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Sample records for molecular interaction networks

  1. Topology of molecular interaction networks.

    PubMed

    Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

  2. Topology of molecular interaction networks

    PubMed Central

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

  3. WebInterViewer: visualizing and analyzing molecular interaction networks

    PubMed Central

    Han, Kyungsook; Ju, Byong-Hyon; Jung, Haemoon

    2004-01-01

    Molecular interaction networks, such as those involving protein–protein and protein–DNA interactions, often consist of thousands of nodes or even more, which severely limits the usefulness of many graph drawing tools because they become too slow for interactive analysis of the networks and because they produce cluttered drawings with many edge crossings. We present a new, fast-layout algorithm and its implementation called WebInterViewer for visualizing large-scale molecular interaction networks. WebInterViewer (i) finds a layout of the connected components of an entire network, (ii) finds a global layout of nodes with respect to pivot nodes within the connected components and (iii) refines the local layout of each connected component by first relocating midnodes with respect to their cutvertices and the direct neighbors of the cutvertices, and then relocating all nodes with respect to their neighbors within distance 2. The advantages of WebInterViewer over classical graph drawing methods include the facts that (i) it is an order of magnitude faster, (ii) it can visualize data directly from protein interaction databases and (iii) it provides several abstraction and comparison operations for analyzing large-scale biological networks effectively. WebInterViewer is accessible at http://interviewer.inha.ac.kr/. PMID:15215357

  4. Topology and static response of interaction networks in molecular biology.

    PubMed

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2006-02-22

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace-Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason-Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  5. Topology and static response of interaction networks in molecular biology

    PubMed Central

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2005-01-01

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace–Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason–Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  6. Protozoan HSP90-heterocomplex: molecular interaction network and biological significance.

    PubMed

    Figueras, Maria J; Echeverria, Pablo C; Angel, Sergio O

    2014-05-01

    The HSP90 chaperone is a highly conserved protein from bacteria to higher eukaryotes. In eukaryotes, this chaperone participates in different large complexes, such as the HSP90 heterocomplex, which has important biological roles in cell homeostasis and differentiation. The HSP90-heterocomplex is also named the HSP90/HSP70 cycle because different co-chaperones (HIP, HSP40, HOP, p23, AHA1, immunophilins, PP5) participate in this complex by assembling sequentially, from the early to the mature complex. In this review, we analyze the conservation and relevance of HSP90 and the HSP90-heterocomplex in several protozoan parasites, with emphasis in Plasmodium spp., Toxoplasma spp., Leishmania spp. and Trypanosoma spp. In the last years, there has been an outburst of studies based on yeast two-hybrid methodology, co-immunoprecipitation-mass spectrometry and bioinformatics, which have generated a most comprehensive protein-protein interaction (PPI) network of HSP90 and its co-chaperones. This review analyzes the existing PPI networks of HSP90 and its co-chaperones of some protozoan parasites and discusses the usefulness of these powerful tools to analyze the biological role of the HSP90-heterocomplex in these parasites. The generation of a T. gondii HSP90 heterocomplex PPI network based on experimental data and a recent Plasmodium HSP90 heterocomplex PPI network are also included and discussed. As an example, the putative implication of nuclear transport and chromatin (histones and Sir2) as HSP90-heterocomplex interactors is here discussed. PMID:24694366

  7. DyNet: visualization and analysis of dynamic molecular interaction networks

    PubMed Central

    Goenawan, Ivan H.; Lynn, David J.

    2016-01-01

    Summary: The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most ‘rewired’ nodes across many network states. Availability and Implementation: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). Contact: david.lynn@sahmri.com. Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:27153624

  8. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

    PubMed

    Latysheva, Natasha S; Oates, Matt E; Maddox, Louis; Flock, Tilman; Gough, Julian; Buljan, Marija; Weatheritt, Robert J; Babu, M Madan

    2016-08-18

    Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer. PMID:27540857

  9. Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell.

    PubMed

    De Las Rivas, Javier; Fontanillo, Celia

    2012-11-01

    Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics. PMID:22908212

  10. From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular network.

    PubMed

    Yuan, Ruoshi; Zhu, Xiaomei; Radich, Jerald P; Ao, Ping

    2016-01-01

    Acute promyelocytic leukemia (APL) remains the best example of a malignancy that can be cured clinically by differentiation therapy. We demonstrate that APL may emerge from a dynamical endogenous molecular-cellular network obtained from normal, non-cancerous molecular interactions such as signal transduction and translational regulation under physiological conditions. This unifying framework, which reproduces APL, normal progenitor, and differentiated granulocytic phenotypes as different robust states from the network dynamics, has the advantage to study transition between these states, i.e. critical drivers for leukemogenesis and targets for differentiation. The simulation results quantitatively reproduce microarray profiles of NB4 and HL60 cell lines in response to treatment and normal neutrophil differentiation, and lead to new findings such as biomarkers for APL and additional molecular targets for arsenic trioxide therapy. The modeling shows APL and normal states mutually suppress each other, both in "wiring" and in dynamical cooperation. Leukemogenesis and recovery under treatment may be a consequence of spontaneous or induced transitions between robust states, through "passes" or "dragging" by drug effects. Our approach rationalizes leukemic complexity and constructs a platform towards extending differentiation therapy by performing "dry" molecular biology experiments. PMID:27098097

  11. From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular network

    PubMed Central

    Yuan, Ruoshi; Zhu, Xiaomei; Radich, Jerald P.; Ao, Ping

    2016-01-01

    Acute promyelocytic leukemia (APL) remains the best example of a malignancy that can be cured clinically by differentiation therapy. We demonstrate that APL may emerge from a dynamical endogenous molecular-cellular network obtained from normal, non-cancerous molecular interactions such as signal transduction and translational regulation under physiological conditions. This unifying framework, which reproduces APL, normal progenitor, and differentiated granulocytic phenotypes as different robust states from the network dynamics, has the advantage to study transition between these states, i.e. critical drivers for leukemogenesis and targets for differentiation. The simulation results quantitatively reproduce microarray profiles of NB4 and HL60 cell lines in response to treatment and normal neutrophil differentiation, and lead to new findings such as biomarkers for APL and additional molecular targets for arsenic trioxide therapy. The modeling shows APL and normal states mutually suppress each other, both in “wiring” and in dynamical cooperation. Leukemogenesis and recovery under treatment may be a consequence of spontaneous or induced transitions between robust states, through “passes” or “dragging” by drug effects. Our approach rationalizes leukemic complexity and constructs a platform towards extending differentiation therapy by performing “dry” molecular biology experiments. PMID:27098097

  12. Precritical State Transition Dynamics in the Attractor Landscape of a Molecular Interaction Network Underlying Colorectal Tumorigenesis

    PubMed Central

    Cho, Kwang-Hyun

    2015-01-01

    From the perspective of systems science, tumorigenesis can be hypothesized as a critical transition (an abrupt shift from one state to another) between proliferative and apoptotic attractors on the state space of a molecular interaction network, for which an attractor is defined as a stable state to which all initial states ultimately converge, and the region of convergence is called the basin of attraction. Before the critical transition, a cellular state might transit between the basin of attraction for an apoptotic attractor and that for a proliferative attractor due to the noise induced by the inherent stochasticity in molecular interactions. Such a flickering state transition (state transition between the basins of attraction for alternative attractors from the impact of noise) would become more frequent as the cellular state approaches near the boundary of the basin of attraction, which can increase the variation in the estimate of the respective basin size. To investigate this for colorectal tumorigenesis, we have constructed a stochastic Boolean network model of the molecular interaction network that contains an important set of proteins known to be involved in cancer. In particular, we considered 100 representative sequences of 20 gene mutations that drive colorectal tumorigenesis. We investigated the appearance of cancerous cells by examining the basin size of apoptotic, quiescent, and proliferative attractors along with the sequential accumulation of gene mutations during colorectal tumorigenesis. We introduced a measure to detect the flickering state transition as the variation in the estimate of the basin sizes for three-phenotype attractors from the impact of noise. Interestingly, we found that this measure abruptly increases before a cell becomes cancerous during colorectal tumorigenesis in most of the gene mutation sequences under a certain level of stochastic noise. This suggests that a frequent flickering state transition can be a precritical

  13. Functional Molecular Ecological Networks

    PubMed Central

    Zhou, Jizhong; Deng, Ye; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-01-01

    Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO2 and ambient CO2 (aCO2) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO2 and aCO2, at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. PMID:20941329

  14. Functional molecular ecological networks.

    PubMed

    Zhou, Jizhong; Deng, Ye; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-01-01

    Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on "species" richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO(2) enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO(2) and ambient CO(2) (aCO(2)) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO(2) and aCO(2), at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO(2) dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. PMID:20941329

  15. Molecular ecological network analyses

    PubMed Central

    2012-01-01

    Background Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP

  16. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

    PubMed

    Kohn, Kurt W; Zeeberg, Barry M; Reinhold, William C; Pommier, Yves

    2014-01-01

    Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1); interactions at adherens junctions (CDH1, ADAP1, CAMSAP3); interactions at desmosomes (PPL, PKP3, JUP); transcription regulation of cell-cell junction complexes (GRHL1 and 2); epithelial RNA splicing regulators (ESRP1 and 2); epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B); epithelial Ca(+2) signaling (ATP2C2, S100A14, BSPRY); terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2); maintenance of apico-basal polarity (RAB25, LLGL2, EPN3). The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets. PMID:24940735

  17. Energy level realignment in weakly interacting donor-acceptor binary molecular networks.

    PubMed

    Zhong, Jian-Qiang; Qin, Xinming; Zhang, Jia-Lin; Kera, Satoshi; Ueno, Nobuo; Wee, Andrew Thye Shen; Yang, Jinlong; Chen, Wei

    2014-02-25

    Understanding the effect of intermolecular and molecule-substrate interactions on molecular electronic states is key to revealing the energy level alignment mechanism at organic-organic heterojunctions or organic-inorganic interfaces. In this paper, we investigate the energy level alignment mechanism in weakly interacting donor-acceptor binary molecular superstructures, comprising copper hexadecafluorophthalocyanine (F16CuPc) intermixed with copper phthalocyanine (CuPc), or manganese phthalocynine (MnPc) on graphite. The molecular electronic structures have been systematically studied by in situ ultraviolet photoelectron spectroscopy (UPS) and low-temperature scanning tunneling microscopy/spectroscopy (LT-STM/STS) experiments and corroborated by density functional theory (DFT) calculations. As demonstrated by the UPS and LT-STM/STS measurements, the observed unusual energy level realignment (i.e., a large downward shift in donor HOMO level and a corresponding small upward shift in acceptor HOMO level) in the CuPc-F16CuPc binary superstructures originates from the balance between intermolecular and molecule-substrate interactions. The enhanced intermolecular interactions through the hydrogen bonding between neighboring CuPc and F16CuPc can stabilize the binary superstructures and modify the local molecular electronic states. The obvious molecular energy level shift was explained by gap-state-mediated interfacial charge transfer. PMID:24433044

  18. Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events

    PubMed Central

    2015-01-01

    Background Biomedical studies need assistance from automated tools and easily accessible data to address the problem of the rapidly accumulating literature. Text-mining tools and curated databases have been developed to address such needs and they can be applied to improve the understanding of molecular pathogenesis of complex diseases like thyroid cancer. Results We have developed a system, PWTEES, which extracts pathway interactions from the literature utilizing an existing event extraction tool (TEES) and pathway named entity recognition (PathNER). We then applied the system on a thyroid cancer corpus and systematically extracted molecular interactions involving either genes or pathways. With the extracted information, we constructed a molecular interaction network taking genes and pathways as nodes. Using curated pathway information and network topological analyses, we highlight key genes and pathways involved in thyroid carcinogenesis. Conclusions Mining events involving genes and pathways from the literature and integrating curated pathway knowledge can help improve the understanding of molecular interactions of complex diseases. The system developed for this study can be applied in studies other than thyroid cancer. The source code is freely available online at https://github.com/chengkun-wu/PWTEES. PMID:26679379

  19. Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

    PubMed Central

    Li, Jiao; Zhu, Xiaoyan; Chen, Jake Yue

    2009-01-01

    The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for

  20. Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts.

    PubMed

    Li, Jiao; Zhu, Xiaoyan; Chen, Jake Yue

    2009-07-01

    The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for

  1. Influence of macromer molecular weight and chemistry on poly(beta-amino ester) network properties and initial cell interactions.

