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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Cell-free synthetic biology for natural product biosynthesis and discovery

Natural products have applications as biopharmaceuticals, agrochemicals, and other high-value chemicals. However, there are challenges in isolating natural products from their native producers (e.g. bacteria, fungi, plants). In many cases, synthetic chemistry or heterologous expression must be used to access these important molecules. The biosynthetic machinery to generate these compounds is found within biosynthetic gene clusters, primarily consisting of the enzymes that biosynthesise a range of natural product classes (including, but not limited to ribosomal and nonribosomal peptides, polyketides, and terpenoids). Cell-free synthetic biology has emerged in recent years as a bottom-up technology applied towards both prototyping pathways and producing molecules. Recently, it has been applied to natural products, both to characterise biosynthetic pathways and produce new metabolites. This review discusses the core biochemistry of cell-free synthetic biology applied to metabolite production and critiques its advantages and disadvantages compared to whole cell and/or chemical production routes. Specifically, we review the advances in cell-free biosynthesis of ribosomal peptides, analyse the rapid prototyping of natural product biosynthetic enzymes and pathways, highlight advances in novel antimicrobial discovery, and discuss the rising use of cell-free technologies in industrial biotechnology and synthetic biology.

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

BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules

A major goal in biomedical science is to move beyond static images of proteins and other biological macromolecules to the internal dynamics underlying their function. This level of study is necessary to understand how these molecules work and to engineer new functions and modulators of function. Stemming from a visionary commitment to this problem by Keith Moffat decades ago, a community of structural biologists has now enabled a set of x-ray scattering technologies for observing intramolecular dynamics in biological macromolecules at atomic resolution and over the broad range of timescales over which motions are functionally relevant. Many of these techniques are provided by BioCARS, a cutting-edge synchrotron radiation facility built under Moffat leadership and located at the Advanced Photon Source at Argonne National Laboratory. BioCARS enables experimental studies of molecular dynamics with time resolutions spanning from 100 ps to seconds and provides both time-resolved x-ray crystallography and small- and wide-angle x-ray scattering. Structural changes can be initiated by several methods—UV/Vis pumping with tunable picosecond and nanosecond laser pulses, substrate diffusion, and global perturbations, such as electric field and temperature jumps. Studies of dynamics typically involve subtle perturbations to molecular structures, requiring specialized computational techniques for data processing and interpretation. In this review, we present the challenges in experimental macromolecular dynamics and describe the current state of experimental capabilities at this facility. As Moffat imagined years ago, BioCARS is now positioned to catalyze the scientific community to make fundamental advances in understanding proteins and other complex biological macromolecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

How a raindrop gets shattered on biological surfaces

Many biological surfaces of animals and plants (e.g., bird feathers, insect wings, plant leaves, etc.) are superhydrophobic with rough surfaces at different length scales. Previous studies have focused on a simple drop-bouncing behavior on biological surfaces with low-speed impacts. However, we observed that an impacting drop at high speeds exhibits more complicated dynamics with unexpected shock-like patterns: Hundreds of shock-like waves are formed on the spreading drop, and the drop is then abruptly fragmented along with multiple nucleating holes. Such drop dynamics result in the rapid retraction of the spreading drop and thereby a more than twofold decrease in contact time. Our results may shed light on potential biological advantages of hypothermia risk reduction for endothermic animals and spore spreading enhancement for fungi via wave-induced drop fragmentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

KG-Hub—building and exchanging biological knowledge graphs

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle

By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testisspecific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Probabilistic measures for biological adaptation and resilience

This paper introduces an approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from nonequilibrium statistical mechanics, we propose a measure termed “ecological resilience through adaptation,” specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. Importantly, the resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Proteomes reveal metabolic capabilities of Yarrowia lipolytica for biological upcycling of polyethylene into high-value chemicals

