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At least 55 records · Page 3

GRAPEVINE: Grids about anything by Poisson's equation in a visually interactive networking environment

A proven 3-D multiple-block elliptic grid generator, designed to run in 'batch mode' on a supercomputer, is improved by the creation of a modern graphical user interface (GUI) running on a workstation. The two parts are connected in real time by a network. The resultant system offers a significant speedup in the process of preparing and formatting input data and the ability to watch the grid solution converge by replotting the grid at each iteration step. The result is a reduction in user time and CPU time required to generate the grid and an enhanced understanding of the elliptic solution process. This software system, called GRAPEVINE, is described, and certain observations are made concerning the creation of such software.

Sorenson, Reese L.↗

Incorporating seed fate into plant–frugivore networks increases interaction diversity across plant regeneration stages

Plant–animal mutualistic interactions, such as pollination and seed dispersal, affect ecosystem functioning by driving plant population dynamics. However, little is known of how the diversity of interactions in these mutualistic networks determines plant regeneration dynamics. To fill this gap, interaction networks should not only account for the number of seeds dispersed by animals, but also for seed fate after dispersal. Here, we compare plant–animal networks at both the seed dispersal and seedling recruitment stage to evaluate how interaction diversity, represented by different network metrics, changes throughout the process of plant regeneration. We focused on a system with six species of frugivorous birds and three species of fleshy‐fruited trees in the temperate secondary forest of the Cantabrian Range (northern Iberian Peninsula). We considered two plant cohorts corresponding to two fruiting years showing strong differences in fruit and frugivore abundance. Seed dispersal interactions were estimated from a spatially‐explicit, field‐validated model predicting tree and bird species‐specific seed deposition in different microhabitats. These interactions were further transformed into interactions at the seedling recruitment stage by accounting for plant‐ and microhabitat‐specific seed fates estimated from field sampling. We found that network interaction diversity varied across plant regeneration stages and cohorts, both in terms of the evenness and the number of paired interactions. Tree–bird interactions were more evenly distributed across species pairs at the recruitment stage than at the seed deposition stage, although some interactions disappeared in the seed‐to‐seedling transition for one plant cohort. The variations in interaction diversity were explained by between‐plant differences in post‐dispersal seed fate and in inter‐annual fruit production, rather than by differences between frugivores in seed deposition patterns. These results highlight the need for integrating plant traits and disperser quality to predict the functional outcome of plant–animal mutualistic networks.

Donoso, Isabel↗

Explorers of the Universe: Interactive Electronic Network

This paper details how the Interactive Electronic Network is being utilized by secondary and postsecondary students, and their teachers and professors, to facilitate learning and understanding. The Interactive Electronic Network is couched within the Explorers of the Universe web site in a restricted portion entitled Gateway.

Alvarez, Marino C.↗

NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families

Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability.

42 ENGINEERING↗

Nutrient cycling for biomass: Interactive proteomic/transcriptomic networks for global carbon management processes within poplar-mycorrhizal interactions

