Search NASA⌕ Search

SEARCH · Search NASA

Results for “Network interaction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41

Inferring Organizational Structure from Behavior

In this project, we researched new techniques for detecting hidden networks of individuals and institutions by introducing the use of temporal correlations among behaviors, leveraging both information sources and metadata. We validated the algorithms using the Wikipedia edit history. The rapid increase in crowd-sourced applications like Wikipedia is providing a rich set of data with both a record of behaviors and a set of direct interactions among individuals. Data sets with network ground truth are needed to develop and validate models, before applying them to national security settings where content and meta-data alone are available.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Community detection in sequence similarity networks based on attribute clustering

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs, for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments

59 BASIC BIOLOGICAL SCIENCES↗

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange mediates interactions among microbes, helping explain diversity in microbial communities. As these interactions often involve a fitness cost, it is unclear how stable cooperation can emerge. Here we use genome-scale metabolic models to investigate whether the release of “costless” metabolites (i.e. those that cause no fitness cost to the producer), can be a prominent driver of intermicrobial interactions. By performing over 2 million pairwise growth simulations of 24 species in a combinatorial assortment of environments, we identify a large space of metabolites that can be secreted without cost, thus generating ample cross-feeding opportunities. In addition to providing an atlas of putative interactions, we show that anoxic conditions can promote mutualisms by providing more opportunities for exchange of costless metabolites, resulting in an overrepresentation of stable ecological network motifs. These results may help identify interaction patterns in natural communities and inform the design of synthetic microbial consortia.

59 BASIC BIOLOGICAL SCIENCES↗

Deconditioning and Reconditioning: Humans in Stressful Environments

Deconditioning is an integrated physiological response of the body to a reduction in metabolic rate; that is, to a reduction in energy use or in exercise level. While it may involve assumption of a horizontal body position, it certainly perturbs bodily homeostasis - at least temporarily. The reduction in physical activity that causes deconditioning is often associated with an increase in the time spent, for whatever reason, in a sitting or horizontal position. As a result, orthostatic factors may also contribute to the deconditioning mechanism. The word decondition may be defined as "1: to cause extinction of (a conditioned response) 2: to cause to lose physical fitness". This definition implies that psychological/emotional factors may accompany physical deconditioning, and it is this interpretation of the word that is used throughout this volume. It is apparent that deconditioning plays a major role in the mechanism of the general adaptive (homeostatic) response that is initiated by exposure to prolonged bed rest (BR). And the total homeostatic response to BR involves more than deconditioning per se. For example, it has been shown that the restoration of plasma volume and maximal work capacity after 4 weeks of BR deconditioning left other bodily functions (submaximal exercise oxygen uptake and cardiac output, leg proprioception and posterior leg muscle thickness and volume, head-up tilt tolerance, and sleep quality) functioning at decreased levels. The precise effect of deconditioning on BR homeostasis is difficult to determine, because the fundamental interactive neuro-endocrine-immune control networks that facilitate conditioning and deconditioning also act to maintain basic wholebody homeostasis. For example, is the mechanism of BR-induced deconditioning independent of the mechanism that provokes concomitant orthostatic intolerance; that is, fainting? Assumption of the recumbent body position for prolonged periods of time, results in a new adaptive-homeostatic state. This state occurs in response to the mutually interactive effects of the change in bodily position (hydrostatic pressure), to the virtual elimination of longitudinal pressure on the bones, to the increased confinement with possible reduction in total daily energy (exercise) expenditure, to the reorientation of stimuli within the vestibular organs, and (often) to altered socio-psychological conditions. The exercise-training (reconditioning) syndrome affects total body homeostasis by facilitating increases in work capacity and endurance, whereas deconditioning decreases physical performance. There are many interrelated factors that influence the control parameters that seek to maintain the adaptive conditioning-deconditioning syndrome. These control parameters can be better elucidated by subjecting otherwise healthy ambulatory people to various stresses, such as exercise training and prolonged spaceflight, bed rest, water immersion, hyperbaria, and isolation and confinement. Changes in control parameters will be manifested in muscle function, orthostatic tolerance, cardiorespiratory responses, musculo-skeletal systems, free-radical processes, and body thermoregulation with overarching effects on the subjects' psycho-sociological states. A discussion of these factors and the control parameters constitutes the substance of this volume. Special emphasis is placed on delineating practical applications of the findings that will be of special interest to physicians, nurses, and other health-care workers.