    PubMed

    Brey, Darren M; Erickson, Isaac; Burdick, Jason A

    2008-06-01

    A library of photocrosslinkable poly(beta-amino ester)s (PBAEs) was recently synthesized to expand the number of degradable polymers that can be screened and developed for a variety of biological applications. In this work, the influence of variations in macromer chemistry and macromer molecular weight (MMW) on network reaction behavior, overall bulk properties, and cell interactions were investigated. The MMW was controlled through alterations in the initial diacrylate to amine ratio (> or =1) during synthesis and decreased with an increase in this ratio. Lower MMWs reacted more quickly and to higher double bond conversions than higher MMWs, potentially due to the higher concentration of reactive groups. Additionally, the lower MMWs led to networks with higher compressive and tensile moduli that degraded slower than networks formed from higher MMWs because of an increase in the crosslinking density and decrease in the number of degradable units per crosslink. The adhesion and spreading of osteoblast-like cells on polymer films was found to be dependent on both the macromer chemistry and the MMW. In general, the number of cells was similar on networks formed from a range of MMWs, but the spreading was dramatically influenced by MMW (higher spreading with lower MMWs). These results illustrate further diversity in photocrosslinkable PBAE properties and that the chemistry and macromer structure must be carefully selected for the desired application. PMID:17896761

  2. A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host-Pathogen Interaction Networks.

    PubMed

    Wu, Chia-Chou; Chen, Bor-Sen

    2016-01-01

    Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host-pathogen dynamic interaction networks. The consideration of host-pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host-pathogen molecular interaction networks, and consequent inferences of the host-pathogen relationship could be translated into biomedical applications. PMID:26881892

  3. A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host–Pathogen Interaction Networks

    PubMed Central

    Wu, Chia-Chou; Chen, Bor-Sen

    2016-01-01

    Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host–pathogen dynamic interaction networks. The consideration of host–pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host–pathogen molecular interaction networks, and consequent inferences of the host–pathogen relationship could be translated into biomedical applications. PMID:26881892

  4. An Interaction Network Predicted from Public Data as a Discovery Tool: Application to the Hsp90 Molecular Chaperone Machine

    PubMed Central

    Echeverría, Pablo C.; Bernthaler, Andreas; Dupuis, Pierre; Mayer, Bernd; Picard, Didier

    2011-01-01

    Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community. PMID:22022502

  5. Identifying Gene Interaction Networks

    PubMed Central

    Bebek, Gurkan

    2016-01-01

    In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network’s topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources. PMID:22307715

  6. Atomic & Molecular Interactions

    SciTech Connect

    2002-07-12

    The Gordon Research Conference (GRC) on Atomic & Molecular Interactions was held at Roger Williams University, Bristol, RI. Emphasis was placed on current unpublished research and discussion of the future target areas in this field.

  7. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

    PubMed Central

    Tse, Amanda; Verkhivker, Gennady M.

    2015-01-01

    Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating

  8. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution.

    PubMed

    Tse, Amanda; Verkhivker, Gennady M

    2015-01-01

    Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating

  9. QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells

    PubMed Central

    Fisher, Ciarán P.; Plant, Nicholas J.; Moore, J. Bernadette; Kierzek, Andrzej M.

    2013-01-01

    Motivation: Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype–phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. Results: We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype–phenotype relationships. Availability and implementation: The model and simulation software implemented in C++ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/. Contact: a.kierzek@surrey.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24064420

  10. Network-Based Models in Molecular Biology

    NASA Astrophysics Data System (ADS)

    Beyer, Andreas

    Biological systems are characterized by a large number of diverse interactions. Interaction maps have been used to abstract those interactions at all biological scales ranging from food webs at the ecosystem level down to protein interaction networks at the molecular scale.

  11. Unveiling the complex network of interactions in Ionic Liquids: a combined EXAFS and Molecular Dynamics approach

    NASA Astrophysics Data System (ADS)

    Serva, A.; Migliorati, V.; Lapi, A.; D'Angelo, P.

    2016-05-01

    The structural properties of geminal dicationic ionic liquids ([Cn (mim)2]Br2)/water mixtures have been investigated by means of extended X-ray absorption fine structure (EXAFS) spectroscopy and Molecular Dynamics (MD) simulations. This synergic approach allowed us to assess the reliability of the MD results and to provide accurate structural information about the first coordination shell of the Br- ion. We found that the local environment around the anion changes as a function of the water concentration, while it is the same independently from the length of the bridge-alkyl chain. Moreover, as regards the long-range structural organization, no tail-tail aggregation occurs with increasing alkyl chain length.

  12. Cross-cancer profiling of molecular alterations within the human autophagy interaction network

    PubMed Central

    Lebovitz, Chandra B; Robertson, A Gordon; Goya, Rodrigo; Jones, Steven J; Morin, Ryan D; Marra, Marco A; Gorski, Sharon M

    2015-01-01

    Aberrant activation or disruption of autophagy promotes tumorigenesis in various preclinical models of cancer, but whether the autophagy pathway is a target for recurrent molecular alteration in human cancer patient samples is unknown. To address this outstanding question, we surveyed 211 human autophagy-associated genes for tumor-related alterations to DNA sequence and RNA expression levels and examined their association with patient survival outcomes in multiple cancer types with sequence data from The Cancer Genome Atlas consortium. We found 3 (RB1CC1/FIP200, ULK4, WDR45/WIPI4) and one (ATG7) core autophagy genes to be under positive selection for somatic mutations in endometrial carcinoma and clear cell renal carcinoma, respectively, while 29 autophagy regulators and pathway interactors, including previously identified KEAP1, NFE2L2, and MTOR, were significantly mutated in 6 of the 11 cancer types examined. Gene expression analyses revealed that GABARAPL1 and MAP1LC3C/LC3C transcripts were less abundant in breast cancer and non-small cell lung cancers than in matched normal tissue controls; ATG4D transcripts were increased in lung squamous cell carcinoma, as were ATG16L2 transcripts in kidney cancer. Unsupervised clustering of autophagy-associated mRNA levels in tumors stratified patient overall survival in 3 of 9 cancer types (acute myeloid leukemia, clear cell renal carcinoma, and head and neck cancer). These analyses provide the first comprehensive resource of recurrently altered autophagy-associated genes in human tumors, and highlight cancer types and subtypes where perturbed autophagy may be relevant to patient overall survival. PMID:26208877

  13. TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

    PubMed Central

    2012-01-01

    Background Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. Results We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous

  14. Artificial Neural Networks Analysis Used to Evaluate the Molecular Interactions between Selected Drugs and Human Cyclooxygenase2 Receptor

    PubMed Central

    Tayarani, Ali; Baratian, Ali; Naghibi Sistani, Mohammad-Bagher; Saberi, Mohammad Reza; Tehranizadeh, Zeinab

    2013-01-01

    Objective(s): A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Artificial neural networks (ANNs) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. We proposed a structure that obtains binding energy using physicochemical molecular descriptions of the selected drugs. Material and Methods: The set of 33 drugs with their binding energy to cyclooxygenase enzyme (COX2) in hand, from different structure groups, were considered. 27 physicochemical property descriptors were calculated by standard molecular modeling. Binding energy was calculated for each compound through docking and also ANN. A multi-layer perceptron neural network was used. Results: The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between binding energy observed and calculated. The final model possessed a 27-4-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.973, 0.956 and 0.950, respectively. Conclusion: Results show that docking results and ANN data have a high correlation. It was shown that ANN is a strong tool for prediction of the binding energy and thus inhibition constants for different drugs in very short periods of time. PMID:24494073

  15. Network models provide insights into how oriens–lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations

    PubMed Central

    Ferguson, Katie A.; Huh, Carey Y. L.; Amilhon, Bénédicte; Manseau, Frédéric; Williams, Sylvain; Skinner, Frances K.

    2015-01-01

    Hippocampal theta is a 4–12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens–lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power, we explored how the number of OLM-BiC connections and connection strength affected local theta power. We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not. Inactivation of OLM cells could result in no change or even an increase in theta power. We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations. Overall, our network models reveal a dynamic interplay

  16. Probing Allosteric Inhibition Mechanisms of the Hsp70 Chaperone Proteins Using Molecular Dynamics Simulations and Analysis of the Residue Interaction Networks.

    PubMed

    Stetz, Gabrielle; Verkhivker, Gennady M

    2016-08-22

    Although molecular mechanisms of allosteric regulation in the Hsp70 chaperones have been extensively studied at both structural and functional levels, the current understanding of allosteric inhibition of chaperone activities by small molecules is still lacking. In the current study, using a battery of computational approaches, we probed allosteric inhibition mechanisms of E. coli Hsp70 (DnaK) and human Hsp70 proteins by small molecule inhibitors PET-16 and novolactone. Molecular dynamics simulations and binding free energy analysis were combined with network-based modeling of residue interactions and allosteric communications to systematically characterize and compare molecular signatures of the apo form, substrate-bound, and inhibitor-bound chaperone complexes. The results suggested a mechanism by which the allosteric inhibitors may leverage binding energy hotspots in the interaction networks to stabilize a specific conformational state and impair the interdomain allosteric control. Using the network-based centrality analysis and community detection, we demonstrated that substrate binding may strengthen the connectivity of local interaction communities, leading to a dense interaction network that can promote an efficient allosteric communication. In contrast, binding of PET-16 to DnaK may induce significant dynamic changes and lead to a fractured interaction network and impaired allosteric communications in the DnaK complex. By using a mechanistic-based analysis of distance fluctuation maps and allosteric propensities of protein residues, we determined that the allosteric network in the PET-16 complex may be small and localized due to the reduced communication and low cooperativity of the substrate binding loops, which may promote the higher rates of substrate dissociation and the decreased substrate affinity. In comparison with the significant effect of PET-16, binding of novolactone to HSPA1A may cause only moderate network changes and preserve allosteric

  17. Interactive molecular dynamics

    NASA Astrophysics Data System (ADS)

    Schroeder, Daniel V.

    2015-03-01

    Physics students now have access to interactive molecular dynamics simulations that can model and animate the motions of hundreds of particles, such as noble gas atoms, that attract each other weakly at short distances but repel strongly when pressed together. Using these simulations, students can develop an understanding of forces and motions at the molecular scale, nonideal fluids, phases of matter, thermal equilibrium, nonequilibrium states, the Boltzmann distribution, the arrow of time, and much more. This article summarizes the basic features and capabilities of such a simulation, presents a variety of student exercises using it at the introductory and intermediate levels, and describes some enhancements that can further extend its uses. A working simulation code, in html5 and javascript for running within any modern Web browser, is provided as an online supplement.

  18. Estimation of intermolecular interactions in polymer networks

    SciTech Connect

    Subrananian, P.R.; Galiatsatos, V.

    1993-12-31

    Strain-birefringence measurements have been used to estimate intermolecular interactions in polymer networks. The intensity of the interaction has been quantified through a theoretical scheme recently proposed by Erman. The results show that these interactions diminish with decreasing molecular weight between cross-links and decreasing cross-link functionality.

  19. How to Predict Molecular Interactions between Species?

    PubMed Central

    Schulze, Sylvie; Schleicher, Jana; Guthke, Reinhard; Linde, Jörg

    2016-01-01

    Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We

  20. Modeling of growth factor-receptor systems: from molecular-level protein interaction networks to whole-body compartment models

    PubMed Central

    Wu, Florence T.H.; Stefanini, Marianne O.; Mac Gabhann, Feilim; Popel, Aleksander S.

    2010-01-01

    Most physiological processes are subjected to molecular regulation by growth factors, which are secreted proteins that activate chemical signal transduction pathways through binding of specific cell-surface receptors. One particular growth factor system involved in the in vivo regulation of blood vessel growth is called the vascular endothelial growth factor (VEGF) system. Computational and numerical techniques are well-suited to handle the molecular complexity (the number of binding partners involved, including ligands, receptors, and inert binding sites) and multi-scale nature (intra-tissue vs. inter-tissue transport and local vs. systemic effects within an organism) involved in modeling growth factor system interactions and effects. This paper introduces a variety of in silico models that seek to recapitulate different aspects of VEGF system biology at various spatial and temporal scales: molecular-level kinetic models focus on VEGF ligand-receptor interactions at and near the endothelial cell surface; meso-scale single-tissue 3D models can simulate the effects of multi-cellular tissue architecture on the spatial variation in VEGF ligand production and receptor activation; compartmental modeling allows efficient prediction of average interstitial VEGF concentrations and cell-surface VEGF signaling intensities across multiple large tissue volumes, permitting the investigation of whole-body inter-tissue transport (e.g., vascular permeability and lymphatic drainage). The given examples will demonstrate the utility of computational models in aiding both basic science and clinical research on VEGF systems biology. PMID:19897104

  1. Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations

    PubMed Central

    2015-01-01

    The integration of atomic-resolution experimental and computational methods offers the potential for elucidating key aspects of protein folding that are not revealed by either approach alone. Here, we combine equilibrium NMR measurements of thermal unfolding and long molecular dynamics simulations to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue–residue couplings associated with cooperative folding emerges. Such thermodynamic residue–residue couplings appear to be linked to the order of mechanistically significant events that take place during the folding process. Our results on gpW indicate that the methods employed in this study are likely to prove broadly applicable to the fine analysis of folding mechanisms in fast folding proteins. PMID:25924808

  2. Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations.

    PubMed

    Sborgi, Lorenzo; Verma, Abhinav; Piana, Stefano; Lindorff-Larsen, Kresten; Cerminara, Michele; Santiveri, Clara M; Shaw, David E; de Alba, Eva; Muñoz, Victor

    2015-05-27

    The integration of atomic-resolution experimental and computational methods offers the potential for elucidating key aspects of protein folding that are not revealed by either approach alone. Here, we combine equilibrium NMR measurements of thermal unfolding and long molecular dynamics simulations to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue-residue couplings associated with cooperative folding emerges. Such thermodynamic residue-residue couplings appear to be linked to the order of mechanistically significant events that take place during the folding process. Our results on gpW indicate that the methods employed in this study are likely to prove broadly applicable to the fine analysis of folding mechanisms in fast folding proteins. PMID:25924808

  3. Interacting neural networks.

    PubMed

    Metzler, R; Kinzel, W; Kanter, I

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random. PMID:11088736

  4. A Novel Network Model for Molecular Prognosis

    PubMed Central

    Wan, Ying-Wooi; Bose, Swetha; Denvir, James; Guo, Nancy Lan

    2015-01-01

    Network-based genome-wide association studies (NWAS) utilize the molecular interactions between genes and functional pathways in biomarker identification. This study presents a novel network-based methodology for identifying prognostic gene signatures to predict cancer recurrence. The methodology contains the following steps: 1) Constructing genome-wide coexpression networks for different disease states (metastatic vs. non-metastatic). Prediction logic is used to induct valid implication relations between each pair of gene expression profiles in terms of formal logic rules. 2) Identifying differential components associated with specific disease states from the genome-wide coexpression networks. 3) Dissecting network modules that are tightly connected with major disease signal hallmarks from the disease specific differential components. 4) Identifying most significant genes/probes associated with clinical outcome from the pathway connected network modules. Using this methodology, a 14-gene prognostic signature was identified for accurate patient stratification in early stage lung cancer. PMID:26005718

  5. A network of interdependent molecular interactions describes a higher order Nrd1-Nab3 complex involved in yeast transcription termination.