This project addresses the need to develop system-scale models at the symbiotic interface between ectomycorrhizal fungi (Laccaria bicolor) and tree species (Populus tremuloides) in response to environmental nutrient availability / biochemistry. Using our now well-established laboratory Laccaria x poplar system, we address the hypothesis that essential regulatory and metabolic mechanisms can be inferred from genomic, transcriptomic and proteomic-level changes that occur in response to environmental nutrient availability. The project addresses this hypothesis by applying state-of-the-art protein-level analytic approaches to fill the gap in our understanding of how mycorrhizal regulatory and metabolic processes at the transcript-level translate to nutrient uptake, carbon management and ultimate net primary productivity of plants. In most cases, these techniques were not previously optimized for poplar trees or Laccaria. Thus, one of the major contributions of this project has been to provide avenues for new research in these species by overcoming the pitfalls that had previously prevented the use of techniques such as ChIP-Seq and SWATH-proteomics. Since it is the proteins that sense and interact with the environment, participate in signal cascades, activate and regulate gene expression, perform the activities of metabolism and ultimately sequester carbon and generate biomass, an understanding of protein activities during symbiosis-linked nutrient uptake is critical to any systems-level approach that links metabolic processes to the environment. This project uses a team of experts at The University of Alabama in Huntsville (UAH), The University of Alabama at Birmingham (UAB) and Argonne National Laboratory (ANL) to address the above hypothesis using a multiple "omics" approach that combines gene and protein expression as well as protein modifications, and biochemical analyses (performed at Brookhaven National Laboratory (BNL)) in poplar trees under mycorrhizal and free living conditions. Together, the assembled team of experts completed all of the planned milestones set forth in this project. In addition to the planned approaches, several lines of exciting new research have also evolved during the course of this project that involved FTIR Imaging using the National Synchrotron Light Source at BNL. A summary of the approaches used in this project and key highlights are as follows: Having the right combination of microbes associated with plants is largely responsible for the plant’s ability to mine nutrients from the soil and to develop a strong “immune system”. Our current chemically focused and intensive culture tends to forget that plants obtain nutrients in two ways: (1) via water soluble chemical nutrients and (2) via the activity of acquired microbial symbionts. In healthy natural ecosystems, chemical nutrients are always in low abundance because the organisms within that system have already locked such nutrients away within the biological system itself. Thus, in nature it is the biological sources of nutrients and the microbes that have the capacity to mine those nutrients for their plant hosts that actually control the terrestrial nutrient cycles on this planet. Thus, a new push in the future may very well be to use our skills at elucidating complex patterns to strategically guide soil microbe communities to do what we want, essentially allowing nature to do the work of figuring out what is most efficient and effective for human needs. However, the findings of this project and other work in our lab lead to the hypothesis that the specific soil community composition is less important than the emergent properties of those communities. So, additional research into what soil communities are effective and how they are established will be key in developing human understanding of how to manipulate biological systems to meet human needs without causing undue damage to our environment.

59 BASIC BIOLOGICAL SCIENCES↗

Broad substrate-specific phosphorylation events are associated with the initial stage of plant cell wall recognition in Neurospora crassa

Fungal plant cell wall degradation processes are governed by complex regulatory mechanisms, allowing the organisms to adapt their metabolic program with high specificity to the substrate at hand. While the uptake of representative plant cell wall mono- and disaccharides is known to induce specific transcriptional and translational responses, the processes related to early signal reception and transduction are still enigmatic. A fast and reversible way of transmitting extracellular signals throughout the cell are protein phosphorylations, which could initiate adaptations of the fungal metabolism on a post-translational level. In an effort to elucidate how changes in the initial substrate recognition phase of Neurospora crassa affect the global phosphorylation pattern, phosphoproteomics was performed after a short (2 minute) induction period with several plant cell wall-related mono- and disaccharides. The MS/MS-based peptide analysis revealed a large number of proteins that are phosphorylated or de-phosphorylated in a substrate-specific manner. A protein-protein-interaction network was constructed based on the proteins identified by MS/MS. Using this network, putative interaction partners of several phosphorylated proteins of interest were determined. This led to the discovery of substrate-specific protein-interactions that might be involved in mediating the substrate recognition of N. crassa. Besides a high number of kinases, phosphatases and transcription factors to be affected by differential phosphorylation, our data demonstrate, for example, that the cAMP signaling pathway is heavily modified by substrate-specific phosphorylations, including the key component adenylate cyclase CR-1 as well as several G-protein-coupled receptors. In addition, four differentially phosphorylated F-Box proteins were identified in the phospho-proteome. When physiologically characterized using the respective gene deletion strains, Fbx-19 and Fbx-22 were found to be involved in carbon catabolite repression responses. Overall, these results improve our understanding of how fungi respond to environmental cues and provide novel insights into the mechanisms of sensory perception and signal transduction during lignocellulose degradation.