Greenleaf, John E.↗

Assembling supramolecular networks by halogen bonding in coordination polymers driven by 5-bromonicotinic acid

A series of six coordination compounds ([Zn(5-Brnic){sub 2}]·1.5H{sub 2}O){sub n} (1), [Cd(5-Brnic){sub 2}]{sub n} (2), [Co(5-Brnic){sub 2}(H{sub 2}O){sub 2}]{sub n} (3), [Zn(5-Brnic){sub 2}(H{sub 2}biim)]{sub n} (4), ([Cd(5-Brnic){sub 2}(phen)]·H{sub 2}O){sub n} (5), and [Pb(5-Brnic){sub 2}(phen)] (6) have been generated by the hydrothermal method from the metal(II) nitrates, 5-bromonicotinic acid (5-BrnicH), and an optional ancillary 1,10-phenanthroline (phen) or 2,2′-biimidazole (H{sub 2}biim) ligand. All the products 1–6 have been characterized by IR spectroscopy, elemental, thermal, powder and single-crystal X-ray diffraction analyses. Their 5-bromonicotinate-driven structures vary from the 3D metal-organic framework with the seh-3,5-P21/c topology (in 2) and the 2D interdigitated layers with the sql topology (in 1 and 3), to the 1D chains (in 4 and 5) and the 0D discrete monomers (in 6). The 5-bromonicotinate moiety acts as a versatile building block and its tethered bromine atom plays a key role in reinforcing and extending the structures into diverse 3D supramolecular networks via the various halogen bonding Br⋯O, Br⋯Br, and Br⋯π interactions, as well as the N–H⋯O and C–H⋯O hydrogen bonds. The obtained results demonstrate a useful guideline toward engineering the supramolecular architectures in the coordination network assembly under the influence of various halogen bonding interactions. The luminescent (for 1, 2, 4, 5, and 6) and magnetic (for 3) properties have also been studied and discussed in detail. - Graphical abstract: Six coordination compounds driven by 5-bromonicotinic acid have been generated and structurally characterized, revealing diverse metal-organic networks that are further reinforced and extended via various halogen bonding interactions. - Highlights: • 5-Bromonicotinic acid is a versatile ligand for Zn, Cd, Co and Pb derivatives. • Careful selection of co-ligands and metals resulted in different network structures. • Halogen and hydrogen bonding interactions lead to various supramolecular networks. • Luminescent and magnetic properties were studied and discussed in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A biophysical approach to the performance diagnosis of human–building energy interaction: Information (bits) modeling, algorithm, and indicators of energy flow complexity

Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.

54 ENVIRONMENTAL SCIENCES↗

Improvement of the NOvA Near Detector Event Reconstruction and Primary Vertexing through the Application of Machine Learning Methods

The purpose of this work is to examine the application of a deep learning model in event reconstruction of neutrino interactions. The challenges faced in event reconstruction include the placement of an accurate primary neutrino interaction vertex which is used to support the particle track and prong algorithms. The result of accurate primary vertex ensures all particles involved in a neutrino interaction are included. We propose a regression-based Convolutional Neural Network (CNN) method to predict the primary vertex of a particle interaction. We show that with raw two-dimensional pixel map views as input, the regression-based CNN can predict the primary vertex in all three coordinates. This work is applied as part of the NOvA (NuMI Off-axis $\nu_e$ Appearance) near detector reconstruction efforts. The primary vertex predicted by the regression-based CNN model shows promising results for future applications. This deep learning method can be extended to secondary vertexing through a Kernel Density Estimate algorithm discussed in this work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Shear Stress Dependence of Force Networks in 3D Dense Suspensions

