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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.

Kim, Haseong↗
DOE OSTI2019

Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunityin this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Chemical and Biological Catalysis for Plastics Recycling and Upcycling

Plastics pollution is causing an environmental crisis, prompting the development of new approaches for recycling, and upcycling. Here, we review challenges and opportunities in chemical and biological catalysis for plastics deconstruction, recycling, and upcycling. We stress the need for rigorous characterization and use of widely available substrates, such that catalyst performance can be compared across studies. Where appropriate, we draw parallels between catalysis on biomass and plastics, as both substrates are low-value, solid, recalcitrant polymers. Innovations in catalyst design and reaction engineering are needed to overcome kinetic and thermodynamic limitations of plastics deconstruction. Either chemical and biological catalysts will need to act interfacially, where catalysts function at a solid surface, or polymers will need to be solubilized or processed to smaller intermediates to facilitate improved catalyst–substrate interaction. Overall, developing catalyst-driven technologies for plastics deconstruction and upcycling is critical to incentivize improved plastics reclamation and reduce the severe global burden of plastic waste.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2020

Harnessing biological applications of quantum materials: opportunities and precautions

Since the discovery of graphene, research on two-dimensional (2D) quantum materials has generated a plethora of novel materials with the potential to significantly change our lives. Owing to their unique quantum, electronic, and excitonic properties, biosensing and bio-imaging are two specific areas where these quantum materials have started to make a large impact. This article provides a holistic overview into the properties, synthesis methods, biological applications, and toxicology studies of commonly studied quantum materials. Recent research into the biological applications of graphene and transition metal dichalcogenides is highlighted and their unique electronic properties are explored. Though tremendous research is generated in discovering new 2D materials, toxicology studies in the area are lacking. Historically, toxicology studies have lagged significantly behind new material discovery often with dire consequences. The product development cycle of polymeric materials is a testament to the negative consequences of having a reactionary approach to toxicology. With 2D materials set to become a widespread part of everyday life in the near future, we comment on the need for a proactive approach to examining and understanding their toxicology.

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

Depolymerization of lignin for biological conversion through sulfonation and a chelator-mediated Fenton reaction

The generating value from lignin through depolymerization and biological conversion to valuable fuels, chemicals, or intermediates has great promise but is limited by several factors including lack of cost-effective depolymerization methods, toxicity within the breakdown products, and low bioconversion of the breakdown products. High yield depolymerization of natural lignins requires cleaving carbon-carbon bonds in addition to ether bonds. To address that need, we report that a chelator-mediated Fenton reaction can efficiently cleave C-C bonds in sulfonated polymers at or near room temperature, and that unwanted repolymerization can be minimized through optimizing reaction conditions. This method was used to depolymerize lignosulfonate from M w = 28,000 g/mol to M w = 800 g/mol. The breakdown products were characterized by SEC, FTIR and NMR and evaluated for bioavailability. The breakdown products are rich in acid, aldehyde, and alcohol functionalities but are largely devoid of aromatics and aliphatic dienes. A panel of nine organisms were tested for the ability to grow on the breakdown products. Growth at a low level was observed for several monocultures on the depolymerized LS in absence of glucose. Much stronger growth was observed in the presence of 0.2% glucose and for one organism we demonstrate doubling of melanin production in the presence of depolymerized LS. The results suggest that this chelator-mediated Fenton method is a promising new approach for biological conversion of lignin into higher value chemicals or intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Interactions between fullerene derivatives and biological systems

Attention towards nanoparticles from the pharmaceutical and biomedical fields has significantly increased due to their attractive surface modification, high drug-loading, and improved pharmacokinetics. Fullerenes, an allotrope of carbon, stand out for their molecularly precise structure, potent radical-scavenging activity, photoactivatable reactive-oxygen species generation, and ability to definitively confine metal atoms and clusters. Accordingly, fullerene systems have been applied in various biological contexts, including increased and controlled drug delivery, antioxidative, anti-inflammatory, and photodynamic therapy, and magnetic resonance imaging. Ultimately, the pleiotropic activity of fullerenes, coupled with its precise structure and functionalization, can realize precise and tailorable medicines. Here, different from some excellent reviews focusing on the structure and chemistry of fullerene derivatives and their biomedical applications, this review highlights the interaction of fullerene materials with biological systems, with insights into their structural influence on their interactions with the cellular environment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation

Abstract Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor.

Wong, Gerard C. L. (ORCID:0000000308936383)↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Does the Presence of the Biological Control Agent, Hypena opulenta (Lepidoptera: Erebidae) on Swallow-Worts Deter Monarch Oviposition?