    PubMed

    Loya, Travis J; O'Rourke, Thomas W; Degtyareva, Natalya; Reines, Daniel

    2013-11-22

    Nab3 and Nrd1 are yeast heterogeneous nuclear ribonucleoprotein (hnRNP)-like proteins that heterodimerize and bind RNA. Genetic and biochemical evidence reveals that they are integral to the termination of transcription of short non-coding RNAs by RNA polymerase II. Here we define a Nab3 mutation (nab3Δ134) that removes an essential part of the protein's C terminus but nevertheless can rescue, in trans, the phenotype resulting from a mutation in the RNA recognition motif of Nab3. This low complexity region of Nab3 appears intrinsically unstructured and can form a hydrogel in vitro. These data support a model in which multiple Nrd1-Nab3 heterodimers polymerize onto substrate RNA to effect termination, allowing complementation of one mutant Nab3 molecule by another lacking a different function. The self-association property of Nab3 adds to the previously documented interactions between these hnRNP-like proteins, RNA polymerase II, and the nascent transcript, leading to a network of nucleoprotein interactions that define a higher order Nrd1-Nab3 complex. This was underscored from the synthetic phenotypes of yeast strains with pairwise combinations of Nrd1 and Nab3 mutations known to affect their distinct biochemical activities. The mutations included a Nab3 self-association defect, a Nab3-Nrd1 heterodimerization defect, a Nrd1-polymerase II binding defect, and an Nab3-RNA recognition motif mutation. Although no single mutation was lethal, cells with any two mutations were not viable for four such pairings, and a fifth displayed a synthetic growth defect. These data strengthen the idea that a multiplicity of interactions is needed to assemble a higher order Nrd1-Nab3 complex that coats specific nascent RNAs in preparation for termination. PMID:24100036

  6. Detection of molecular interactions

    DOEpatents

    Groves, John T.; Baksh, Michael M.; Jaros, Michal

    2012-02-14

    A method and assay are described for measuring the interaction between a ligand and an analyte. The assay can include a suspension of colloidal particles that are associated with a ligand of interest. The colloidal particles are maintained in the suspension at or near a phase transition state from a condensed phase to a dispersed phase. An analyte to be tested is then added to the suspension. If the analyte binds to the ligand, a phase change occurs to indicate that the binding was successful.

  7. Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases.

    PubMed

    Wu, Xiaodan; Chen, Luonan; Wang, Xiangdong

    2014-01-01

    Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers. PMID:24995123

  8. Interacting epidemics on overlay networks

    NASA Astrophysics Data System (ADS)

    Funk, Sebastian; Jansen, Vincent A. A.

    2010-03-01

    The interaction between multiple pathogens spreading on networks connecting a given set of nodes presents an ongoing theoretical challenge. Here, we aim to understand such interactions by studying bond percolation of two different processes on overlay networks of arbitrary joint degree distribution. We find that an outbreak of a first pathogen providing immunity to another one spreading subsequently on a second network connecting the same set of nodes does so most effectively if the degrees on the two networks are positively correlated. In that case, the protection is stronger the more heterogeneous the degree distributions of the two networks are. If, on the other hand, the degrees are uncorrelated or negatively correlated, increasing heterogeneity reduces the potential of the first process to prevent the second one from reaching epidemic proportions. We generalize these results to cases where the edges of the two networks overlap to arbitrary amount, or where the immunity granted is only partial. If both processes grant immunity to each other, we find a wide range of possible situations of coexistence or mutual exclusion, depending on the joint degree distribution of the underlying networks and the amount of immunity granted mutually. These results generalize the concept of a coexistence threshold and illustrate the impact of large-scale network structure on the interaction between multiple spreading agents.

  9. Exploring drug combinations in genetic interaction network

    PubMed Central

    2012-01-01

    Background Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. Results In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. Conclusions This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations. PMID:22595004

  10. Translational disease interpretation with molecular networks

    PubMed Central

    Baudot, Anaïs; Gómez-López, Gonzalo; Valencia, Alfonso

    2009-01-01

    Molecular networks are being used to reconcile genotypes and phenotypes by integrating medical information. In this context, networks will be instrumental for the interpretation of disease at the personalized medicine level. PMID:19591646

  11. Dynamic and interacting complex networks

    NASA Astrophysics Data System (ADS)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  12. Dynamic interactions in neural networks

    SciTech Connect

    Arbib, M.A. ); Amari, S. )

    1989-01-01

    The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

  13. Interactive Modelling of Molecular Structures

    NASA Astrophysics Data System (ADS)

    Rustad, J. R.; Kreylos, O.; Hamann, B.

    2004-12-01

    The "Nanotech Construction Kit" (NCK) [1] is a new project aimed at improving the understanding of molecular structures at a nanometer-scale level by visualization and interactive manipulation. Our very first prototype is a virtual-reality program allowing the construction of silica and carbon structures from scratch by assembling them one atom at a time. In silica crystals or glasses, the basic building block is an SiO4 unit, with the four oxygen atoms arranged around the central silicon atom in the shape of a regular tetrahedron. Two silicate units can connect to each other by their silicon atoms covalently bonding to one shared oxygen atom. Geometrically, this means that two tetrahedra can link at their vertices. Our program is based on geometric representations and uses simple force fields to simulate the interaction of building blocks, such as forming/breaking of bonds and repulsion. Together with stereoscopic visualization and direct manipulation of building blocks using wands or data gloves, this enables users to create realistic and complex molecular models in short amounts of time. The NCK can either be used as a standalone tool, to analyze or experiment with molecular structures, or it can be used in combination with "traditional" molecular dynamics (MD) simulations. In a first step, the NCK can create initial configurations for subsequent MD simulation. In a more evolved setup, the NCK can serve as a visual front-end for an ongoing MD simulation, visualizing changes in simulation state in real time. Additionally, the NCK can be used to change simulation state on-the-fly, to experiment with different simulation conditions, or force certain events, e.g., the forming of a bond, and observe the simulation's reaction. [1] http://graphics.cs.ucdavis.edu/~okreylos/ResDev/NanoTech

  14. The MIntAct Project and Molecular Interaction Databases.

    PubMed

    Licata, Luana; Orchard, Sandra

    2016-01-01

    Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination. PMID:27115627

  15. Probabilistic inference of molecular networks from noisy data sources.

    PubMed

    Iossifov, Ivan; Krauthammer, Michael; Friedman, Carol; Hatzivassiloglou, Vasileios; Bader, Joel S; White, Kevin P; Rzhetsky, Andrey

    2004-05-22

    Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here, we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from the literature and yeast two-hybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources. PMID:14871876

  16. Experimental evolution of protein–protein interaction networks

    PubMed Central

    Kaçar, Betül; Gaucher, Eric A.

    2013-01-01

    The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

  17. Protein complexes and functional modules in molecular networks

    NASA Astrophysics Data System (ADS)

    Spirin, Victor; Mirny, Leonid A.

    2003-10-01

    Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

  18. Statistical Mechanics of Temporal and Interacting Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun

    In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide

  19. Protein-protein interaction networks (PPI) and complex diseases

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

    2014-01-01

    The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

  20. Mechanically Induced Trapping of Molecular Interactions and Its Applications.

    PubMed

    Garcia-Cordero, Jose L; Maerkl, Sebastian J

    2016-06-01

    Measuring binding affinities and association/dissociation rates of molecular interactions is important for a quantitative understanding of cellular mechanisms. Many low-throughput methods have been developed throughout the years to obtain these parameters. Acquiring data with higher accuracy and throughput is, however, necessary to characterize complex biological networks. Here, we provide an overview of a high-throughput microfluidic method based on mechanically induced trapping of molecular interactions (MITOMI). MITOMI can be used to obtain affinity constants and kinetic rates of hundreds of protein-ligand interactions in parallel. It has been used in dozens of studies to measure binding affinities of transcription factors, map protein interaction networks, identify pharmacological inhibitors, and perform high-throughput, low-cost molecular diagnostics. This article covers the technological aspects of MITOMI and its applications. PMID:25805850

  1. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  2. Charge transport network dynamics in molecular aggregates.

    PubMed

    Jackson, Nicholas E; Chen, Lin X; Ratner, Mark A

    2016-08-01

    Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, [Formula: see text] Simulations reveal the relevant timescale for local transfer integral decorrelation to be [Formula: see text]100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive with charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed. PMID:27439871

  3. Structure and Interactions in Neurofilament Networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna; Ojeda-Lopez, Miguel; Safinya, Cyrus

    2004-03-01

    Neurofilaments (NFs) are a major constituent of myelinated axons of nerve cells, which assemble from three subunit proteins of low, medium, and high molecular weight to form a 10 nm diameter rod with sidearms radiating from the center. The sidearm interactions impart structural stability and result in an oriented network of NFs running parallel to the axon. Over or under expression of NF subunits is related to abnormal NF-networks, which are known hallmarks of motor neuron diseases (ALS). Here, we reassemble NFs from subunit proteins purified from bovine spinal cord. We demonstrate the formation of the NF network in vitro where synchrotron x-ray scattering (SSRL) reveals a well-defined interfilament spacing while the defect structure in polarized optical microcopy shows the liquid crystalline nature. The spacing varies depending on subunit molar ratios and salt conditions and we relate this change to the mechanical stability of the lattice. This change in lattice spacing yields insight into the stabilizing interactions between the NF sidearms. Supported by NSF DMR- 0203755, CTS-0103516, and NIH GM-59288.

  4. New Insights into Molecular Ehrlichia chaffeensis-Host Interactions

    PubMed Central

    Wakeel, Abdul; Zhu, Bing; Yu, Xue-jie; McBride, Jere W.

    2010-01-01

    Ehrlichia chaffeensis is an obligately intracellular bacterium that exhibits tropism for mononuclear phagocytes and survives by reprogramming the host cell. Here we review new information regarding the newly characterized effector molecules and the complex network of molecular host-pathogen interactions that the organism exploits enabling it to thrive and persist intracellularly. PMID:20116446

  5. Pattern Discovery in Breast Cancer Specific Protein Interaction Network

    PubMed Central

    Wu, Xiaogang; Harrison, Scott H.; Chen, Jake Yue

    2009-01-01

    The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein’s biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of “ant colonies” on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network. PMID:21347162

  6. Interactive Video Networks: Experiences, Issues and Challenges.

    ERIC Educational Resources Information Center

    Stahl, Bil

    1993-01-01

    Discusses multipoint interactive video networks and describes experiences with two networks in North Carolina, the MCNC CONCERT (COmmunications network of North Carolina for Education, Research, and Technology) and the Vision Carolina network. Digital video is explained, and issues concerning various components of the telecommunications industry…

  7. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

  8. Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks

    PubMed Central

    2013-01-01

    Background Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. Methods In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. Results In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions. Conclusion The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer. PMID

  9. Molecular Handshake: Recognition through Weak Noncovalent Interactions

    ERIC Educational Resources Information Center

    Murthy, Parvathi S.

    2006-01-01

    The weak noncovalent interactions between substances, the handshake in the form of electrostatic interactions, van der Waals' interactions or hydrogen bonding is universal to all living and nonliving matter. They significantly influence the molecular and bulk properties and behavior of matter. Their transient nature affects chemical reactions and…

  10. A random interacting network model for complex networks

    NASA Astrophysics Data System (ADS)

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-12-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.

  11. A random interacting network model for complex networks

    PubMed Central

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  12. A random interacting network model for complex networks.

    PubMed

    Goswami, Bedartha; Shekatkar, Snehal M; Rheinwalt, Aljoscha; Ambika, G; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  13. Theoretical studies of molecular interactions

    SciTech Connect

    Lester, W.A. Jr.

    1993-12-01

    This research program is directed at extending fundamental knowledge of atoms and molecules including their electronic structure, mutual interaction, collision dynamics, and interaction with radiation. The approach combines the use of ab initio methods--Hartree-Fock (HF) multiconfiguration HF, configuration interaction, and the recently developed quantum Monte Carlo (MC)--to describe electronic structure, intermolecular interactions, and other properties, with various methods of characterizing inelastic and reaction collision processes, and photodissociation dynamics. Present activity is focused on the development and application of the QMC method, surface catalyzed reactions, and reorientation cross sections.

  14. Network Physiology: How Organ Systems Dynamically Interact

    PubMed Central

    Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  15. Network Physiology: How Organ Systems Dynamically Interact.

