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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Poplar: a phylogenomics pipeline

Motivation Generating phylogenomic trees from the genomic data is essential in understanding biological systems. Each step of this complex process has received extensive attention and has been significantly streamlined over the years. Given the public availability of data, obtaining genomes for a wide selection of species is straightforward. However, analyzing that data to generate a phylogenomic tree is a multistep process with legitimate scientific and technical challenges, often requiring a significant input from a domain-area scientist. Results We present Poplar, a new, streamlined computational pipeline, to address the computational logistical issues that arise when constructing the phylogenomic trees. It provides a framework that runs state-of-the-art software for essential steps in the phylogenomic pipeline, beginning from a genome with or without an annotation, and resulting in a species tree. Running Poplar requires no external databases. In the execution, it enables parallelism for execution for clusters and cloud computing. The trees generated by Poplar match closely with state-of-the-art published trees. The usage and performance of Poplar is far simpler and quicker than manually running a phylogenomic pipeline. Availability and implementation Freely available on GitHub at https://github.com/sandialabs/poplar. Implemented using Python and supported on Linux.

Koning, Elizabeth [Sandia National Laboratories (S↗
DOE OSTI2020

Functional, Hierarchical Colloidal Liquid Crystal Gels and Liquid Crystal Elastomers Nanocomposites. Final Report

Biological systems rely on a hierarchy of dynamic, complex and reconfigurable compartments to carry out functions essential for life. This hierarchical organization serves as an amplifier that allows highly localized, molecular events to propagate into the mesoscale, resulting in dynamic functional properties of biological systems that have not yet been fully realized in synthetic material designs. We pursued a program of research in which we recreated such principles using synthetic liquid crystals (LC) as a versatile platform. We elucidated new hierarchical design strategies that heavily leverage surface anchoring, coexisting phases and interfacial tension gradient to realize equilibrium and non-equilibrium, dynamic mesoscale phenomena in the context of micrometer-sized LC droplets containing multiple compartments. Specifically, we unmasked how equilibrium and dynamical phenomena emerged from hierarchical organizations in reconfigurable, thermotropic liquid crystalline assemblies. The long-term impact of the project is to advance new hierarchical designs of complex soft matter systems in which biomimetic principles, including the propagation of events or information over multiple temporal and spatial scales, are enacted in a facile manner.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. Here, the spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review,we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

42 ENGINEERING↗
DOE OSTI2023

KBase Narrative - SOMATA - Motivating Example

A systems view in the biological context is a useful approach to study complex and potentially interacting systems. In this narrative (accompanying an associated manuscript), we take a systems view of software tools themselves. Instead of using software as a single tool, we demonstrate how users can approach software with the same mindset as the organisms they study; as a complex system that can be optimized based on one’s environment and goals. We demonstrate this idea with an example KBase narrative showing how application parameters in KBase tools can change how a researcher perceives and interacts with bioinformatics tools while conducting a common experimental scenario. In this scenario we are trying to understand how different chemical compounds in a growth media change the metabolic pathways utilized in Escherichia coli.

Cashman, Mikaela↗
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 OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Challenges in cybersecurity: Lessons from biological defense systems

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the ‘self’, are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. In this work we hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Chemical and Morphological Structure of Transgenic Switchgrass Organosolv Lignin Extracted by Ethanol, Tetrahydrofuran, and $\mathcal{γ}$-Valerolactone Pretreatments