Abstract Invasive black and pale swallow-worts (Vincetoxicum nigrum (L.) Moench, and Vincetoxicum rossicum Kelopow), which are related to milkweeds, can act as ecological traps for monarch butterflies (Danaus plexippus L. (Lepidoptera: Nymphalidae)) as they lay eggs on them that fail to develop. A recently approved biological control agent against swallow-worts, Hypena opulenta Christoph, occupies the same feeding guild on swallow-worts as monarch larvae and could be perceived as a competitor to monarchs. We tested how the presence of this defoliating moth on swallow-worts may influence monarch host selection. In a two-year field experiment, we placed pale swallow-wort plants that were either infested with H. opulenta or noninfested as well as common milkweed (Asclepias syriaca L.), into monarch habitats to assess oviposition rates. In the laboratory, monarchs were either given a choice or not between milkweeds and black swallow-worts with or without H. opulenta. While monarchs strongly preferred common milkweed in the field, up to 25% of the eggs we observed were laid on pale swallow-wort, without preference for swallow-wort with (10.7%) or without (14.3%) H. opulenta. In laboratory choice and no-choice tests, monarchs did not lay any eggs on black swallow-wort, likely because of the long-term laboratory rearing on common milkweeds. Our results confirm that pale swallow-wort may act as an oviposition sink to monarchs in Michigan as well. Since the biological control program is still in its infancy, the nature of interactions between monarchs and H. opulenta may change as the biocontrol agent becomes more widespread.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Language Barriers in Organismal Biology: What Can Journals Do Better?

Synopsis In the field of organismal biology, as in much of academia, there is a strong incentive to publish in internationally recognized, highly regarded, English-language journals to promote career advancement. This expectation has created a linguistic hegemony in scientific publishing, whereby scholars for whom English is an additional language face additional barriers to achieving the same scientific recognition as scholars who speak English as a first language. Here, we surveyed the author guidelines of 230 journals in organismal biology with impact factors of 1.5 or greater for linguistically inclusive and equitable practices and policies. We looked for efforts that reflect first steps toward reducing barriers to publication for authors globally, including the presence of statements that encouraged submissions from authors of diverse nationalities and backgrounds, policies regarding manuscript rejection based on perceived inadequacies of the English language, the existence of bias-conscious reviewer practices, whether translation and editing resources or services are available, allowance for non-English abstracts, summaries, or translations, and whether journals offer license options that would permit authors (or other scholars) to translate their work and publish it elsewhere. We also directly contacted a subset of journals to verify whether the information on their author guidelines page accurately reflects their policies and the accommodations they would make. We reveal that journals and publishers have made little progress toward beginning to recognize or reduce language barriers. Counter to our predictions, journals associated with scientific societies did not appear to have more inclusive policies compared to non-society journals. Many policies lacked transparency and clarity, which can generate uncertainty, result in avoidable manuscript rejections, and necessitate additional time and effort from both prospective authors and journal editors. We highlight examples of equitable policies and summarize actions that journals can take to begin to alleviate barriers to scientific publishing.

Nolde-Lopez, B.↗
DOE OSTI2022

X-ray fluorescence microscopy methods for biological tissues

Abstract Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data-acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pretreatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.

Pushie, M. Jake (ORCID:0000000174945427)↗
DOE OSTI2025

A mixture parameterized biologically based dosimetry model to predict body burdens of polycyclic aromatic hydrocarbons in developmental zebrafish toxicity assays

Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental toxicants found ubiquitously as complex mixtures in human-impacted environments. Developmental zebrafish exposures have been used widely to study PAH toxicity, but most studies report nominal exposure concentrations. Nominal exposure concentrations can be unreliable dose metrics due to differences in toxicant bioavailability resulting from disparate exposure methodologies and chemical properties. Toxicokinetic modeling can predict toxicant tissue doses to facilitate comparison between exposures of different chemicals, methodologies, and biological models. We parameterize a biologically based dosimetry model for developmental zebrafish toxicity assays for 9 PAHs. The model was optimized with measurements from media, tissue, and plastic plate walls throughout a static developmental exposure to a mixture of 10 PAHs of high abundance within the Portland Harbor Superfund Site. Plate binding, volatilization, zebrafish permeability, and tissue—media partitioning coefficients vary widely between PAHs. Model predictions accounted for 83% and 54% of 48 hpf body burdens within a factor of 2 resulting from exposures to mixtures and individual PAHs, respectively. Accounting for solubility significantly improves model performance. Competition for active sites in metabolizing enzymes may change biotransformation kinetics between individual PAH and mixture exposures. Area under the curve estimations of concentrations in zebrafish resulted in altered hazard rankings from nominal exposure concentrations. Future work will be oriented to generalizing the model to other PAHs. This PAH dosimetry model improves the interpretability of developmental zebrafish toxicity assays by providing time-resolved body burdens from nominal exposure concentrations.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Capacitive response of biological membranes

