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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗
DOE OSTI2013

Protein Co-Expression Network Analysis (ProCoNA)

Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Proteomic Analysis of Detergent Resistant Membrane Domains during Early Interaction of Macrophages with Rough and Smooth Brucella melitensis

The plasma membrane contains discrete nanometer-sized domains that are resistant to non-ionic detergents, and which are called detergent resistant membrane domains (DRMDs) or lipid rafts. Exposure of host cells to pathogenic bacteria has been shown to induce the re-distribution of specific host proteins between DRMDs and detergent soluble membranes, which leads to the initiation of cell signaling that enable pathogens to access host cells. DRMDs have been shown to play a role in the invasion of Brucella into host macrophages and the formation of replicative phagosomes called Brucella-containing vacuoles (BCVs). In this study we sought to characterize changes to the protein expression profiles in DRMDs and to respective cellular pathways and networks of Mono Mac 6 cells in response to the adherence of rough VTRM1 and smooth 16 M B. melitensis strains. DRMDs were extracted from Mono Mac 6 cells exposed for 2 minutes at 4uC to Brucella (no infection occurs) and from unexposed control cells. Protein expression was determined using the non-gel based quantitative iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) mass spectrometry technique. Using the identified iTRAQ proteins we performed enrichment analyses and probed constructed human biochemical networks for interactions and metabolic reactions. We identified 149 proteins, which either became enriched, depleted or whose amounts did not change in DRMDs upon Brucella exposure. Several of these proteins were distinctly enriched or depleted in DRMDs upon exposure to rough and smooth B. melitensis strains which results in the differential engagement of cellular pathways and networks immediately upon Brucella encounter. For some of the proteins such as myosin 9, small G protein signaling modulator 3, lysine-specific demethylase 5D, erlin-2, and voltage-dependent anion-selective channel protein 2, we observed extreme differential depletion or enrichment in DRMDs. The identified proteins and pathways could provide the basis for novel ways of treating or diagnosing Brucellosis.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
DOE OSTI2017

Triazine containing N-rich microporous organic polymers for CO{sub 2} capture and unprecedented CO{sub 2}/N{sub 2} selectivity

Targeted synthesis of microporous adsorbents for CO{sub 2} capture and storage is very challenging in the context of remediation from green house gases. Herein we report two novel N-rich microporous networks SB-TRZ-CRZ and SB-TRZ-TPA by extensive incorporation of triazine containing tripodal moiety in the porous polymer framework. These materials showed excellent CO{sub 2} storage capacities: SB-TRZ-CRZ displayed the CO{sub 2} uptake capacity of 25.5 wt% upto 1 bar at 273 K and SB-TRZ-TPA gave that of 16 wt% under identical conditions. The substantial dipole quadruple interaction between network (polar triazine) and CO{sub 2} boosts the selectivity for CO{sub 2}/N{sub 2}. SB-TRZ-CRZ has this CO{sub 2}/N{sub 2} selectivity ratio of 377, whereas for SB-TRZ-TPA it was 97. Compared to other porous polymers, these materials are very cost effective, scalable and very promising material for clean energy application and environmental issues. - Graphical abstract: We report two novel N-rich microporous polymeric materials by doping of triazine containing tripodal dopant in the organic framework. These materials showed excellent CO{sub 2} storage capacities as high as 25.5 wt% under 1 bar pressure with exceptional CO{sub 2}/N{sub 2} selectivity of 377. - Highlights: • Triazine containing trimodal moiety incorporated in polycarbazolic and poly triphenylamine networks. • N-rich crosslinked polymers with high BET surface area and 1.5–1.7 nm size large micropores. • CO{sub 2} uptake capacity of 25.5 wt% upto 1 bar at 273 K. • These crosslinked porous polymers showed exceptional CO{sub 2}/N{sub 2} selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2020

Fuel availability not fire weather controls boreal wildfire severity and carbon emissions

Carbon (C) emissions from wildfires are a key terrestrial–atmosphere interaction that influences global atmospheric composi-tion and climate. Positive feedbacks between climate warming and boreal wildfires are predicted based on top-down controls of fire weather and climate, but C emissions from boreal fires may also depend on bottom-up controls of fuel availability related to edaphic controls and overstory tree composition. Here we synthesized data from 417 field sites spanning six ecoregions in the northwestern North American boreal forest and assessed the network of interactions among potential bottom-up and top-down drivers of C emissions. Our results indicate that C emissions are more strongly driven by fuel availability than by fire weather, highlighting the importance of fine-scale drainage conditions, overstory tree species composition and fuel accumulation rates for predicting total C emissions. By implication, climate change-induced modification of fuels needs to be considered for accu-rately predicting future C emissions from boreal wildfires.

