coupled with high throughput genome engineering strategies, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require significant advances in methodological throughput as well as improvements in detailed systems biology understanding of photosynthetic processes and cellular metabolism. The ability of diatoms to thrive in upwelling-induced, periodically nutrient-rich conditions makes them the base for the world’s shortest and most energy-efficient food webs. Diatom photosynthesis is estimated to account for between 25% and 40% of the 45-50 billion tons of organic carbon fixed annually in the sea.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Proton Conducting Sulfonated Poly(Ionic Liquid) Block Copolymers

Herein, we report the synthesis of proton-conducting sulfonated poly(ionic liquid) block copolymers (S-PILBCPs) containing one block with sulfonic acid (sulfonated styrene, SS) and the other with an IL moiety (vinylbenzylmethylimidazolium bis(trifluoromethylsulfonyl)imide, VBMIm-TFSI) using reversible addition–fragmentation chain-transfer (RAFT) polymerization and post-polymerization modifications (i.e., functionalization, anion exchange reactions, and sulfonation). The S-PILBCPs uniquely conjoin the SS block with mobile protons (H + ) and the PIL block with mobile anions (TFSI – ), where multiple highly desired properties, including high proton conductivity (from the SS block), and high IL-philicity and oxygen solubility (from the PIL block) can exist compartmentally within a microphase separated morphology (evidenced by differential scanning calorimetry (DSC) and small-angle X-ray scattering (SAXS)). High ion conductivity of 79.7 mS/cm at a PIL block composition of 21.6 mol % was observed at 80 °C and 90% relative humidity (RH) (comparable to the benchmark Nafion ionomer). This work successfully demonstrates the design of S-PILBCPs as a new material platform and showcases its promise as an ionomer for proton exchange membrane fuel cells (PEMFCs) as they simultaneously and compartmentally combine proton conductivity and oxygen solubility. Furthermore, these benefits have recently been leveraged to achieve substantial improvement in oxygen reduction reaction (ORR) activity and subsequently fuel cell performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