We present a minimal model to analyze the capacitive response of a biological membrane subjected to a step voltage via blocking electrodes. Through a perturbative analysis of the underlying electrolyte transport equations, we show that the leading-order relaxation of the transmembrane potential is governed by a capacitive timescale, τ_{C}=λ_{D}L/D(2+Γδ^{M}/L/4+Γδ^{M}/λ_{D}), where λ_{D} is the Debye screening length, L is the electrolyte width, Γ is the ratio of the permittivity of the electrolyte to the membrane, δ^{M} is the membrane thickness, and D is the ionic diffusivity. This timescale is considerably shorter than the traditional RC timescale λ_{D}L/D for a bare electrolyte due to the membrane's low permittivity and finite thickness. Beyond the linear regime, however, salt diffusion in the bulk electrolyte drives a secondary, nonlinear relaxation process of the transmembrane potential over a longer timescale τ_{L}=L^{2}/4π^{2}D. A simple equivalent-circuit model accurately captures the linear behavior, and the perturbation expansion remains applicable across the entire range of observed physiological transmembrane potentials. Together, these findings underscore the importance of the faster capacitive timescale and nonlinear effects on the bulk diffusion timescale in determining transmembrane potential dynamics for a range of biological systems.

Farhadi, Jafar↗
DOE OSTI2024

Three Photon Excited Image Scanning Microscopy for in-Depth Super-Resolution Studies of Biological Samples

Multiphoton laser scanning microscopy is a powerful tool for deep imaging of thick biological samples. Image scanning microscopy (ISM) has demonstrated significant improvements in the signal-to-noise ratio in confocal laser scanning microscopy, while at the same time improving upon the effectively attainable resolution. Two-photon excitation (2PE), combined with ISM, has been shown to allow for deep tissue imaging with enhanced resolution compared to 2PE microscopy. Three-photon excitation (3PE) has enabled record imaging depth and contrast for multiphoton imaging, due to the superior suppression of out-of-focus signal generation. In this paper, we demonstrate super-resolution 3PE ISM. This is achieved using a single-photon avalanche detector array, and 1040-nm pulses for 3PE of blue fluorescence. This method enables subdiffraction limited resolution imaging of biological samples stained with blue fluorescent markers, such as mouse myocardial and spinal cord tissues stained with 4 ′ , 6 -diamidino-2-phenylindole. Deconvolution improves the resolving power further and allows for imaging with better than λ / 8 resolution with respect to the 3PE wavelength λ . With the ISM pixel reassignment procedure, we demonstrate a resolution enhancement of ∼ 1.6 laterally, compared to the resolution attained using a photomultiplier tube in a non-descanned detection arrangement, and a factor of ∼ 1.8 enhancement in axial resolution. The experimentally measured three-dimensional point spread function volume is shrunk ∼ 4.4 -fold, which is close to the theoretically expected enhancement. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗
DOE OSTI2019

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗
DOE OSTI2026

SEGUID v2: Extending SEGUID checksums for circular, linear, single- and double-stranded biological sequences

Background Synthetic biology involves combining different DNA fragments, each containing functional biological parts, to address specific problems. Fundamental gene-function research often requires cloning and propagating DNA fragments, such as those from the iGEM Parts Registry or Addgene, typically distributed as circular plasmids. Addgene’s repository alone offers around 150,000 plasmids. To ensure data integrity, cryptographic checksums can be calculated for the sequences. Each sequence has a unique checksum, making checksums useful for validation and quick lookups of associated annotations. For example, the SEGUID checksum uniquely identifies protein sequences with a 27-character string. Objectives The original SEGUID, while effective for protein sequences and single-stranded DNA (ssDNA), is not suitable for circular DNA since there is no natural starting position nor for double-stranded DNA (dsDNA) since two separate sequences are present. Challenges include how to uniquely represent linear dsDNA, circular ssDNA, and circular dsDNA. To meet these needs, we propose SEGUID v2, which extends the original SEGUID to handle additional types of sequences. Conclusions SEGUID v2 produces orientation and rotation invariant checksums for single-stranded, double-stranded, possibly staggered, linear, and circular DNA and RNA sequences. Customizable alphabets allow for other types of sequences. In contrast to the original SEGUID, which uses Base64, SEGUID v2 uses Base64url to encode the SHA-1 hash. This ensures SEGUID v2 checksums can be used as-is in filenames, regardless of platform, and in URLs, with minimal friction. Availability SEGUID v2 is readily available for major programming languages, distributed under the MIT license. JavaScript package seguid is available on npm, Python package seguid on PyPi, R package seguid on CRAN, and a Tcl script on GitHub. These tools, along with documentation, examples, and an online SEGUID Calculator , can be found at https://www.seguid.org .

Pereira, Humberto↗
DOE OSTI2019

AmeriFlux US-xKZ NEON Konza Prairie Biological Station (KONZ)

This is the AmeriFlux version of the carbon flux data for the site US-xKZ NEON Konza Prairie Biological Station (KONZ). Site Description - Konza Prairie Biological Station (KPBS) was established to provide a "natural laboratory" to conduct ecological research and is located on a 3,487 hectare native tallgrass prairie preserve. KPBS is a field research station dedicated to conservation, education and long-term ecological research. Over 1,580 scientific papers have been published by scientists conducting studies at KPBS. The study of ecological patterns and processes in native tallgrass prairie ecosystems is the primary subject of research.

Network), NEON (National Ecological Observatory↗