X J Walker↗
NASA NTRS2008

Toward Optimal Transport Networks

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

Alexandrov, Natalia↗
DOE OSTI2026

Identification of drug repurposing candidates for amyotrophic lateral sclerosis using electronic health records: a retrospective cohort study

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a life expectancy of only 3–5 years and few approved treatments. To identify drug repurposing candidates for the treatment of ALS, we analysed the electronic health records (EHRs) of a large cohort of military veterans with ALS. We analysed the EHRs of individuals in the US Veterans Health Administration (VHA) database who were diagnosed with ALS between Jan 1, 2009 and Dec 31, 2019 to assess medication effects. Individuals without recorded prescriptions after the date of diagnosis were excluded. Two sets of criteria were applied to ascertain exposure. Exposure criteria A were met if the dispense date or the end date of the medication was within 12 months of ALS diagnosis and the end date was at least 6 months after the dispense date. Exposure criteria B were met if there were at least two dispenses within 6 months before diagnosis and 12 months after diagnosis. Propensity score-matched control groups were generated on the basis of confounders included in the EHR, with methodology of potential outcomes used to infer treatment effects. The primary outcome was death. A standard Cox proportional hazards analysis was done to assess association with survival. Survival was defined as the time from diagnosis date recorded in the EHR to death reported in the Department for Veterans Affairs Vital Status File. Follow-up survival time was censored on Dec 31, 2020, for those alive on this date. Downstream protein targets of drugs with clinically significant effects were analysed using the protein–protein interaction networks-based algorithm PathFX. The EHRs of 11 003 individuals with ALS in the VHA database were appropriate for analysis. 162 medications with treatment groups of 30 or more individuals were identified. Among these 162 medications, 27 were associated with statistically significant changes (≥0·1) in the hazard ratio (HR) for death. 18 of the medications were associated with a reduced HR for death (prolonged survival), and nine were associated with an increased HR for death (reduced survival). Drugs associated with reduced HR included HMG-CoA reductase inhibitors (simvastatin, pravastatin, lovastatin, and atorvastatin), PDE5 inhibitors (vardenafil and sildenafil), and α-adrenergic antagonists (tamsulosin and terazosin). The medications associated with an increased HR were drugs used either in the management of clinical features of ALS associated with poor outcomes or in end-of-life care. PathFx analysis identified a complex of proteins interacting with several of the identified drugs. To our knowledge, this analysis is the largest EHR-based study for identifying drug repurposing candidates for ALS. We identified several drugs that warrant further assessment as therapeutic options in ALS, as well as a protein network complex that might serve as a therapeutic target for ALS.

Reimer, Richard J. [Stanford Univ., CA (United Sta↗
DOE OSTI2024

Synchrotron micro-computed tomography analysis of neutron-irradiated U-Mo fuel

The three-dimensional (3D) microstructure of neutron-irradiated uranium-10 wt.% molybdenum (U-10Mo) fuel with a burn-up of 9.8 × 10 21 fissions/cm 3 was characterized using a novel, multi-modal synchrotron micro-computed tomography approach combining propagation-based phase-contrast enhanced and absorption contrast techniques. The porosity development, porosity interconnectedness, swelling, composition, local thickness of the zirconium (Zr) diffusion barrier, and the influence of the fuel–cladding interaction on the local composition and pore morphology, were uniquely determined in 3D. Two cuboids were produced using a focused ion beam-scanning electron microscope at the Zr diffusion barrier–fuel interface and in the bulk fuel. The bulk fuel sample swelled by 53.3 [+9.7/−3.1]%, while the fuel near the Zr–fuel interface swelled by 63.3 [+14.7/−7.2]%. The average local thickness of the Zr diffusion barrier decreased by 53 %, compared to the expected pre-irradiated thickness. Four pore morphology regions were identified initiating parallel to the fuel–Zr interaction region: (1) an interaction layer of suppressed porosity, (2) a layer of elongated and interconnected porosity, (3) a transition zone of low porosity, and (4) a layer of unoriented porosity representative of the bulk fuel behavior. The increase in porosity near the diffusion barrier corresponded to a higher U concentration compared to that in the bulk fuel. The interconnected porosity in the fuel near the diffusion barrier was extensive and oriented parallel to the diffusion barrier, while the bulk fuel had more compact and isolated pore networks. The interaction layer, despite having suppressed porosity, was nearly 100 wt.% U. Porosity suppression at the diffusion barrier corresponds to the expected reduction in radiation-driven diffusion of Xe at the interface despite the anticipated increase in fission product nucleation originating from a higher U concentration. In conclusion, the novel 3D insights of the porosity, swelling, and compositional variations characterized herein can improve the fidelity of fuel performance codes for proliferation-resistant fuels for research and test reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Hierarchical modeling of molecular energies using a deep neural network