The geometric organization and force networks of 3D dense suspensions that exhibit both shear thinning and thickening have been examined as a function of varying strength of interparticle attractive interactions using lubrication flow discrete element simulations. Significant rearrangement of the geometric topology does not occur at either the local or global scale as these systems transition across the shear thinning and shear thickening regimes. In contrast massive rearrangements in the balance of attractive, lubrication, and contact forces are observed with interesting behavior of network growth and competition. In agreement with prior work, in shear thinning regions the attractive force is dominant, however as the shear thickening region is approached there is growth of lubrication forces. Lubrication forces oppose the attraction forces, but as viscosity continues to increase under increasing shear stress, the lubrication forces are dominated by contact forces that also resist attraction. Contact forces are the dominant interactions during shear thickening and are an order of magnitude higher than their values in the shear-thinning regime. At high attractive interaction strength, contact networks can form even under shear thinning conditions, however high shear stress is still required before contact networks become the driving mechanism of shear thickening. Analysis of the contact force network during shear thickening generally indicate a uniformly spreading network that rapidly forms across empty domains, however the growth patterns have structure that are significantly dependent upon the strength of interparticle interactions, indicating subtle variations in the mechanism of shear thickening.

Edens, Lance E.↗

Making social networks more human: A topological approach

A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5 or greater. Furthermore, after we remove the roughly 15 million violator-violator edges from the 1.2-billion-edge Twitter-2010 follower graph, 34% of the violator nodes experience a factor-of-two decrease in PageRank. PageRank is a key component of many graph algorithms such as node/edge ranking and graph sparsification. Thus, this artificial inflation would bias algorithmic output, and result in some incorrect decisions based on this output.

97 MATHEMATICS AND COMPUTING↗

Evaluation of slurry characteristics for rechargeable lithium-ion batteries

Graphical abstract: - Highlights: • Lithium-ion battery slurries are prepared for rechargeable batteries. • The dispersion state of slurry constituents is identified. • Thermal, morphological, rheological, and electrical properties of slurries are analyzed. - Abstract: A multi-component slurry for rechargeable batteries is prepared by dispersing LiCoO{sub 2}, conductive additives, and polymeric binders in a solvent. The physical properties, including rheological, morphological, electrical, and spectroscopic features of battery slurries are investigated. The relationship between the measured physical properties and the internal structure of the slurry is analyzed. It is found that the rheological behavior of the slurry is determined by the interaction of active materials and binding materials (e.g., network structure) and that the dispersion state of conductive additives (e.g., agglomeration) also depends on the binder–carbon interaction.

36 MATERIALS SCIENCE↗

Graph network heterogeneity predicts interplant wake losses

Wind plants generate large-scale wakes, which can affect the performance of neighboring installations. Such wakes are challenging to estimate due to the inherent complexity in modeling wake interactions between large quantities of turbines at various distances. Weighted directed graph networks can inform complex models by linking turbine pairs into chains of upstream and downstream neighbors for a given wind direction. A novel interpretation of the graph network adjacency matrix is proposed where each element of the matrix represents the cumulative impact of upstream turbines on an individual. In this study, wake losses were estimated with an engineering wake model across a range of inflow conditions for nine parametric variations of a system containing two neighboring wind plants. The parametric nature of the study isolates turbine spacing within the plant, separation distance between plants, and wind direction as the main drivers of wake losses. Spatial heterogeneity is computed from the weighted average adjacency matrix of each plant arrangement. The proposed method is orders of magnitude faster than wake modeling and does not require detailed turbine information or atmospheric conditions. Furthermore, the weighted average adjacency matrix provides insight on the spatial organization of wake losses at various scales. Plant heterogeneity is correlated with wake losses within and among plants. Framing wind plant wake interaction in terms of graph network spatial heterogeneity provides an efficient approach for predicting wake losses within and among neighboring wind plants with applications to other complex systems where wake interactions are key factor.