ABSTRACT Polyolefins derived from plastic wastes are recalcitrant for biological upcycling. However, chemical depolymerization of polyolefins can generate depolymerized plastic (DP) oil, comprising of a complex mixture of saturated, unsaturated, even, and odd hydrocarbons suitable for biological conversion. While DP oil contains a rich carbon and energy source, it is inhibitory to cells. Understanding and harnessing robust metabolic capabilities of microorganisms to upcycle the hydrocarbons in DP oil, both naturally and unnaturally occurring, into high-value chemicals are limited. Here, we discovered that an oleaginous yeast, Yarrowia lipolytica, undergoing short-term adaptation to DP oil robustly utilized a wide range of hydrocarbons for cell growth and production of citric acid and neutral lipids. When growing on hydrocarbons, Y. lipolytica partitioned into planktonic and oil-bound cells with each exhibiting distinct proteomes and amino acid distributions invested in establishing these proteomes. Significant proteome reallocation toward energy and lipid metabolism, belonging to 2 of the 23 Eukaryotic Orthologous Groups classes C and I, enabled robust growth of Y. lipolytica on hydrocarbons, with n-hexadecane as the preferential substrate. This investment was even higher for growth on DP oil where classes C and I were ranked one and two, respectively, and many associated proteins and pathways were expressed and upregulated including the hydrocarbon degradation pathway, Krebs cycle, glyoxylate shunt and, unexpectedly, propionate metabolism. However, a reduction in proteome allocation for protein biosynthesis, at the expense of the observed increase toward energy and lipid metabolisms, might have caused the inhibitory effect of DP oil on cell growth. IMPORTANCE Sustainable processes for biological upcycling of plastic wastes in a circular bioeconomy are needed to promote decarbonization and reduce environmental pollution due to increased plastic consumption, incineration, and landfill storage. Strain characterization and proteomic analysis revealed the robust metabolic capabilities of Yarrowia lipolytica to upcycle polyethylene into high-value chemicals. Significant proteome reallocation toward energy and lipid metabolisms was required for robust growth on hydrocarbons with n-hexadecane as the preferential substrate. However, an apparent over-investment in these same categories to utilize complex depolymerized plastic (DP) oil came at the expense of protein biosynthesis, limiting cell growth. Taken together, this study elucidates how Y. lipolytica activates its metabolism to utilize DP oil and establishes Y. lipolytica as a promising host for the upcycling of plastic wastes.

1-alkenes↗
DOE OSTI2025

A Roadmap for the Future of Systems Biology in Cancer Research

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.

Modeling & Simulation↗
DOE OSTI2023

How to engineer the unknown: Advancing a quantitative and predictive understanding of plant and soil biology to address climate change

Our basic understanding of carbon cycling in the biosphere remains qualitative and incomplete, precluding our ability to effectively engineer novel solutions to climate change. How can we attempt to engineer the unknown? This challenge has been faced before in plant biology, providing a roadmap to guide future efforts. We use examples from over a century of photosynthesis research to illustrate the key principles that will set future plant engineering on a solid footing, namely, an effort to identify the key control variables, quantify the effects of systematically tuning these variables, and use theory to account for these observations. The main contributions of plant synthetic biology will stem not from delivering desired genotypes but from enabling the kind of predictive understanding necessary to rationally design these genotypes in the first place. Only then will synthetic plant biology be able to live up to its promise.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Applicability for MELCOR Accident Code System (MACCS) to Model the Atmospheric Transport of Biological Contaminants

Characterization of airborne transmission behavior for biological contaminants is a critical aspect in developing public health guidelines in the event of an outbreak. Guidelines such as safe separation distances and physical protections (mask and face-shields) are heavily influenced by the particular biological airborne transmission risk, which depends on a multitude of factors and is not easily or readily determined, therefore potentially lagging behind the time sensitive need to inform effective public health guidelines. Herein is proposed a means to leverage the substantial investment over the last three decades of the Nuclear Regulatory Commission in the MACCS code suite developed at Sandia National Laboratories. This work includes augmenting the MACCS code to represent biological rather than radioactive contaminants in the atmosphere, therefore characterizing the airborne transmission risk for such contaminants and helping to inform critical and time sensitive public health guidelines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Determination of Total, Organic, and Inorganic Carbon in Biological Cultures and Liquid Fraction Process Samples

This procedure describes a method for analyzing dissolved and suspended total carbon, in organic and inorganic form in biological cultures, in the presence or absence of cells, and liquid fraction conversion process samples. This method calculates total organic carbon by subtraction of inorganic carbon, derived from sample acidification, from total dissolved carbon by combustion at 680°C with detection of derived gaseous CO 2 by a non-dispersive infrared (NDIR) sensor. The inclusion of details on a process step of heat treating biologically active samples before analysis can potentially halt biological activity, preserving carbon stores and allowing for accurate quantification of the carbon content at the time of sample collection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology (Final Technical Report)