In this work, we introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. Finally, with minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evolution of Pore Structure and Permeability of Rocks Under Hydrothermal Conditions (Final Report)

The physical and transport properties of porous rocks can be altered by a variety of diagenetic, metamorphic, and tectonic processes, and the changes that result are of critical importance to such industrial applications as resource recovery, carbon dioxide sequestration, and waste isolation in geologic formations. The associated interrelationships between rocks, pore fluids and deformation are also key to understanding many natural processes, including dynamic metamorphism, fault mechanics, fault stability and pressure solution creep. In this project, we investigated the changes of permeability and pore geometry owing to inelastic deformation by solution-transfer, brittle fracturing and dislocation creep. In particular, we studied the coupling between pore fluids and deformation in fluid-filled quartz, calcite, mudstones and ultramafic rocks and examine the effects of coupled mechanical and chemical processes on the evolution of porosity and permeability under hydrothermal conditions. The investigations used a combination of techniques, that included triaxial laboratory experiments; uniaxial compressive loading with in situ observations of microstructure; permeability under hydrostatic compression; observations of microstructure using optical and electron microscopes, and micro-CT imaging; and numerical calculations. Laboratory experiments were designed to provide mechanical and transport data under conditions that isolate the particular mechanisms responsible for the changes. The data obtained were used to quantify changes in surface roughness, porosity, pore dimensions and their spatial fluctuations. The results of the experiments and data from image analyses were compared to the results of network, finite-difference and other numerical models to verify the validity of experimentally established relations between permeability and other rock properties. A bibliography for the period from 2015-2019 is appended below. New results obtained in the final year and the unfunded extension period are detailed below.

58 GEOSCIENCES↗
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 OSTI2015

Dynamics in microbial communities: Unraveling mechanisms to identify principles

Diversity begets higher order properties such as functional stability and robustness in microbial communities, but principles that inform conceptual (and eventually predictive) models of community dynamics are lacking. Recent work has shown that selection as well as dispersal and drift shape communities, but the mechanistic bases for assembly of communities and the forces that maintain their function in the face of environmental perturbation are not well understood. Conceptually, some interactions among community members could generate endogenous dynamics in composition, even in the absence of environmental changes. These endogenous dynamics are further perturbed by exogenous forcing factors to produce a richer network of community interactions, and it is this “system” that is the basis for higher order community properties. Elucidation of principles that follow from this conceptual model requires identifying the mechanisms that (a) optimize diversity within a community and (b) impart community stability. The network of interactions between organisms can be an important element by providing a buffer against disturbance beyond the effect of functional redundancy, as alternative pathways with different combinations of microbes can be recruited to fulfill specific functions.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2012

Human Initiated Cascading Failures in Societal Infrastructures

In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

PageRank as a Collective Variable to Study Complex Chemical Transformations and Their Energy Landscapes

A reduced set of reaction coordinates are often employed in chemistry to describe the collective change between reactants and products within the context of rare event theories and in the exploration of energy landscapes. While incredibly powerful, use of these collective variables becomes increasingly challenging as the systems under study become more complex. Recent advancement of new descriptions of collective molecular coordinates have included PageRank (PR) analysis of the network of interactions about molecules and atoms within a system. Herein, PR is developed as a continuous variable that not only captures chemical transformations but can also be employed as a collective variable for a process that could span a highly dimensional energy surface. The derivatives of PR with respect to atomic coordinates are presented, and subsequently combined with forces during a chemical transformation within biased molecular dynamics sampling simulations to create the potential of mean force (PMF). This technique is demonstrated for two reactions: 1) the transformation of tetrahedral [Al(OH)4]-(aq) to octahedral [Al(OH)4(H2O)2]-(aq), and 2) the formation of contact ion pairs (CIPs) and solvent separated ion pairs (SSIPs) of Na+ and OH- in bulk water. Comparison of the behavior of the PR collective variable in both examples reveals that the ability of the PR to resolve local versus extended configurational attributes is tuned by the size of the network employed. This is a unique feature of PageRank that enables control over the resolution of the PMF that is produced.