We report the recalcitrance of lignocellulosic biomass is a challenge in biological-based biorefinery systems due to the complex physicochemical structure of plant cell walls. Pretreatment and genetic modification are two approaches in biomass conversion that have succeeded in modifying the structure of lignocellulose to enable better enzymatic deconstruction. However, the structural differences among pretreatment-solubilized lignin isolated from switchgrass genotypes have not been extensively investigated. Here, three organosolv pretreatment systems-ethanol (EtOH), tetrahydrofuran (THF), and γ-valerolactone (GVL)-were used on wild-type (WT) and two transgenic switchgrasses. All organosolv pretreatments caused a significant reduction in the molecular mass of lignins; particularly, up to ~90% decrease was observed in EtOH-pretreated lignin compared to untreated lignin. The WT EtOH lignin also presented the smallest particle size among all WT lignins. THF pretreated transgenic lignins showed a higher molecular mass, β-O-4 linkages, and aliphatic hydroxyl content compared to EtOH and GVL pretreated lignin. The number of hydrogen bonds between lignin and the organic solvents calculated from the molecular dynamics simulations followed the same trend as the experimentally determined reduction in lignin molecular mass. The results revealed the structural changes of solubilized lignin isolated from wild-type and transgenic switchgrass after different organosolv pretreatments.

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

Expanding the Scope of Bacterial CRISPR Activation with PAM-Flexible dCas9 Variants

CRISPR-Cas transcriptional tools have been widely applied for programmable regulation of complex biological networks. In comparison to eukaryotic systems, bacterial CRISPR activation (CRISPRa) has stringent target site requirements for effective gene activation. While genes may not always have an NGG protospacer adjacent motif (PAM) at the appropriate position, PAM-flexible dCas9 variants can expand the range of targetable sites. Here we systematically evaluate a panel of PAM-flexible dCas9 variants for their ability to activate bacterial genes. We observe that dxCas9-NG provides a high dynamic range of gene activation for sites with NGN PAMs while dSpRY permits modest activity across almost any PAM. Similar trends were observed for heterologous and endogenous promoters. For all variants tested, improved PAM-flexibility comes with the trade-off that CRISPRi-mediated gene repression becomes less effective. Weaker CRISPR interference (CRISPRi) gene repression can be partially rescued by expressing multiple sgRNAs to target many sites in the gene of interest. Furthermore, our work provides a framework to choose the most effective dCas9 variant for a given set of gene targets, which will further expand the utility of CRISPRa/i gene regulation in bacterial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data

Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging approach. Here, we developed an approach that combines two MSI techniques, matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The known relationships between the two MSI channels of information are enforced via a physically constrained machine-learning approach and directly incorporated in the data processing. We demonstrate the robustness of this method by generating intact molecular MALDI-type spectra and chemical maps at ToF-SIMS resolution when imaging mouse brain thin tissue sections. This approach can be readily adopted for other types of bioimaging where physical relationships between methods have to be considered to boost the confidence in the reconstruction product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Comparison of human poly-N-acetyl-lactosamine synthase structure with GT-A fold glycosyltransferases supports a modular assembly of catalytic subsites

Poly-N-acetyl-lactosamine (poly-LacNAc) structures are composed of repeating [-Galβ(1,4)-GlcNAcβ(1,3)-]n glycan extensions. They are found on both N- and O-glycoproteins and glycolipids and play an important role in development, immune function, and human disease. The majority of mammalian poly-LacNAc is synthesized by the alternating iterative action of β1,3-N-acetylglucosaminyltransferase 2 (B3GNT2) and β1,4-galactosyltransferases. B3GNT2 is in the largest mammalian glycosyltransferase family, GT31, but little is known about the structure, substrate recognition, or catalysis by family members. Here we report the structures of human B3GNT2 in complex with UDP:Mg 2+ and in complex with both UDP:Mg 2+ and a glycan acceptor, lacto-N-neotetraose. The B3GNT2 structure conserves the GT-A fold and the DxD motif that coordinates a Mg 2+ ion for binding the UDP-GlcNAc sugar donor. The acceptor complex shows interactions with only the terminal Galβ(1,4)-GlcNAcβ(1,3)- disaccharide unit, which likely explains the specificity for both N- and O-glycan acceptors. Modeling of the UDP-GlcNAc donor supports a direct displacement inverting catalytic mechanism. Comparative structural analysis indicates that nucleotide sugar donors for GT-A fold glycosyltransferases bind in similar positions and conformations without conserving interacting residues, even for enzymes that use the same donor substrate. In contrast, the B3GNT2 acceptor binding site is consistent with prior models suggesting that the evolution of acceptor specificity involves loops inserted into the stable GT-A fold. These observations support the hypothesis that GT-A fold glycosyltransferases employ coevolving donor, acceptor, and catalytic subsite modules as templates to achieve the complex diversity of glycan linkages in biological systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