59 BASIC BIOLOGICAL SCIENCES↗

GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

We present the Galaxy Assembly and Interaction Neural Networks (Gainn), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 < z < 15. The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies. The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M). We compare the performance of the neural networks using different combinations of these filters, as well as different activation functions and numbers of layers. The best neural network predicted redshift with a normalized rms error of $0.010^{+0.003}_{-0.001}$, stellar mass with rms = $0.089^{+0.044}_{-0.022}$, halo mass with a mean-squared error of $0.022^{+0.014}_{-0.008}$, and mass-weighted age with rms = $12.466^{+5.065}_{-2.408}$. We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias $\langle$Δz$\rangle$ < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.

97 MATHEMATICS AND COMPUTING↗

MetConSIN

Metabolic Context Species Interaction Networks (MetConSIN) generates microbe-metabolte and metabolite-metabolite interaction networks using constraint based metabolic modeling. To do this, MetConSIN simulates with the dynamic FBA system and saves the sequence of interaction networks implied by that system, as well as the time-average network.

Brunner, James↗

Toward a Better Understanding of Species Interactions through Network Biology

Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance. These developments prompt two important questions. What can be done with network approaches to better understand microbial species interactions? What challenges remain in applying network approaches to query the more complex systems of natural settings? Here, I briefly describe what has been done and what questions still need to be answered. Over the next 5 to 10 years, we will be in a strong position to infer networks that contain multiple kinds of omic data and describe systems with multiple species. These applications will open the door for a better understanding and use of microbiomes across a variety of fields.

Microbiology↗

Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation: Competition in bioremediation system

When trying to discern network interactions among different species/populations in microbial communities interests have been evoked in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. We modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140–269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. In particular, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.

59 BASIC BIOLOGICAL SCIENCES↗

Microbial Community Structure and Ecological Networks during Simulation of Diatom Sinking

Microbial-mediated utilization of particulate organic matter (POM) during its downward transport from the surface to the deep ocean constitutes a critical component of the global ocean carbon cycle. However, it remains unclear as to how high hydrostatic pressure (HHP) and low temperature (LT) with the sinking particles affects community structure and network interactions of the particle-attached microorganisms (PAM) and those free-living microorganisms (FLM) in the surrounding water. In this study, we investigated microbial succession and network interactions in experiments simulating POM sinking in the ocean. Diatom-derived 13C- and 12C-labeled POM were used to incubate surface water microbial communities from the East China Sea (ECS) under pressure (temperature) of 0.1 (25 °C), 20 (4 °C), and 40 (4 °C) MPa (megapascal). Our results show that the diversity and species richness of the PAM and FLM communities decreased significantly with HHP and LT. Microbial community analysis indicated an increase in the relative abundance of Bacteroidetes at high pressure (40 MPa), mostly at the expense of Gammaproteobacteria, Alphaproteobacteria, and Gracilibacteria at atmospheric pressure. Hydrostatic pressure and temperature affected lifestyle preferences between particle-attached (PA) and free-living (FL) microbes. Ecological network analysis showed that HHP and LT enhanced microbial network interactions and resulted in higher vulnerability to networks of the PAM communities and more resilience of those of the FLM communities. Most interestingly, the PAM communities occupied most of the module hubs of the networks, whereas the FLM communities mainly served as connectors of the modules, suggesting their different ecological roles of the two groups of microbes. These results provided novel insights into how HHP and LT affected microbial community dynamics, ecological networks during POM sinking, and the implications for carbon cycling in the ocean.

59 BASIC BIOLOGICAL SCIENCES↗

Planting increases the abundance and structure complexity of soil core functional genes relevant to carbon and nitrogen cycling

Plants have an important impact on soil microbial communities and their functions. However, how plants determine the microbial composition and network interactions is still poorly understood. During a four-year field experiment, we investigated the functional gene composition of three types of soils (Phaeozem, Cambisols and Acrisol) under maize planting and bare fallow regimes located in cold temperate, warm temperate and subtropical regions, respectively. The core genes were identified using high-throughput functional gene microarray (GeoChip 3.0) and functional molecular ecological networks (fMENs) were subsequently developed with the random matrix theory (RMT)-based conceptual framework. Our results demonstrated that planting significantly (P < 0.05) increased the gene alpha-diversity in terms of richness and Shannon – Simpson’s indexes for all three types of soils and 83.5% of microbial alpha-diversity can be explained by the plant factor. Moreover, planting had significant impacts on the microbial community structure and the network interactions of the microbial communities. The calculated network complexity was higher under maize planting than under bare fallow regimes. The increase of the functional genes led to an increase in both soil respiration and nitrification potential with maize planting, indicating that changes in the soil microbial communities and network interactions influenced ecological functioning.