    PubMed

    Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  16. Hunting complex differential gene interaction patterns across molecular contexts

    PubMed Central

    Song, Mingzhou; Zhang, Yang; Katzaroff, Alexia J.; Edgar, Bruce A.; Buttitta, Laura

    2014-01-01

    Heterogeneity in genetic networks across different signaling molecular contexts can suggest molecular regulatory mechanisms. Here we describe a comparative chi-square analysis (CPχ2) method, considerably more flexible and effective than other alternatives, to screen large gene expression data sets for conserved and differential interactions. CPχ2 decomposes interactions across conditions to assess homogeneity and heterogeneity. Theoretically, we prove an asymptotic chi-square null distribution for the interaction heterogeneity statistic. Empirically, on synthetic yeast cell cycle data, CPχ2 achieved much higher statistical power in detecting differential networks than alternative approaches. We applied CPχ2 to Drosophila melanogaster wing gene expression arrays collected under normal conditions, and conditions with overexpressed E2F and Cabut, two transcription factor complexes that promote ectopic cell cycling. The resulting differential networks suggest a mechanism by which E2F and Cabut regulate distinct gene interactions, while still sharing a small core network. Thus, CPχ2 is sensitive in detecting network rewiring, useful in comparing related biological systems. PMID:24482443

  17. A Network Synthesis Model for Generating Protein Interaction Network Families

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2012-01-01

    In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/. PMID:22912671

  18. STALK : an interactive virtual molecular docking system.

    SciTech Connect

    Levine, D.; Facello, M.; Hallstrom, P.; Reeder, G.; Walenz, B.; Stevens, F.; Univ. of Illinois

    1997-04-01

    Several recent technologies-genetic algorithms, parallel and distributed computing, virtual reality, and high-speed networking-underlie a new approach to the computational study of how biomolecules interact or 'dock' together. With the Stalk system, a user in a virtual reality environment can interact with a genetic algorithm running on a parallel computer to help in the search for likely geometric configurations.

  19. Measuring specialization in species interaction networks

    PubMed Central

    Blüthgen, Nico; Menzel, Florian; Blüthgen, Nils

    2006-01-01

    Background Network analyses of plant-animal interactions hold valuable biological information. They are often used to quantify the degree of specialization between partners, but usually based on qualitative indices such as 'connectance' or number of links. These measures ignore interaction frequencies or sampling intensity, and strongly depend on network size. Results Here we introduce two quantitative indices using interaction frequencies to describe the degree of specialization, based on information theory. The first measure (d') describes the degree of interaction specialization at the species level, while the second measure (H2') characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and derived from Shannon entropy. The species-level index d' can be used to analyze variation within networks, while H2' as a network-level index is useful for comparisons across different interaction webs. Analyses of two published pollinator networks identified differences and features that have not been detected with previous approaches. For instance, plants and pollinators within a network differed in their average degree of specialization (weighted mean d'), and the correlation between specialization of pollinators and their relative abundance also differed between the webs. Rarefied sampling effort in both networks and null model simulations suggest that H2' is not affected by network size or sampling intensity. Conclusion Quantitative analyses reflect properties of interaction networks more appropriately than previous qualitative attempts, and are robust against variation in sampling intensity, network size and symmetry. These measures will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions. PMID:16907983

  20. Modularity in the evolution of yeast protein interaction network

    PubMed Central

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

    Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution. PMID:25914446

  1. Tools of the trade: studying molecular networks in plants.

    PubMed

    Proost, Sebastian; Mutwil, Marek

    2016-04-01

    Driven by recent technological improvements, genes can be now studied in a larger biological context. Genes and their protein products rarely operate as a single entity and large-scale mapping by protein-protein interactions can unveil the molecular complexes that form in the cell to carry out various functions. Expression analysis under multiple conditions, supplemented with protein-DNA binding data can highlight when genes are active and how they are regulated. Representing these data in networks and finding strongly connected sub-graphs has proven to be a powerful tool to predict the function of unknown genes. As such networks are gradually becoming available for various plant species, it becomes possible to study how networks evolve. This review summarizes currently available network data and related tools for plants. Furthermore we aim to provide an outlook of future analyses that can be done in plants based on work done in other fields. PMID:26990519

  2. Dynamic network analysis of protein interactions

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind; Deri, Joya

    2007-03-01

    Network approaches have recently become a popular tool to study complex systems such as cellular metabolism and protein interactions. A substantial number of analyses of the protein interaction network (PIN) of the yeast Saccharomyces cerevisiae have considered this network as a static entity, not taking the network's dynamic nature into account. Here, we examine the time-variation of gene regulation superimposed on the PIN by defining mRNA expression profiles throughout the cell cycle as node weights. To characterize these network dynamics, we have both developed a set of novel network measures as well as studied previously published measures for weighted networks. We expect that our approach will provide a deeper understanding of protein regulation during the cell cycle.

  3. Interactivity vs. fairness in networked linux systems

    SciTech Connect

    Wu, Wenji; Crawford, Matt; /Fermilab

    2007-01-01

    In general, the Linux 2.6 scheduler can ensure fairness and provide excellent interactive performance at the same time. However, our experiments and mathematical analysis have shown that the current Linux interactivity mechanism tends to incorrectly categorize non-interactive network applications as interactive, which can lead to serious fairness or starvation issues. In the extreme, a single process can unjustifiably obtain up to 95% of the CPU! The root cause is due to the facts that: (1) network packets arrive at the receiver independently and discretely, and the 'relatively fast' non-interactive network process might frequently sleep to wait for packet arrival. Though each sleep lasts for a very short period of time, the wait-for-packet sleeps occur so frequently that they lead to interactive status for the process. (2) The current Linux interactivity mechanism provides the possibility that a non-interactive network process could receive a high CPU share, and at the same time be incorrectly categorized as 'interactive.' In this paper, we propose and test a possible solution to address the interactivity vs. fairness problems. Experiment results have proved the effectiveness of the proposed solution.

  4. VIBE: A virtual biomolecular environment for interactive molecular modeling

    SciTech Connect

    Cruz-Neira, C.; Langley, R.; Bash, P.A.

    1996-12-31

    Virtual reality tightly coupled to high performance computing and communications ushers in a new era for the study of molecular recognition and the rational design of pharmaceutical compounds. We have created a Virtual Biomolecular Environment (VIBE), which consists of (1) massively parallel computing to simulate the physical and chemical properties of a molecular system, (2) the Cave Automatic Virtual Environment (CAVE) for immersive display and interaction with the molecular system, and (3) a high-speed network interface to exchange data between the simulation and the CAVE. VIBE enables molecular scientists to have a visual, auditory, and haptic experience with a chemical system, while simultaneously manipulating its physical properties by steering, in real-time, a simulation executed on a supercomputer. We demonstrate the characteristics of VIBE using an HIV protease-cyclic urea inhibitor complex. 22 refs., 4 figs.

  5. Bacterial molecular networks: bridging the gap between functional genomics and dynamical modelling.

    PubMed

    van Helden, Jacques; Toussaint, Ariane; Thieffry, Denis

    2012-01-01

    This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein interaction networks, and regulatory networks. Each protocol is documented by concrete case studies dedicated to model bacteria or interacting populations. Altogether, the chapters provide a representative overview of state-of-the-art methods for data integration and retrieval, network visualization, graph analysis, and dynamical modelling. PMID:22144145

  6. Mutually-antagonistic interactions in baseball networks

    NASA Astrophysics Data System (ADS)

    Saavedra, Serguei; Powers, Scott; McCotter, Trent; Porter, Mason A.; Mucha, Peter J.

    2010-03-01

    We formulate the head-to-head matchups between Major League Baseball pitchers and batters from 1954 to 2008 as a bipartite network of mutually-antagonistic interactions. We consider both the full network and single-season networks, which exhibit structural changes over time. We find interesting structure in the networks and examine their sensitivity to baseball’s rule changes. We then study a biased random walk on the matchup networks as a simple and transparent way to (1) compare the performance of players who competed under different conditions and (2) include information about which particular players a given player has faced. We find that a player’s position in the network does not correlate with his placement in the random walker ranking. However, network position does have a substantial effect on the robustness of ranking placement to changes in head-to-head matchups.

  7. Using Graph-Based Assessments within Socratic Tutorials to Reveal and Refine Students' Analytical Thinking about Molecular Networks

    ERIC Educational Resources Information Center

    Trujillo, Caleb; Cooper, Melanie M.; Klymkowsky, Michael W.

    2012-01-01

    Biological systems, from the molecular to the ecological, involve dynamic interaction networks. To examine student thinking about networks we used graphical responses, since they are easier to evaluate for implied, but unarticulated assumptions. Senior college level molecular biology students were presented with simple molecular level scenarios;…

  8. Molecular interaction maps as information organizers and simulation guides

    NASA Astrophysics Data System (ADS)

    Kohn, Kurt W.

    2001-03-01

    A graphical method for mapping bioregulatory networks is presented that is suited for the representation of multimolecular complexes, protein modifications, as well as actions at cell membranes and between protein domains. The symbol conventions defined for these molecular interaction maps are designed to accommodate multiprotein assemblies and protein modifications that can generate combinatorially large numbers of molecular species. Diagrams can either be "heuristic," meaning that detailed knowledge of all possible reaction paths is not required, or "explicit," meaning that the diagrams are totally unambiguous and suitable for simulation. Interaction maps are linked to annotation lists and indexes that provide ready access to pertinent data and references, and that allow any molecular species to be easily located. Illustrative interaction maps are included on the domain interactions of Src, transcription control of E2F-regulated genes, and signaling from receptor tyrosine kinase through phosphoinositides to Akt/PKB. A simple method of going from an explicit interaction diagram to an input file for a simulation program is outlined, in which the differential equations need not be written out. The role of interaction maps in selecting and defining systems for modeling is discussed.

  9. Spin vibronics in interacting nonmagnetic molecular nanojunctions

    NASA Astrophysics Data System (ADS)

    Weiss, S.; Brüggemann, J.; Thorwart, M.

    2015-07-01

    We show that in the presence of ferromagnetic electronic reservoirs and spin-dependent tunnel couplings, molecular vibrations in nonmagnetic single molecular transistors induce an effective intramolecular exchange magnetic field. It generates a finite spin accumulation and precession for the electrons confined on the molecular bridge and occurs under (non)equilibrium conditions. The effective exchange magnetic field is calculated here to lowest order in the tunnel coupling for a nonequilibrium transport setup. Coulomb interaction between electrons is taken into account as well as a finite electron-phonon coupling. We show that for realistic physical parameters, an effective spin-phonon coupling emerges. It is induced by quantum many-body interactions, which are either of electron-phonon or Coulomb type. We investigate the precession and accumulation of the confined spins as function of bias and gate voltages as well as their dependence on the angle enclosed by the magnetizations between the left and right reservoir.

  10. The dissimilarity of species interaction networks.

    PubMed

    Poisot, Timothée; Canard, Elsa; Mouillot, David; Mouquet, Nicolas; Gravel, Dominique

    2012-12-01

    In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β-diversity. We emphasise that scaling up β-diversity of community composition to the β-diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions. PMID:22994257

  11. Multiple interactions between molecular and supramolecular ordering

    NASA Astrophysics Data System (ADS)

    Manno, M.; Emanuele, A.; Martorana, V.; Bulone, D.; San Biagio, P. L.; Palma-Vittorelli, M. B.; Palma, M. U.

    1999-02-01

    We report studies of the interplay among processes of molecular conformational changes, spinodal demixing of the solution, and molecular crosslinking involved in the physical gelation of a biopolysaccharide-water system. Multiple interactions and kinetic competition among these processes were studied under largely different absolute and relative values of their individual rates by appropriate choices of the quenching temperature at constant polymer concentration. Quenching temperature strongly affects the rate of growth but not the final value of the fractal dimension of the gel. Kinetic competition plays a central role in determining the final conformation of individual molecules and the structure and properties of the final gel. This behavior highlights the frustrated nature of the system, and the need of bringing kinetics sharply into focus in gelation theories. General aspects of the present findings and, specifically, the interplay of molecular conformation changes, solution demixing, and molecular crosslinking extend the relevance of these studies to the fast growing field of amyloid condensation and Prion diseases.

  12. Integrated inference and evaluation of host–fungi interaction networks

    PubMed Central

    Remmele, Christian W.; Luther, Christian H.; Balkenhol, Johannes; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus T.

    2015-01-01

    Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data. PMID:26300851

  13. Rationalizing Tight Ligand Binding through Cooperative Interaction Networks

    PubMed Central

    2011-01-01

    Small modifications of the molecular structure of a ligand sometimes cause strong gains in binding affinity to a protein target, rendering a weakly active chemical series suddenly attractive for further optimization. Our goal in this study is to better rationalize and predict the occurrence of such interaction hot-spots in receptor binding sites. To this end, we introduce two new concepts into the computational description of molecular recognition. First, we take a broader view of noncovalent interactions and describe protein–ligand binding with a comprehensive set of favorable and unfavorable contact types, including for example halogen bonding and orthogonal multipolar interactions. Second, we go beyond the commonly used pairwise additive treatment of atomic interactions and use a small world network approach to describe how interactions are modulated by their environment. This approach allows us to capture local cooperativity effects and considerably improves the performance of a newly derived empirical scoring function, ScorpionScore. More importantly, however, we demonstrate how an intuitive visualization of key intermolecular interactions, interaction networks, and binding hot-spots supports the identification and rationalization of tight ligand binding. PMID:22087588

  14. A simple model for studying interacting networks

    NASA Astrophysics Data System (ADS)

    Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.