The 5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology will be held virtually from November 15 to 17, 2021. The meeting is organized by the American Institute of Chemical Engineers (AIChE), which has an excellent and long-standing record convening scientific conferences and events. This conference has been held annually since 2016 and it is expected to as successful as previous conferences. The planned sessions featured BER-relevant topics including mew technologies, bioenergy, bioproducts and materials, plant metabolic engineering, crops for future climates, and biosafety. The event brought together scientists and engineers from universities, industry, and government working in all aspects of plant synthetic biology, plant bioengineering and plant biotechnology. The conference provided a forum for leading scientists to interact with new investigators and students and engage in discussions on the latest scientific advances and technology developments in plant synthetic biology and genome engineering. The organizing committee and meeting participants include diverse members of the BER research community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

6 th International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology (Workshop Report)

The objective of the project is to hold a Conference to bring together scientists and engineers from universities, industry and government working in all aspects of plant synthetic biology, plant bioengineering and plant biotechnology. To meet this objective, AIChE will host the 6th International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology on December 9-11, 2022 in Fort Lauderdale, Florida. The conference will feature five technical sessions: (1) Genome and Epigenome Editing, (2) Specialized Metabolism, (3) Engineering Plant Resilience, (4) Engineering more efficient plants, and (5) New tools and Technologies. The conference will allow leading scientists and engineers from universities, industry and government to discuss new tools and technologies being developed as well as their applications, and gaps in the field. The conference will allow students to learn about these new technologies, engage in a discussion with professionals in their field of interest, and showcase their work. The scope, theme, and aims of the Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology fall squarely within the mission of DOE which includes securing US leadership in energy technology and to maintaining a vibrant US effort in science and engineering as a cornerstone for our economic prosperity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Biologically Cooling the Planet: A BRISK Approach that Directly Connects the Ground to Deep Space

Climate change is a grave threat to our ability to thrive and prosper on Earth. Accumulation of greenhouse gases in the atmosphere stifle the Earth’s ability to cool itself, which leads to a rise of the average temperature – Global warming. The planetary cooling is exclusively radiative – thermal radiation escapes into cold outer space via a flux of photons at the low-infrared range. The penetration of these photons through Earth’s atmosphere is dictated by atmospheric transparency, which, serendipitously possesses a window centered around the peak of the Plank spectrum for a black body at about room temperature – the average temperature of Earth. This window spanning about 8 --13 μm wavelengths allows most of the photons in this range to escape into the Cosmos carrying our planet’s heat away. The efficiency of this outer heat flux – and thus planet’s cooling ability – can be controlled via emissivity – a function of wavelength which multiplies the fundamental Plank spectrum of a black body at a given temperature. In the presence of non-trivial atmospheric transparency it can be shown that radiative cooling can be enhanced with properly “designed” emissivity function. For the last 100 years, or so, people have been looking into ways to engineer materials to take advantage of such enhancement for various cooling applications, from making ice to cool clothes, and substantial progress has been achieved, including cooling in direct sunlight. The beauty if this approach is that it is entirely passive – no energy input is required. The objective of this project was to investigate whether biological systems possess such advanced cooling capacity, developed via millennia of evolution, to enhance plant’s adaptation to a hot environment, to increase cooling capability, and possibly modulate spectral emissivities on short or long time scales. We wanted to know if BRISK (Biological Radiative Sky Cooling) occurs and to what extent. It is unknown whether biological systems can perform this "spectral redistribution." It is reasonable to expect, however, that they might: in particular, desert plants under high heat stress and arid conditions might evolve this mechanism in order to maintain a tolerable internal temperature. Our intended approach was twofold: extensive literature search and supporting experimental investigations on select plants. We obtained the answers from the former, while the latter proved to be more complicated to perform with required accuracy in the timespan/budget of a small seedling project. After one year of research, we concluded that among the plant species studied there seem to be no evidence of BRISK, but it’s not the end of the story. Our planet’s biosphere is immense, and the evolution is continuing. With our preliminary results and a better understanding of what to look for further discoveries await, considering the accelerated pace of infrared imaging and spectroscopy technology development, from microscopic to satellite-based studies of plants and canopies. In the end, Earth’s energy balance is ultimately tied to radiative cooling so that further investigation into this problem is directly tied to solving the Climate Change challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

National User Resource for Biological Accelerator Mass Spectrometry Annual Report

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science.

59 BASIC BIOLOGICAL SCIENCES↗