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 OSTI2024

Polarized metal–metal multiple bonding and reactivity of phosphinoamide-bridged heterobimetallic group IV/cobalt compounds

Heterobimetallic complexes are studied for their ability to mimic biological systems as well as active sites in heterogeneous catalysts. While specific interest in early/late heterobimetallic systems has fluctuated, they serve as important models to fundamentally understand metal–metal bonding. Specifically, the polarized metal–metal multiple bonds formed in highly reduced early/late heterobimetallic complexes exemplify how each metal modulates the electronic environment and reactivity of the complex as a whole. In this Perspective, we chronicle the development of phosphinoamide-supported group IV/cobalt heterobimetallic complexes. This combination of metals allows access to a low valent Co −I center, which performs a rich variety of bond activation reactions when coupled with the pendent Lewis acidic metal center. Conversely, the low valent late transition metal is also observed to act as an electron reservoir, allowing for redox processes to occur at the d 0 group IV metal site. Most of the bond activation reactions carried out by phosphinoamide-bridged M/Co −I (M = Ti, Zr, Hf) complexes are facilitated by cleavage of metal–metal multiple bonds, which serve as readily accessible electron reservoirs. Comparative studies in which both the number of buttressing ligands as well as the identity of the early metal were varied to give a library of heterobimetallic complexes are summarized, providing a thorough understanding of the reactivity of M/Co −I heterobimetallic systems

Hunter, Nathanael H.↗
DOE OSTI2020

Probing a Silent Metal: A Combined X-ray Absorption and Emission Spectroscopic Study of Biologically Relevant Zinc Complexes

As the second most common transition metal in the human body, Zinc is of great interest in research but has few viable routes for its direct structural study in biological systems. Herein, Zn valence-to-core X-ray emission spectroscopy (VtC XES) and Zn K-edge X-ray absorption spectroscopy (XAS) are presented as a means to understand the local structure of zinc in biological systems through the application of these methods to a series of biologically relevant model complexes. Taken together, the Zn K-edge XAS and VtC XES provide a means to establish the ligand identity, local geometry, and metal-ligand bond lengths. Experimental results were supported by strongly correlated density-functional-theory-based calculations, which, coupled with the complementarity of the two spectroscopic approaches, further enables future applications to protein systems to be examined in a predictive fashion. In this regard, test calculations were performed to showcase the sensitivity of these techniques to realistic biological applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Three-dimensional electronic scaffolds for monitoring and regulation of multifunctional hybrid tissues

Recently, the integration of electronic elements with cellular scaffolds has brought forth the ability to monitor and control tissue function actively by using flexible free-standing two-dimensional (2D) systems. Capabilities for electrically probing complex, physicochemical and biological three-dimensional (3D) microenvironments demand, however, 3D electronic scaffolds with well-controlled geometries and functional-component distributions. This work presents the development of flexible 3D electronic scaffolds with precisely defined dimensions and microelectrode configurations formed using a process that relies on geometric transformation of 2D precursors by compressive buckling. It demonstrates a capability to fabricate these constructs in diverse 3D architectures and/or electrode distributions aimed at achieving an enhanced level of control and regulation of tissue function relatively to that of other approaches. In addition, this work presents the integration of these 3D electronic scaffolds within engineered 3D cardiac tissues, for monitoring of tissue function, controlling tissue contraction through electrical stimulation, and initiating on-demand, local release of drugs, each through well-defined volumetric spaces. These ideas provide opportunities in fields ranging from in vitro drug development to in vivo tissue repair and many others.

3D electronic scaffolds↗