    2011-03-01

    Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.

  15. Supra-molecular networks for CO2 capture

    NASA Astrophysics Data System (ADS)

    Sadowski, Jerzy; Kestell, John

    Utilizing capabilities of low-energy electron microscopy (LEEM) for non-destructive interrogation of the real-time molecular self-assembly, we have investigated supramolecular systems based on carboxylic acid-metal complexes, such as trimesic and mellitic acid, doped with transition metals. Such 2D networks can act as host systems for transition-metal phthalocyanines (MPc; M = Fe, Ti, Sc). The electrostatic interactions of CO2 molecules with transition metal ions can be tuned by controlling the type of TM ion and the size of the pore in the host network. We further applied infrared reflection-absorption spectroscopy (IRRAS) to determine of the molecular orientation of the functional groups and the whole molecule in the 2D monolayers of carboxylic acid. The kinetics and mechanism of the CO2 adsorption/desorption on the 2D molecular network, with and without the TM ion doping, have been also investigated. This research used resources of the Center for Functional Nanomaterials, which is the U.S. DOE Office of Science User Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704.

  16. Michigan molecular interactions r2: from interacting proteins to pathways.

    PubMed

    Tarcea, V Glenn; Weymouth, Terry; Ade, Alex; Bookvich, Aaron; Gao, Jing; Mahavisno, Vasudeva; Wright, Zach; Chapman, Adriane; Jayapandian, Magesh; Ozgür, Arzucan; Tian, Yuanyuan; Cavalcoli, Jim; Mirel, Barbara; Patel, Jignesh; Radev, Dragomir; Athey, Brian; States, David; Jagadish, H V

    2009-01-01

    Molecular interaction data exists in a number of repositories, each with its own data format, molecule identifier and information coverage. Michigan molecular interactions (MiMI) assists scientists searching through this profusion of molecular interaction data. The original release of MiMI gathered data from well-known protein interaction databases, and deep merged this information while keeping track of provenance. Based on the feedback received from users, MiMI has been completely redesigned. This article describes the resulting MiMI Release 2 (MiMIr2). New functionality includes extension from proteins to genes and to pathways; identification of highlighted sentences in source publications; seamless two-way linkage with Cytoscape; query facilities based on MeSH/GO terms and other concepts; approximate graph matching to find relevant pathways; support for querying in bulk; and a user focus-group driven interface design. MiMI is part of the NIH's; National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org. PMID:18978014

  17. Systematic computational prediction of protein interaction networks.

    PubMed

    Lees, J G; Heriche, J K; Morilla, I; Ranea, J A; Orengo, C A

    2011-06-01

    Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use. PMID:21572181

  18. Constrained inference of protein interaction networks for invadopodium formation in cancer

    PubMed Central

    Wang, Haizhou; Leung, Ming; Wandinger-Ness, Angela; Hudson, Laurie G.; Song, Mingzhou

    2016-01-01

    Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes’ rule is the demotion of novel interactions with low prior probabilities. Here we present constrained generalized logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. We benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions center around focal adhesion kinase (FAK) and tyrosine kinase substrate TKS4, and 14 interactions are supported by literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs. PMID:26997662

  19. Constrained inference of protein interaction networks for invadopodium formation in cancer.

    PubMed

    Wang, Haizhou; Leung, Ming; Wandinger-Ness, Angela; Hudson, Laurie G; Song, Mingzhou

    2016-04-01

    Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions centre around focal adhesion kinase and tyrosine kinase substrate TKS4, and 14 interactions are supported by the literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs. PMID:26997662

  20. Molecular Mediators Governing Iron-Copper Interactions

    PubMed Central

    Gulec, Sukru; Collins, James F.

    2015-01-01

    Given their similar physiochemical properties, it is a logical postulate that iron and copper metabolism are intertwined. Indeed, iron-copper interactions were first documented over a century ago, but the homeostatic effects of one on the other has not been elucidated at a molecular level to date. Recent experimental work has, however, begun to provide mechanistic insight into how copper influences iron metabolism. During iron deficiency, elevated copper levels are observed in the intestinal mucosa, liver, and blood. Copper accumulation and/or redistribution within enterocytes may influence iron transport, and high hepatic copper may enhance biosynthesis of a circulating ferroxidase, which potentiates iron release from stores. Moreover, emerging evidence has documented direct effects of copper on the expression and activity of the iron-regulatory hormone hepcidin. This review summarizes current experimental work in this field, with a focus on molecular aspects of iron-copper interplay and how these interactions relate to various disease states. PMID:24995690

  1. Broadband networks for interactive telemedical applications

    NASA Astrophysics Data System (ADS)

    Graschew, Georgi; Roelofs, Theo A.; Rakowsky, Stefan; Schlag, Peter M.

    2002-08-01

    Using off-the-shelf hardware components and a specially developed high-end software communication system (WinVicos) satellite networks for interactive telemedicine have been designed and developed. These networks allow for various telemedical applications, like intraoperative teleconsultation, second opinioning, teleteaching, telementoring, etc.. Based on the successful GALENOS network, several projects are currently being realized: MEDASHIP (Medical Assistance for Ships); DELTASS (Disaster Emergency Logistic Telemedicine Advanced Satellites Systems) and EMISPHER (Euro-Mediterranean Internet-Satellite Platform for Health, medical Education and Research).

  2. Description of interatomic interactions with neural networks

    NASA Astrophysics Data System (ADS)

    Hajinazar, Samad; Shao, Junping; Kolmogorov, Aleksey N.

    Neural networks are a promising alternative to traditional classical potentials for describing interatomic interactions. Recent research in the field has demonstrated how arbitrary atomic environments can be represented with sets of general functions which serve as an input for the machine learning tool. We have implemented a neural network formalism in the MAISE package and developed a protocol for automated generation of accurate models for multi-component systems. Our tests illustrate the performance of neural networks and known classical potentials for a range of chemical compositions and atomic configurations. Supported by NSF Grant DMR-1410514.

  3. Harnessing Surface Dislocation Networks for Molecular Self-Assembly

    NASA Astrophysics Data System (ADS)

    Pohl, Karsten

    2009-03-01

    The controlled fabrication of functional wafer-based nano-arrays is one of the ultimate quests in current nanotechnologies. Well-ordered misfit dislocation networks of ultrathin metal films are viable candidates for the growth of two- dimensional ordered cluster arrays in the nanometer regime. Such bottom-up processes can be very complex, involving collective effects from a large number of atoms. Unraveling the fundamental forces that drive these self-assembly processes requires detailed experimental information at the atomic level of large ensembles of hundreds to thousands of atoms. The combination of variable temperature measurements from our home-built STM correlated with 2D Frenkel-Kontorova models based on first-principle interaction parameters is used to explain how uniform arrays can form with the strain in the thin film as the driving force responsible for the surface self-assembly process. This process is generally applicable to assemble many molecular species thus opening avenues towards complex self-assembled structures based on a lock-and-key type approach. Moreover, when increasing the molecular coverage and/or decreasing the strain in the thin film the intermolecular interactions will eventually dominate the elastic effects and dictate the self-assembly process via molecular structure and functionality. We will show that controlling this delicate balance leads to a richness of structures, ranging from disperse ordered arrays of molecular clusters to patterned self-assembled monolayers (SAMs) of functionalized fullerenes and methanethiol.

  4. Molecular network topology and reliability for multipurpose diagnosis

    PubMed Central

    Jalil, MA; Moongfangklang, N; Innate, K; Mitatha, S; Ali, J; Yupapin, PP

    2011-01-01

    This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects). Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used. PMID:22072875

  5. GINI: From ISH Images to Gene Interaction Networks

    PubMed Central

    Puniyani, Kriti; Xing, Eric P.

    2013-01-01

    Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and

  6. The Networking of Interactive Bibliographic Retrieval Systems.

    ERIC Educational Resources Information Center

    Marcus, Richard S.; Reintjes, J. Francis

    Research in networking of heterogeneous interactive bibliographic retrieval systems is being conducted which centers on the concept of a virtual retrieval system. Such a virtual system would be created through a translating computer interface that would provide access to the different retrieval systems and data bases in a uniform and convenient…

  7. Swelling molecular entanglement networks in polymer glasses.

    PubMed

    McGraw, Joshua D; Dalnoki-Veress, Kari

    2010-08-01

    Entanglements in a polymer network are like knots between the polymer chains, and they are at the root of many phenomena observed in polymer systems. When a polymer glass is strained, cracklike deformations called crazes may be formed and the study of these regions can reveal much about the nature of entanglements. We have studied crazes in systems that are blends of long polymer chains diluted with chains of various small molecular weights. The range of diluting chain lengths is such that a fraction of them have conformations leading to entanglements. It has been found that a system with more short chains added acts like one in which the entanglement density is smaller than that in an undiluted system. We propose a model that quantitatively predicts the density of effective entanglements of a polydisperse system of polymer chains which is consistent with our experimental data. PMID:20866829

  8. Chain networking revealed by molecular dynamics simulation

    NASA Astrophysics Data System (ADS)

    Zheng, Yexin; Tsige, Mesfin; Wang, Shi-Qing

    Based on Kremer-Grest model for entangled polymer melts, we demonstrate how the response of a polymer glass depends critically on the chain length. After quenching two melts of very different chain lengths (350 beads per chain and 30 beads per chain) into deeply glassy states, we subject them to uniaxial extension. Our MD simulations show that the glass of long chains undergoes stable necking after yielding whereas the system of short chains is unable to neck and breaks up after strain localization. During ductile extension of the polymer glass made of long chain significant chain tension builds up in the load-bearing strands (LBSs). Further analysis is expected to reveal evidence of activation of the primary structure during post-yield extension. These results lend support to the recent molecular model 1 and are the simulations to demonstrate the role of chain networking. This work is supported, in part, by a NSF Grant (DMR-EAGER-1444859)

  9. Screened Electrostatic Interactions in Molecular Mechanics.

    PubMed

    Wang, Bo; Truhlar, Donald G

    2014-10-14

    In a typical application of molecular mechanics (MM), the electrostatic interactions are calculated from parametrized partial atomic charges treated as point charges interacting by radial Coulomb potentials. This does not usually yield accurate electrostatic interactions at van der Waals distances, but this is compensated by additional parametrized terms, for example Lennard-Jones potentials. In the present work, we present a scheme involving radial screened Coulomb potentials that reproduces the accurate electrostatics much more accurately. The screening accounts for charge penetration of one subsystem's charge cloud into that of another subsystem, and it is incorporated into the interaction potential in a way similar to what we proposed in a previous article (J. Chem. Theory Comput. 2010, 6, 3330) for combined quantum mechanical and molecular mechanical (QM/MM) simulations, but the screening parameters are reoptimized for MM. The optimization is carried out with electrostatic-potential-fitted partial atomic charges, but the optimized parameters should be useful with any realistic charge model. In the model we employ, the charge density of an atom is approximated as the sum of a point charge representing the nucleus and inner electrons and a smeared charge representing the outermost electrons; in particular, for all atoms except hydrogens, the smeared charge represents the two outermost electrons in the present model. We find that the charge penetration effect can cause very significant deviations from the popular point-charge model, and by comparison to electrostatic interactions calculated by symmetry-adapted perturbation theory, we find that the present results are considerably more accurate than point-charge electrostatic interactions. The mean unsigned error in electrostatics for a large and diverse data set (192 interaction energies) decreases from 9.2 to 3.3 kcal/mol, and the error in the electrostatics for 10 water dimers decreases from 1.7 to 0.5 kcal

  10. Molecular mechanisms of membrane interaction at implantation.

    PubMed

    Davidson, Lien M; Coward, Kevin

    2016-03-01

    Successful pregnancy is dependent upon the implantation of a competent embryo into a receptive endometrium. Despite major advancement in our understanding of reproductive medicine over the last few decades, implantation failure still occurs in both normal pregnancies and those created artificially by assisted reproductive technology (ART). Consequently, there is significant interest in elucidating the etiology of implantation failure. The complex multistep process of implantation begins when the developing embryo first makes contact with the plasma membrane of epithelial cells within the uterine environment. However, although this biological interaction marks the beginning of a fundamental developmental process, our knowledge of the intricate physiological and molecular processes involved remains sparse. In this synopsis, we aim to provide an overview of our current understanding of the morphological changes which occur to the plasma membrane of the uterine endothelium, and the molecular mechanisms that control communication between the early embryo and the endometrium during implantation. A multitude of molecular factors have been implicated in this complex process, including endometrial integrins, extracellular matrix molecules, adhesion molecules, growth factors, and ion channels. We also explore the development of in vitro models for embryo implantation to help researchers investigate mechanisms which may underlie implantation failure. Understanding the precise molecular pathways associated with implantation failure could help us to generate new prognostic/diagnostic biomarkers, and may identify novel therapeutic targets. PMID:26969610

  11. Mathematical inference and control of molecular networks from perturbation experiments

    NASA Astrophysics Data System (ADS)

    Mohammed-Rasheed, Mohammed

    in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five

  12. Functional module identification in protein interaction networks by interaction patterns

    PubMed Central

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP2 by random walk. A spectral approximate algorithm SLCP2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP2 to a new algorithm (greedy algorithm for LCP2) GLCP2 to identify overlapping functional modules. We compare SLCP2 and GLCP2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/∼xqian/fmi/slcp2hop

  13. Spin–orbit interaction mediated molecular dissociation

    SciTech Connect

    Kokkonen, E. Jänkälä, K.; Kettunen, J. A.; Heinäsmäki, S.; Karpenko, A.; Huttula, M.; Löytynoja, T.