chemistry, theories, PageRank↗
DOE OSTI2023

The N‐terminal intrinsically disordered region of Ncb5or docks with the cytochrome b 5 core to form a helical motif that is of ancient origin

Abstract NADH cytochromeb 5 oxidoreductase (Ncb5or) is a cytosolic ferric reductase implicated in diabetes and neurological conditions. Ncb5or comprises cytochromeb 5 (b 5 ) and cytochromeb 5 reductase (b 5 R) domains separated by a CHORD‐Sgt1 (CS) linker domain. Ncb5or redox activity depends on proper inter‐domain interactions to mediate electron transfer from NADH or NADPH via FAD to heme. While full‐length human Ncb5or has proven resistant to crystallization, we have succeeded in obtaining high‐resolution atomic structures of theb 5 domain and a construct containing the CS andb 5 R domains (CS/b 5 R). Ncb5or also contains an N‐terminal intrinsically disordered region of 50 residues that has no homologs in other protein families in animals but features a distinctive, conserved L 34 MDWIRL 40 motif also present in reduced lateral root formation (RLF) protein in rice and increased recombination center 21 in baker's yeast, all attaching to ab 5 domain. After unsuccessful attempts at crystallizing a human Ncb5or construct comprising the N‐terminal region naturally fused to theb 5 domain, we were able to obtain a high‐resolution atomic structure of a recombinant rice RLF construct corresponding to residues 25–129 of human Ncb5or (52% sequence identity; 74% similarity). The structure reveals Trp 120 (corresponding to invariant Trp 37 in Ncb5or) to be part of an 11‐residue α‐helix (S 116 QMDWLKLTRT 126 ) packing against two of the four helices in theb 5 domain that surround heme (α2 and α5). The Trp 120 side chain forms a network of interactions with the side chains of four highly conserved residues corresponding to Tyr 85 and Tyr 88 (α2), Cys 124 (α5), and Leu 47 in Ncb5or. Circular dichroism measurements of human Ncb5or fragments further support a key role of Trp 37 in nucleating the formation of the N‐terminal helix, whose location in the N/b 5 module suggests a role in regulating the function of this multi‐domain redox enzyme. This study revealed for the first time an ancient origin of a helical motif in the N/b 5 module as reflected by its existence in a class of cytochromeb 5 proteins from three kingdoms among eukaryotes.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Transcriptomic Network Analysis of Cyanobacterial-Methylotroph Interactions in Coculture and Axenic Conditions

A previous study demonstrated the potential for Cyanobacterial-Methylotroph cocultures to facilitate biogas processing as well as to be used in other biotechnological applications. To advance this technology, we investigated potential interactions between Cyanobacterium stanieri HL-69 (HL69) and Methylotuvimicrobium alkaliphilum 20Z (20Z) by inferring and analyzing gene co-expression networks under co-culture and axenic conditions. Five different co-expression networks were examined. These networks were inferred using gene expression profiles for 20Z axenic condition, HL-69 axenic, HL-69, 20Z coculture, HL-69 coculture, and cross-species HL-69-20Z coculture. Through the analysis of node (gene) betweenness and node normalized degree values in all five network cases, we compared adjustments in gene expression between growth conditions (axenic vs co-culture) as well as identify biological functions relevant to interspecies interactions. This analysis was done to distinguish between gene interactions within an organism and gene interactions between two organisms. Moreover, for all five cases we investigated two different network cutoff levels of 3,000 and 10,000. By shedding light on inter- and intra- species interactions, we hope to gain a better understanding of how these two organisms interact. This research will allow the investigation of further biotechnological applications of coculture systems and optimization of such applications for biotechnological purposes.

59 BASIC BIOLOGICAL SCIENCES↗