17 WIND ENERGY↗

A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice

Salinity is one of the most common abiotic stresses in agriculture production. Salt tolerance of rice (Oryza sativa) is an important trait controlled by various genes. The mechanism of rice salt tolerance, currently with limited understanding, is of great interest to molecular breeding in improving grain yield. In this study, a gene regulatory network of rice salt tolerance is constructed using a systems biology approach with a number of novel computational methods. We developed an improved volcano plot method in conjunction with a new machine-learning method for gene selection based on gene expression data and applied the method to choose genes related to salt tolerance in rice. The results were then assessed by quantitative trait loci (QTL), co-expression and regulatory binding motif analysis. The selected genes were constructed into a number of network modules based on predicted protein interactions including modules of phosphorylation activity, ubiquity activity, and several proteinase activities such as peroxidase, aspartic proteinase, glucosyltransferase, and flavonol synthase. All of these discovered modules are related to the salt tolerance mechanism of signal transduction, ion pump, abscisic acid mediation, reactive oxygen species scavenging and ion sequestration. We also predicted the three-dimensional structures of some crucial proteins related to the salt tolerance QTL for understanding the roles of these proteins in the network. Our computational study sheds some new light on the mechanism of salt tolerance and provides a systems biology pipeline for studying plant traits in general.

59 BASIC BIOLOGICAL SCIENCES↗

Chaotic dynamics and diffusion in a piecewise linear equation

Genetic interactions are often modeled by logical networks in which time is discrete and all gene activity states update simultaneously. However, there is no synchronizing clock in organisms. An alternative model assumes that the logical network is preserved and plays a key role in driving the dynamics in piecewise nonlinear differential equations. We examine dynamics in a particular 4-dimensional equation of this class. In the equation, two of the variables form a negative feedback loop that drives a second negative feedback loop. By modifying the original equations by eliminating exponential decay, we generate a modified system that is amenable to detailed analysis. In the modified system, we can determine in detail the Poincaré (return) map on a cross section to the flow. By analyzing the eigenvalues of the map for the different trajectories, we are able to show that except for a set of measure 0, the flow must necessarily have an eigenvalue greater than 1 and hence there is sensitive dependence on initial conditions. Further, there is an irregular oscillation whose amplitude is described by a diffusive process that is well-modeled by the Irwin-Hall distribution. There is a large class of other piecewise-linear networks that might be analyzed using similar methods. The analysis gives insight into possible origins of chaotic dynamics in periodically forced dynamical systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model

The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Different dimensional coordination polymers with 4,4'-oxybis(benzoate): Syntheses, structures and properties

Five transition-metal coordination polymers, namely, [Zn{sub 7}Cl{sub 6}(oba){sub 4}]{sub n} (1), [Cd{sub 7}Cl{sub 6}(oba){sub 4}]{sub n} (2), [Zn(oba)(H{sub 2}O)]{sub n} (3), [Ag{sub 2}(oba)]{sub n} (4) and [Co(oba)(H{sub 2}O){sub 2}]{sub n} (5) (H{sub 2}oba=4,4′-oxydibenzoic acid), have been achieved under hydrothermal conditions and structurally characterized by IR, elemental analyses, X-ray single-crystal diffraction and TGA. The X-ray single-crystal diffraction reveals that compounds 1 and 2 are isomorphism, featuring pillared-layer 3D motifs, in which the 2D inorganic layers (Zn{sub 6}Cl{sub 7}){sub n} (or (Cd{sub 6}Cl{sub 7}){sub n}) are connected by oba{sup 2−} pillars. Compound 3 exhibits 1D stair-like chain and extends to a 3D network by two different interchain O–H–O hydrogen bonding interactions while compound 4 features wave chains and stretches to 2D layer by interchain Ag–O weak contacts. Compound 5 shows 2D network in which Co-chains are pillared by oba{sup 2−} ligand and then forms a 3D network by four different O–H–O hydrogen bonding interactions. Furthermore, 1–4 exhibit luminescent properties at a solid state and 5 shows antiferromagnetic behavior. - Graphical abstract: Five new transition-metal coordination complexes 1–5 have been synthesized and characterized by single-crystal X-ray diffractions, IR spectra, elemental analyses, thermogravimetric analyses (TGA), photoluminescent spectra and magnetic measurement. - Highlights: • Compound 1 exhibits a pillared-layer 3D network. • The photoluminescent properties of 1–4 have been measured. • Compound 5 exhibits antiferromagnetic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.

Jeong, Sangwon↗

SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data

The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.

59 BASIC BIOLOGICAL SCIENCES↗

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