    2014-05-14

    The effect of the spin–orbit interaction to photofragmentation is investigated in the mercury(II) bromide (HgBr{sub 2}) molecule. Changes in the fragmentation between the two spin–orbit components of Hg 5d photoionization, as well as within the molecular-field-splitted levels of these components are observed. Dissociation subsequent to photoionization is studied with synchrotron radiation and photoelectron-photoion coincidence spectroscopy. The experimental results are accompanied by relativistic ab initio analysis of the photoelectron spectrum.

  14. Evolutionarily Conserved Herpesviral Protein Interaction Networks

    PubMed Central

    Fossum, Even; Friedel, Caroline C.; Rajagopala, Seesandra V.; Titz, Björn; Baiker, Armin; Schmidt, Tina; Kraus, Theo; Stellberger, Thorsten; Rutenberg, Christiane; Suthram, Silpa; Bandyopadhyay, Sourav; Rose, Dietlind; von Brunn, Albrecht; Uhlmann, Mareike; Zeretzke, Christine; Dong, Yu-An; Boulet, Hélène; Koegl, Manfred; Bailer, Susanne M.; Koszinowski, Ulrich; Ideker, Trey; Uetz, Peter; Zimmer, Ralf; Haas, Jürgen

    2009-01-01

    Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species. PMID:19730696

  15. Molecular Determinants in Phagocyte-Bacteria Interactions.

    PubMed

    Kaufmann, Stefan H E; Dorhoi, Anca

    2016-03-15

    Phagocytes are crucial for host defense against bacterial pathogens. As first demonstrated by Metchnikoff, neutrophils and mononuclear phagocytes share the capacity to engulf, kill, and digest microbial invaders. Generally, neutrophils focus on extracellular, and mononuclear phagocytes on intracellular, pathogens. Reciprocally, extracellular pathogens often capitalize on hindering phagocytosis and killing of phagocytes, whereas intracellular bacteria frequently allow their engulfment and then block intracellular killing. As foreseen by Metchnikoff, phagocytes become highly versatile by acquiring diverse phenotypes, but still retaining some plasticity. Further, phagocytes engage in active crosstalk with parenchymal and immune cells to promote adjunctive reactions, including inflammation, tissue healing, and remodeling. This dynamic network allows the host to cope with different types of microbial invaders. Here we present an update of molecular and cellular mechanisms underlying phagocyte functions in antibacterial defense. We focus on four exemplary bacteria ranging from an opportunistic extracellular to a persistent intracellular pathogen. PMID:26982355

  16. 2010 Atomic & Molecular Interactions Gordon Research Conference

    SciTech Connect

    Todd Martinez

    2010-07-23

    The Atomic and Molecular Interactions Gordon Conferences is justifiably recognized for its broad scope, touching on areas ranging from fundamental gas phase and gas-condensed matter collision dynamics, to laser-molecule interactions, photophysics, and unimolecular decay processes. The meeting has traditionally involved scientists engaged in fundamental research in gas and condensed phases and those who apply these concepts to systems of practical chemical and physical interest. A key tradition in this meeting is the strong mixing of theory and experiment throughout. The program for 2010 conference continues these traditions. At the 2010 AMI GRC, there will be talks in 5 broadly defined and partially overlapping areas of intermolecular interactions and chemical dynamics: (1) Photoionization and Photoelectron Dynamics; (2) Quantum Control and Molecules in Strong Fields; (3) Photochemical Dynamics; (4) Complex Molecules and Condensed Phases; and (5) Clusters and Reaction Dynamics. These areas encompass many of the most productive and exciting areas of chemical physics, including both reactive and nonreactive processes, intermolecular and intramolecular energy transfer, and photodissociation and unimolecular processes. Gas phase dynamics, van der Waals and cluster studies, laser-matter interactions and multiple potential energy surface phenomena will all be discussed.

  17. Cooperative Tertiary Interaction Network Guides RNA Folding

    SciTech Connect

    Behrouzi, Reza; Roh, Joon Ho; Kilburn, Duncan; Briber, R.M.; Woodson, Sarah A.

    2013-04-08

    Noncoding RNAs form unique 3D structures, which perform many regulatory functions. To understand how RNAs fold uniquely despite a small number of tertiary interaction motifs, we mutated the major tertiary interactions in a group I ribozyme by single-base substitutions. The resulting perturbations to the folding energy landscape were measured using SAXS, ribozyme activity, hydroxyl radical footprinting, and native PAGE. Double- and triple-mutant cycles show that most tertiary interactions have a small effect on the stability of the native state. Instead, the formation of core and peripheral structural motifs is cooperatively linked in near-native folding intermediates, and this cooperativity depends on the native helix orientation. The emergence of a cooperative interaction network at an early stage of folding suppresses nonnative structures and guides the search for the native state. We suggest that cooperativity in noncoding RNAs arose from natural selection of architectures conducive to forming a unique, stable fold.

  18. Allele-Specific Behavior of Molecular Networks: Understanding Small-Molecule Drug Response in Yeast

    PubMed Central

    Li, Chunquan; Hao, Dapeng; Zhang, Shaojun; Zhou, Meng; Su, Fei; Chen, Xi; Zhi, Hui; Li, Xia

    2013-01-01

    The study of systems genetics is changing the way the genetic and molecular basis of phenotypic variation, such as disease susceptibility and drug response, is being analyzed. Moreover, systems genetics aids in the translation of insights from systems biology into genetics. The use of systems genetics enables greater attention to be focused on the potential impact of genetic perturbations on the molecular states of networks that in turn affects complex traits. In this study, we developed models to detect allele-specific perturbations on interactions, in which a genetic locus with alternative alleles exerted a differing influence on an interaction. We utilized the models to investigate the dynamic behavior of an integrated molecular network undergoing genetic perturbations in yeast. Our results revealed the complexity of regulatory relationships between genetic loci and networks, in which different genetic loci perturb specific network modules. In addition, significant within-module functional coherence was found. We then used the network perturbation model to elucidate the underlying molecular mechanisms of individual differences in response to 100 diverse small molecule drugs. As a result, we identified sub-networks in the integrated network that responded to variations in DNA associated with response to diverse compounds and were significantly enriched for known drug targets. Literature mining results provided strong independent evidence for the effectiveness of these genetic perturbing networks in the elucidation of small-molecule responses in yeast. PMID:23308257

  19. Molecular networks in skeletal muscle plasticity.

    PubMed

    Hoppeler, Hans

    2016-01-01

    The skeletal muscle phenotype is subject to considerable malleability depending on use as well as internal and external cues. In humans, low-load endurance-type exercise leads to qualitative changes of muscle tissue characterized by an increase in structures supporting oxygen delivery and consumption, such as capillaries and mitochondria. High-load strength-type exercise leads to growth of muscle fibers dominated by an increase in contractile proteins. In endurance exercise, stress-induced signaling leads to transcriptional upregulation of genes, with Ca(2+) signaling and the energy status of the muscle cells sensed through AMPK being major input determinants. Several interrelated signaling pathways converge on the transcriptional co-activator PGC-1α, perceived to be the coordinator of much of the transcriptional and post-transcriptional processes. Strength training is dominated by a translational upregulation controlled by mTORC1. mTORC1 is mainly regulated by an insulin- and/or growth-factor-dependent signaling cascade as well as mechanical and nutritional cues. Muscle growth is further supported by DNA recruitment through activation and incorporation of satellite cells. In addition, there are several negative regulators of muscle mass. We currently have a good descriptive understanding of the molecular mechanisms controlling the muscle phenotype. The topology of signaling networks seems highly conserved among species, with the signaling outcome being dependent on the particular way individual species make use of the options offered by the multi-nodal networks. As a consequence, muscle structural and functional modifications can be achieved by an almost unlimited combination of inputs and downstream signaling events. PMID:26792332

  20. The activation of interactive attentional networks.

    PubMed

    Xuan, Bin; Mackie, Melissa-Ann; Spagna, Alfredo; Wu, Tingting; Tian, Yanghua; Hof, Patrick R; Fan, Jin

    2016-04-01

    Attention can be conceptualized as comprising the functions of alerting, orienting, and executive control. Although the independence of these functions has been demonstrated, the neural mechanisms underlying their interactions remain unclear. Using the revised attention network test and functional magnetic resonance imaging, we examined cortical and subcortical activity related to these attentional functions and their interactions. Results showed that areas in the extended frontoparietal network (FPN), including dorsolateral prefrontal cortex, frontal eye fields (FEF), areas near and along the intraparietal sulcus, anterior cingulate and anterior insular cortices, basal ganglia, and thalamus were activated across multiple attentional functions. Specifically, the alerting function was associated with activation in the locus coeruleus (LC) in addition to regions in the FPN. The orienting functions were associated with activation in the superior colliculus (SC) and the FEF. The executive control function was mainly associated with activation of the FPN and cerebellum. The interaction effect of alerting by executive control was also associated with activation of the FPN, while the interaction effect of orienting validity by executive control was mainly associated with the activation in the pulvinar. The current findings demonstrate that cortical and specific subcortical areas play a pivotal role in the implementation of attentional functions and underlie their dynamic interactions. PMID:26794640

  1. Network integration and graph analysis in mammalian molecular systems biology

    PubMed Central

    Ma'ayan, A.

    2009-01-01

    Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks. PMID:19045817

  2. Dynamics of interacting information waves in networks.

    PubMed

    Mirshahvalad, A; Esquivel, A V; Lizana, L; Rosvall, M

    2014-01-01

    To better understand the inner workings of information spreading, network researchers often use simple models to capture the spreading dynamics. But most models only highlight the effect of local interactions on the global spreading of a single information wave, and ignore the effects of interactions between multiple waves. Here we take into account the effect of multiple interacting waves by using an agent-based model in which the interaction between information waves is based on their novelty. We analyzed the global effects of such interactions and found that information that actually reaches nodes reaches them faster. This effect is caused by selection between information waves: lagging waves die out and only leading waves survive. As a result, and in contrast to models with noninteracting information dynamics, the access to information decays with the distance from the source. Moreover, when we analyzed the model on various synthetic and real spatial road networks, we found that the decay rate also depends on the path redundancy and the effective dimension of the system. In general, the decay of the information wave frequency as a function of distance from the source follows a power-law distribution with an exponent between -0.2 for a two-dimensional system with high path redundancy and -0.5 for a tree-like system with no path redundancy. We found that the real spatial networks provide an infrastructure for information spreading that lies in between these two extremes. Finally, to better understand the mechanics behind the scaling results, we provide analytical calculations of the scaling for a one-dimensional system. PMID:24580283

  3. Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

    PubMed Central

    Shen, Ru; Wang, Xiaosheng; Guda, Chittibabu

    2015-01-01

    Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. PMID:26495282

  4. Neural Network predictions of Diatomic and Triatomic Molecular Data

    NASA Astrophysics Data System (ADS)

    Blake Laing, W.

    1997-11-01

    The arrangement of molecules in periodic systems offers an enhanced comprehension of trends in molecular properties, a more efficient method of sorting and searching of molecular databases, and bases for the prediction of new data. Neural networks have the ability to "learn" existing data and to forecast a large amount of new data without a smoothing equation.(R. Hefferlin, B. Davis, W. B. Laing, "The Learning and Prediction of Triatomic Molecular Data with Neural Networks," International Arctic Seminar 1997, Murmansk, Russia)(J. Wohlers, W. B. Laing, R. Hefferlin, and B. Daivs, "Least-Squares and Neural-Network Forecasting from Citical Data: Diatomic Molecular Internuclear Separations and Triatomic Heats of Atomization and Ionization Potentials," Advances in Molecular Similarity: JIA book series, in press) This report will present periodic systems of molecules as well as neural network predictions for additional properties of diatomic and triatomic molecules.