60 APPLIED LIFE SCIENCES↗

Tools and Models for Integrating Multiple Cellular Networks

In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed CRIT for correlation analysis in systems biology [5]. For Aim 3, we have further investigated the scaling relationship that the number of Transcription Factors (TFs) in a genome is proportional to the square of the total number of genes. We have extended the analysis from transcription factors to various classes of functional categories, and from individual categories to joint distribution [6]. By introducing a new analytical framework, we have generalized the original toolbox model to take into account of metabolic network with arbitrary network topology [7].

09 BIOMASS FUELS↗

Tackling the Challenges in Scene Graph Generation With Local-to-Global Interactions

In this work, we seek new insights into the underlying challenges of the scene graph generation (SGG) task. Quantitative and qualitative analysis of the visual genome (VG) dataset implies: 1) ambiguity: even if interobject relationship contains the same object (or predicate), they may not be visually or semantically similar; 2) asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies; and 3) higher-order contexts: leveraging the identities of certain graph elements can help generate accurate scene graphs. Motivated by the analysis, we design a novel SGG framework, Local-to-global interaction networks (LOGINs). Locally, interactions extract the essence between three instances of subject, object, and background, while baking direction awareness into the network by explicitly constraining the input order of subject and object. Globally, interactions encode the contexts between every graph component (i.e., nodes and edges). Finally, Attract and Repel loss is utilized to fine-tune the distribution of predicate embeddings. By design, our framework enables predicting the scene graph in a bottom-up manner, leveraging the possible complementariness. To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed. Overall, experimental results demonstrate that LOGIN can successfully distinguish relational direction than existing methods (in BRC task), while showing state-of-the-art results on the VG benchmark (in SGG task).

97 MATHEMATICS AND COMPUTING↗

Metal-hydrogen-pi-bonded organic frameworks

We report the synthesis and characterization of a new series of permanently porous, three-dimensional metal–organic frameworks (MOFs), M-HAF-2 (M = Fe, Ga, or In), constructed from tetratopic, hydroxamate-based, chelating linkers. Here, the structure of M-HAF-2 was determined by three-dimensional electron diffraction (3D ED), revealing a unique interpenetrated hcb-a net topology. This unusual topology is enabled by the presence of free hydroxamic acid groups, which lead to the formation of a diverse network of cooperative interactions comprising metal–hydroxamate coordination interactions at single metal nodes, staggered π–π interactions between linkers, and H-bonding interactions between metal-coordinated and free hydroxamate groups. Such extensive, multimodal interconnectivity is reminiscent of the complex, noncovalent interaction networks of proteins and endows M-HAF-2 frameworks with high thermal and chemical stability and allows them to readily undergo postsynthetic metal ion exchange (PSE) between trivalent metal ions. We demonstrate that M-HAF-2 can serve as versatile porous materials for ionic separations, aided by one-dimensional channels lined by continuously π-stacked aromatic groups and H-bonding hydroxamate functionalities. As an addition to the small group of hydroxamic acid-based MOFs, M-HAF-2 represents a structural merger between MOFs and hydrogen-bonded organic frameworks (HOFs) and illustrates the utility of non-canonical metal-coordinating functionalities in the discovery of new bonding and topological patterns in reticular materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modes of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.

59 BASIC BIOLOGICAL SCIENCES↗

Molnets: An Artificial Chemistry Based on Neural Networks

The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

Colombano, Silvano↗