  5. Interaction prediction using conserved network motifs in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2005-03-01

    High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. This talk will describe the recent developments in analyzing and understanding protein interaction networks, then present a method that uses the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ``leave-one-ou approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org

  6. Cellular automata with object-oriented features for parallel molecular network modeling.

    PubMed

    Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

    2005-06-01

    Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented. PMID:16117022

  7. Spatially-Interactive Biomolecular Networks Organized by Nucleic Acid Nanostructures

    PubMed Central

    Fu, Jinglin; Liu, Minghui; Liu, Yan; Yan, Hao

    2013-01-01

    Conspectus Living systems have evolved a variety of nanostructures to control the molecular interactions that mediate many functions including the recognition of targets by receptors, the binding of enzymes to substrates, and the regulation of enzymatic activity. Mimicking these structures outside of the cell requires methods that offer nanoscale control over the organization of individual network components. Advances in DNA nanotechnology have enabled the design and fabrication of sophisticated one-, two- and three-dimensional (1D, 2D and 3D) nanostructures that utilize spontaneous and sequence specific DNA hybridization. Compared to other self-assembling biopolymers, DNA nanostructures offer predictable and programmable interactions, and surface features to which other nanoparticles and bio-molecules can be precisely positioned. The ability to control the spatial arrangement of the components while constructing highly-organized networks will lead to various applications of these systems. For example, DNA nanoarrays with surface displays of molecular probes can sense noncovalent hybridization interactions with DNA, RNA, and proteins and covalent chemical reactions. DNA nanostructures can also align external molecules into well-defined arrays, which may improve the resolution of many structural determination methods, such as X-ray diffraction, cryo-EM, NMR, and super-resolution fluorescence. Moreover, by constraining target entities to specific conformations, self-assembled DNA nanostructures can serve as molecular rulers to evaluate conformation-dependent activities. This Account describes the most recent advances in the DNA nanostructure directed assembly of biomolecular networks and explores the possibility of applying this technology to other fields of study. Recently, several reports have demonstrated the DNA nanostructure directed assembly of spatially-interactive biomolecular networks. For example, researchers have constructed synthetic multi-enzyme cascades

  8. Molecular networks of human muscle adaptation to exercise and age.

    PubMed

    Phillips, Bethan E; Williams, John P; Gustafsson, Thomas; Bouchard, Claude; Rankinen, Tuomo; Knudsen, Steen; Smith, Kenneth; Timmons, James A; Atherton, Philip J

    2013-03-01

    Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%), and when applying Ingenuity Pathway Analysis (IPA) up-stream analysis to ~580 genes that co-varied with gain in lean mass, we identified rapamycin (mTOR) signaling associating with growth (P = 1.4 × 10(-30)). Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. Differential analysis found networks mimicking developmental processes (activated all-trans-retinoic acid (ATRA, Z-score = 4.5; P = 6 × 10(-13)) and inhibited aryl-hydrocarbon receptor signaling (AhR, Z-score = -2.3; P = 3 × 10(-7))) with RET. Intriguingly, as ATRA and AhR gene-sets were also a feature of endurance exercise training (EET), they appear to represent "generic" physical activity responsive gene-networks. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals. Instead, utilizing two independent cohorts (n = 45 and n = 52), with a continuum of subject ages (18-78 y), the first reproducible set of age-related transcripts in human muscle was identified. This analysis identified ~500 genes highly enriched in post-transcriptional processes (P = 1 × 10(-6)) and with negligible links to the aforementioned generic exercise regulated gene-sets and some overlap with ribosomal genes. The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR activation were significantly related to

  9. Molecular Networks of Human Muscle Adaptation to Exercise and Age

    PubMed Central

    Phillips, Bethan E.; Williams, John P.; Gustafsson, Thomas; Bouchard, Claude; Rankinen, Tuomo; Knudsen, Steen; Smith, Kenneth

    2013-01-01

    Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%), and when applying Ingenuity Pathway Analysis (IPA) up-stream analysis to ∼580 genes that co-varied with gain in lean mass, we identified rapamycin (mTOR) signaling associating with growth (P = 1.4×10−30). Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. Differential analysis found networks mimicking developmental processes (activated all-trans-retinoic acid (ATRA, Z-score = 4.5; P = 6×10−13) and inhibited aryl-hydrocarbon receptor signaling (AhR, Z-score = −2.3; P = 3×10−7)) with RET. Intriguingly, as ATRA and AhR gene-sets were also a feature of endurance exercise training (EET), they appear to represent “generic” physical activity responsive gene-networks. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals. Instead, utilizing two independent cohorts (n = 45 and n = 52), with a continuum of subject ages (18–78 y), the first reproducible set of age-related transcripts in human muscle was identified. This analysis identified ∼500 genes highly enriched in post-transcriptional processes (P = 1×10−6) and with negligible links to the aforementioned generic exercise regulated gene-sets and some overlap with ribosomal genes. The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR

  10. Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases

    PubMed Central

    2012-01-01

    Background The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii). Results Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer. Conclusions We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition

  11. Molecular interactions with ice: Molecular embedding, adsorption, detection, and release

    SciTech Connect

    Gibson, K. D.; Langlois, Grant G.; Li, Wenxin; Sibener, S. J.; Killelea, Daniel R.

    2014-11-14

    The interaction of atomic and molecular species with water and ice is of fundamental importance for chemistry. In a previous series of publications, we demonstrated that translational energy activates the embedding of Xe and Kr atoms in the near surface region of ice surfaces. In this paper, we show that inert molecular species may be absorbed in a similar fashion. We also revisit Xe embedding, and further probe the nature of the absorption into the selvedge. CF{sub 4} molecules with high translational energies (≥3 eV) were observed to embed in amorphous solid water. Just as with Xe, the initial adsorption rate is strongly activated by translational energy, but the CF{sub 4} embedding probability is much less than for Xe. In addition, a larger molecule, SF{sub 6}, did not embed at the same translational energies that both CF{sub 4} and Xe embedded. The embedding rate for a given energy thus goes in the order Xe > CF{sub 4} > SF{sub 6}. We do not have as much data for Kr, but it appears to have a rate that is between that of Xe and CF{sub 4}. Tentatively, this order suggests that for Xe and CF{sub 4}, which have similar van der Waals radii, the momentum is the key factor in determining whether the incident atom or molecule can penetrate deeply enough below the surface to embed. The more massive SF{sub 6} molecule also has a larger van der Waals radius, which appears to prevent it from stably embedding in the selvedge. We also determined that the maximum depth of embedding is less than the equivalent of four layers of hexagonal ice, while some of the atoms just below the ice surface can escape before ice desorption begins. These results show that energetic ballistic embedding in ice is a general phenomenon, and represents a significant new channel by which incident species can be trapped under conditions where they would otherwise not be bound stably as surface adsorbates. These findings have implications for many fields including environmental science, trace gas

  12. CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers.

    PubMed

    Nepomnyachiy, Sergey; Ben-Tal, Nir; Kolodny, Rachel

    2015-05-01

    It can be informative to view biological data, e.g., protein-protein interactions within a large complex, in a network representation coupled with three-dimensional structural visualizations of individual molecular entities. CyToStruct, introduced here, provides a transparent interface between the Cytoscape platform for network analysis and molecular viewers, including PyMOL, UCSF Chimera, VMD, and Jmol. CyToStruct launches and passes scripts to molecular viewers from the network's edges and nodes. We provide demonstrations to analyze interactions among subunits in large protein/RNA/DNA complexes, and similarities among proteins. CyToStruct enriches the network tools of Cytoscape by adding a layer of structural analysis, offering all capabilities implemented in molecular viewers. CyToStruct is available at https://bitbucket.org/sergeyn/cytostruct/wiki/Home and in the Cytoscape App Store. Given the coordinates of a molecular complex, our web server (http://trachel-srv.cs.haifa.ac.il/rachel/ppi/) automatically generates all files needed to visualize the complex as a Cytoscape network with CyToStruct bridging to PyMOL, UCSF Chimera, VMD, and Jmol. PMID:25865247

  13. Molecular Recognition and Specific Interactions for Biosensing Applications

    PubMed Central

    Kim, Dong Chung; Kang, Dae Joon

    2008-01-01

    Molecular recognition and specific interactions are reliable and versatile routes for site-specific and well-oriented immobilization of functional biomolecules on surfaces. The control of surface properties via the molecular recognition and specific interactions at the nanoscale is a key element for the nanofabrication of biosensors with high sensitivity and specificity. This review intends to provide a comprehensive understanding of the molecular recognition- and specific interaction-mediated biosensor fabrication routes that leads to biosensors with well-ordered and controlled structures on both nanopatterned surfaces and nanomaterials. Herein self-assembly of the biomolecules via the molecular recognition and specific interactions on nanoscaled surfaces as well as nanofabrication techniques of the biomolecules for biosensor architecture are discussed. We also describe the detection of molecular recognition- and specific interaction-mediated molecular binding as well as advantages of nanoscale detection.

  14. Computer-Based Semantic Network in Molecular Biology: A Demonstration.

    ERIC Educational Resources Information Center

    Callman, Joshua L.; And Others

    This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

  15. Interactive Data Mining for Molecular Graphs

    PubMed Central

    Yılmaz, Burcu; Göktürk, Mehmet

    2009-01-01

    Designing new medical drugs for a specific disease requires extensive analysis of many molecules that have an activity for the disease. The main goal of these extensive analyses is to discover substructures (fragments) that account for the activity of these molecules. Once they are discovered, these fragments are used to understand the structure of new drugs and design new medicines for the disease. In this paper, we propose an interactive approach for visual molecule mining to discover fragments of molecules that are responsible for the desired activity with respect to a specific disease. Our approach visualizes molecular data in a form that can be interpreted by a human expert. Using a pipelining structure, it enables experts to contribute to the solution with their expertise at different levels. In order to derive desired fragments, it combines histogram-based filtering and clustering methods in a novel way. This combination enables a flexible determination of frequent fragments that repeat in molecules exactly or with some variations. PMID:20052387

  16. Differential molecular interactions between the crystalline and the amorphous phases of celecoxib.

    PubMed

    Gupta, Piyush; Thilagavathi, R; Chakraborti, Asit K; Bansal, Arvind K

    2005-10-01

    We have investigated the differences in molecular interactions between the crystalline (ordered) and amorphous (disordered) phase of a poorly soluble drug, celecoxib. Molecular interactions in the crystalline phase were investigated with the help of Mercury software, using single crystal X-ray diffractometric data for celecoxib. A simulated annealing molecular dynamics approach was used for the assessment of altered molecular interactions in the amorphous phase. Crystalline celecoxib was found to contain an ordered network of H-bonding between all its electron donors (-S=O group, 2-N of pyrazole ring and -C-F) and the acceptor (-N-H). Amorphous celecoxib retained all these interactions in its disordered molecular arrangement, with a relatively stronger H-bonding between the interacting groups, as compared with crystalline celecoxib. However, these inter-molecular interactions differed in strength in the two solid-state forms. The altered configurations of the molecular arrangement in the two phases were supported by the shifts observed in the Fourier-transform infra-red vibrational spectra of respective states. These interactions could have strong implications on devitrification kinetics of amorphous celecoxib, and could further guide the choice of stabilizers for the amorphous form. PMID:16259755

  17. Single Molecular Film for Recognizing Biological Molecular Interaction: DNA-Protein Interaction and Enzyme Reaction

    NASA Astrophysics Data System (ADS)

    Kurihara, Kazue

    Protein-protein and protein-substrate interactions play essential roles in biological functions. Surface forces measurement and atomic force microscopy, which directly measure the interaction forces as a function of the surface separation, enable us to quantitatively evaluate these interactions [1-3]. We have employed the surface forces measurement [4] and colloidal probe atomic force microscopy [5] to study interactions involved in specific molecular recognition of DNA-protein and enzyme-substrate reaction. Studied are interactions between nucleic acid bases (adenine and thymine) [6], Spo0A-DB (the DNA-binding site of a transcription factor Spo0A), and DNA [7,8], those between subunits I and II of heptaprenyl diphosphate (HepPP) synthase in the presence of a substrate ((E,E)-farnesyl diphosphate, FPP) and a cofactor (Mg2+) [9-11], and the selectivity of the substrates in this enzymatic reaction [12]. Keys of our approach are the preparation of well-defined samples and the appropriate analysis. We have modified he substrate surfaces with these proteins using the Langmuir-Blodgett (LB) method. This chapter reviews the LB modification method and subsequent demonstrations of biological specific interactions employing this approach.

  18. The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

    PubMed

    Hermjakob, Henning; Montecchi-Palazzi, Luisa; Bader, Gary; Wojcik, Jérôme; Salwinski, Lukasz; Ceol, Arnaud; Moore, Susan; Orchard, Sandra; Sarkans, Ugis; von Mering, Christian; Roechert, Bernd; Poux, Sylvain; Jung, Eva; Mersch, Henning; Kersey, Paul; Lappe, Michael; Li, Yixue; Zeng, Rong; Rana, Debashis; Nikolski, Macha; Husi, Holger; Brun, Christine; Shanker, K; Grant, Seth G N; Sander, Chris; Bork, Peer; Zhu, Weimin; Pandey, Akhilesh; Brazma, Alvis; Jacq, Bernard; Vidal, Marc; Sherman, David; Legrain, Pierre; Cesareni, Gianni; Xenarios, Ioannis; Eisenberg, David; Steipe, Boris; Hogue, Chris; Apweiler, Rolf

    2004-02-01

    A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany). PMID:14755292

  19. Site-directed deep electronic tunneling through a molecular network

    SciTech Connect

    Caspary, Maytal; Peskin, Uri

    2005-10-15

    Electronic tunneling in a complex molecular network of N(>2) donor/acceptor sites, connected by molecular bridges, is analyzed. The 'deep' tunneling dynamics is formulated using a recursive perturbation expansion, yielding a McConnell-type reduced N-level model Hamiltonian. Applications to models of molecular junctions demonstrate that the donor-bridge contact parameters can be tuned in order to control the tunneling dynamics and particularly to direct the tunneling pathway to either one of the various acceptors.

  20. Mass Spectral Molecular Networking of Living Microbial Colonies

    SciTech Connect

    Watrous, Jeramie D.; Roach, Patrick J.; Alexandrov, Theodore; Heath, Brandi S.; Yang, Jane Y.; Kersten, Roland; vander Voort, Menno; Pogliano, Kit; Gross, Harald; Raaijmakers, Jos M.; Moore, Bradley S.; Laskin, Julia; Bandeira, Nuno; Dorrestein, Pieter C.

    2012-06-26

    Integrating the governing chemistry with the genomics and phenotypes of microbial colonies has been a "holy grail" in microbiology. This work describes a highly sensitive, broadly applicable, and costeffective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample preparation. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and molecular networking, enabled monitoring of metabolite production from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. This work demonstrates that, by using these tools to visualize small molecular changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this experimental platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi [R. Mendes et al. (2011) Science 332:1097–1100]. The antifungal effect of strain SHC52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. Our technology, in combination with our recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is amonochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in our ability to understand the spatiotemporal dynamics of metabolite production in live microbial colonies and communities.

  1. Chemoinformatics Approach for Building Molecular Networks from Marine Organisms.

    PubMed

    Karthikeyan, Muthukumarasamy; Nimje, Deepika; Pahujani, Rakhi; Tyagi, Kushal; Bapat, Sanket; Vyas, Renu; Pillai Padmakumar, Krishna

    2015-01-01

    Natural products obtained from marine sources are considered to be a rich and diverse source of potential drugs. In the present work we demonstrate the use of chemoinformatics approach for the design of new molecules inspired by molecules from marine organisms. Accordingly we have assimilated information from two major scientific domains namely chemoinformatics and biodiversity informatics to develop an interactive marine database named MIMMO (Medicinally Important Molecules from Marine Organisms). The database can be queried for species, molecules, scaffolds, drugs, diseases and associated cumulative biological activity spectrum along with links to the literature resources. Molecular informatics analysis of the molecules obtained from MIMMO was performed to study their chemical space. The distinct skeletal features of the biologically active compounds isolated from marine species were identified. Scaffold molecules and species networks were created to identify common scaffolds from marine source and drug space. An analysis of the entire molecular data revealed a unique list of around 2000 molecules from which ten most frequently occurring distinct scaffolds were obtained. PMID:26138570

  2. TeloPIN: a database of telomeric proteins interaction network in mammalian cells.

    PubMed

    Luo, Zhenhua; Dai, Zhiming; Xie, Xiaowei; Feng, Xuyang; Liu, Dan; Songyang, Zhou; Xiong, Yuanyan

    2015-01-01

    Interaction network surrounding telomeres has been intensively studied during the past two decades. However, no specific resource by integrating telomere interaction information data is currently available. To facilitate the understanding of the molecular interaction network by which telomeres are associated with biological process and diseases, we have developed TeloPIN (Telomeric Proteins Interaction Network) database (http://songyanglab.sysu.edu.cn/telopin/), a novel database that points to provide comprehensive information on protein-protein, protein-DNA and protein-RNA interaction of telomeres. TeloPIN database contains four types of interaction data, including (i) protein--protein interaction (PPI) data, (ii) telomeric proteins ChIP-seq data, (iii) telomere-associated proteins data and (iv) telomeric repeat-containing RNAs (TERRA)-interacting proteins data. By analyzing these four types of interaction data, we found that 358 and 199 proteins have more than one type of interaction information in human and mouse cells, respectively. We also developed table browser and TeloChIP genome browser to help researchers with better integrated visualization of interaction data from different studies. The current release of TeloPIN database includes 1111 PPI, eight telomeric protein ChIP-seq data sets, 1391 telomere-associated proteins and 183 TERRA-interacting proteins from 92 independent studies in mammalian cells. The interaction information provided by TeloPIN database will greatly expand our knowledge of telomeric proteins interaction network. PMID:25792605

  3. Entropy bounds for hierarchical molecular networks.

    PubMed

    Dehmer, Matthias; Borgert, Stephan; Emmert-Streib, Frank

    2008-01-01

    In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of non-hierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of non-hierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis. PMID:18769487

  4. Analyzing milestoning networks for molecular kinetics: Definitions, algorithms, and examples

    NASA Astrophysics Data System (ADS)

    Viswanath, Shruthi; Kreuzer, Steven M.; Cardenas, Alfredo E.; Elber, Ron

    2013-11-01

    Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

  5. Mesoscale molecular network formation in amorphous organic materials

    PubMed Central

    Savoie, Brett M.; Kohlstedt, Kevin L.; Jackson, Nicholas E.; Chen, Lin X.; Olvera de la Cruz, Monica; Schatz, George C.; Marks, Tobin J.; Ratner, Mark A.

    2014-01-01

    High-performance solution-processed organic semiconductors maintain macroscopic functionality even in the presence of microscopic disorder. Here we show that the functional robustness of certain organic materials arises from the ability of molecules to create connected mesoscopic electrical networks, even in the absence of periodic order. The hierarchical network structures of two families of important organic photovoltaic acceptors, functionalized fullerenes and perylene diimides, are analyzed using a newly developed graph methodology. The results establish a connection between network robustness and molecular topology, and also demonstrate that solubilizing moieties play a large role in disrupting the molecular networks responsible for charge transport. A clear link is established between the success of mono and bis functionalized fullerene acceptors in organic photovoltaics and their ability to construct mesoscopically connected electrical networks over length scales of 10 nm. PMID:24982179

  6. How to identify essential genes from molecular networks?

    PubMed Central

    del Rio, Gabriel; Koschützki, Dirk; Coello, Gerardo

    2009-01-01

    Background The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion. Results By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined. Conclusion The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes. PMID:19822021

  7. ANAP: An Integrated Knowledge Base for Arabidopsis Protein Interaction Network Analysis1[C][W][OA

    PubMed Central

    Wang, Congmao; Marshall, Alex; Zhang, Dabing; Wilson, Zoe A.

    2012-01-01

    Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http

  8. Molecular signatures of ovarian diseases: Insights from network medicine perspective.

    PubMed

    Kori, Medi; Gov, Esra; Arga, Kazim Yalcin

    2016-08-01

    Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor. PMID:27341345

  9. Formation Mechanism for a Hybrid Supramolecular Network Involving Cooperative Interactions

    NASA Astrophysics Data System (ADS)

    Mura, Manuela; Silly, Fabien; Burlakov, Victor; Castell, Martin R.; Briggs, G. Andrew D.; Kantorovich, Lev N.

    2012-04-01

    A novel mechanism of hybrid assembly of molecules on surfaces is proposed stemming from interactions between molecules and on-surface metal atoms which eventually got trapped inside the network pores. Based on state-of-the-art theoretical calculations, we find that the new mechanism relies on formation of molecule-metal atom pairs which, together with molecules themselves, participate in the assembly growth. Most remarkably, the dissociation of pairs is facilitated by a cooperative interaction involving many molecules. This new mechanism is illustrated on a low coverage Melamine hexagonal network on the Au(111) surface where multiple events of gold atoms trapping via a set of so-called “gate” transitions are found by kinetic Monte Carlo simulations based on transition rates obtained using ab initio density functional theory calculations and the nudged elastic band method. Simulated STM images of gold atoms trapped in the pores of the Melamine network predict that the atoms should appear as bright spots inside Melamine hexagons. No trapping was found at large Melamine coverages, however. These predictions have been supported by preliminary STM experiments which show bright spots inside Melamine hexagons at low Melamine coverages, while empty pores are mostly observed at large coverages. Therefore, we suggest that bright spots sometimes observed in the pores of molecular assemblies on metal surfaces may be attributed to trapped substrate metal atoms. We believe that this type of mechanism could be used for delivering adatom species of desired functionality (e.g., magnetic) into the pores of hydrogen-bonded networks serving as templates for their capture.

  10. On the sufficiency of pairwise interactions in maximum entropy models of networks

    NASA Astrophysics Data System (ADS)

    Nemenman, Ilya; Merchan, Lina

    Biological information processing networks consist of many components, which are coupled by an even larger number of complex multivariate interactions. However, analyses of data sets from fields as diverse as neuroscience, molecular biology, and behavior have reported that observed statistics of states of some biological networks can be approximated well by maximum entropy models with only pairwise interactions among the components. Based on simulations of random Ising spin networks with p-spin (p > 2) interactions, here we argue that this reduction in complexity can be thought of as a natural property of some densely interacting networks in certain regimes, and not necessarily as a special property of living systems. This work was supported in part by James S. McDonnell Foundation Grant No. 220020321.

  11. On the Sufficiency of Pairwise Interactions in Maximum Entropy Models of Networks

    NASA Astrophysics Data System (ADS)

    Merchan, Lina; Nemenman, Ilya

    2016-03-01

    Biological information processing networks consist of many components, which are coupled by an even larger number of complex multivariate interactions. However, analyses of data sets from fields as diverse as neuroscience, molecular biology, and behavior have reported that observed statistics of states of some biological networks can be approximated well by maximum entropy models with only pairwise interactions among the components. Based on simulations of random Ising spin networks with p-spin (p>2) interactions, here we argue that this reduction in complexity can be thought of as a natural property of densely interacting networks in certain regimes, and not necessarily as a special property of living systems. By connecting our analysis to the theory of random constraint satisfaction problems, we suggest a reason for why some biological systems may operate in this regime.

  12. Dynamic interactions of proteins in complex networks

    SciTech Connect

    Appella, E.; Anderson, C.

    2009-10-01

    Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact with the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

  13. Ribo-Proteomics Approach to Profile RNA-Protein and Protein-Protein Interaction Networks.

    PubMed

    Yeh, Hsin-Sung; Chang, Jae-Woong; Yong, Jeongsik

    2016-01-01

    Characterizing protein-protein and protein-RNA interaction networks is a fundamental step to understanding the function of an RNA-binding protein. In many cases, these interactions are transient and highly dynamic. Therefore, capturing stable as well as transient interactions in living cells for the identification of protein-binding partners and the mapping of RNA-binding sequences is key to a successful establishment of the molecular interaction network. In this chapter, we will describe a method for capturing the molecular interactions in living cells using formaldehyde as a crosslinker and enriching a specific RNA-protein complex from cell extracts followed by mass spectrometry and Next-Gen sequencing analyses. PMID:26965265

  14. Multiple tipping points and optimal repairing in interacting networks.

    PubMed

    Majdandzic, Antonio; Braunstein, Lidia A; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Stanley, H Eugene; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks-such as the human body with its biological networks or the global economic network consisting of regional clusters-often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two 'forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  15. Multiple tipping points and optimal repairing in interacting networks

    NASA Astrophysics Data System (ADS)

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-03-01

    Systems composed of many interacting dynamical networks--such as the human body with its biological networks or the global economic network consisting of regional clusters--often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two `forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.

  16. Theoretical analysis of dynamic processes for interacting molecular motors

    NASA Astrophysics Data System (ADS)

    Teimouri, Hamid; Kolomeisky, Anatoly B.; Mehrabiani, Kareem

    2015-02-01

    Biological transport is supported by the collective dynamics of enzymatic molecules that are called motor proteins or molecular motors. Experiments suggest that motor proteins interact locally via short-range potentials. We investigate the fundamental role of these interactions by carrying out an analysis of a new class of totally asymmetric exclusion processes, in which interactions are accounted for in a thermodynamically consistent fashion. This allows us to explicitly connect microscopic features of motor proteins with their collective dynamic properties. A theoretical analysis that combines various mean-field calculations and computer simulations suggests that the dynamic properties of molecular motors strongly depend on the interactions, and that the correlations are stronger for interacting motor proteins. Surprisingly, it is found that there is an optimal strength of interactions (weak repulsion) that leads to a maximal particle flux. It is also argued that molecular motor transport is more sensitive to attractive interactions. Applications of these results for kinesin motor proteins are discussed.

  17. Reconstructing direct and indirect interactions in networked public goods game

    PubMed Central

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  18. Reconstructing direct and indirect interactions in networked public goods game.

    PubMed

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  19. Reconstructing direct and indirect interactions in networked public goods game

    NASA Astrophysics Data System (ADS)

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-07-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

  20. Molecular and genetic inflammation networks in major human diseases.

    PubMed

    Zhao, Yongzhong; Forst, Christian V; Sayegh, Camil E; Wang, I-Ming; Yang, Xia; Zhang, Bin

    2016-07-19

    It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic

  1. NatalieQ: A web server for protein-protein interaction network querying

    PubMed Central

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks. We recently developed Natalie, which computes high-quality network alignments via advanced methods from combinatorial optimization. Results Here, we present NatalieQ, a web server for topology-based alignment of a specified query protein-protein interaction network to a selected target network using the Natalie algorithm. By incorporating similarity at both the sequence and the network level, we compute alignments that allow for the transfer of functional annotation as well as for the prediction of missing interactions. We illustrate the capabilities of NatalieQ with a biological case study involving the Wnt signaling pathway. Conclusions We show that topology-based network alignment can produce results complementary to those obtained by using sequence similarity alone. We also demonstrate that NatalieQ is able to predict putative interactions. The server is available at: http://www.ibi.vu.nl/programs/natalieq/. PMID:24690407

  2. VISUALIZATION OF MOLECULAR INTERACTIONS BY FLUORESCENCE COMPLEMENTATION

    PubMed Central

    Kerppola, Tom K.

    2008-01-01

    The visualization of protein complexes in living cells enables validation of protein interactions in their normal environment and determination of their subcellular localization. The bimolecular fluorescence complementation (BiFC) assay has been used to visualize interactions among multiple proteins in many cell types and organisms. This assay is based on the association between two fluorescent-protein fragments when they are brought together by an interaction between proteins fused to the fragments. Modified forms of this assay have been used to visualize the competition between alternative interaction partners and the covalent modification of proteins by ubiquitin family peptides. PMID:16625152

  3. Complex molecular assemblies at hand via interactive simulations.

    PubMed

    Delalande, Olivier; Férey, Nicolas; Grasseau, Gilles; Baaden, Marc

    2009-11-30

    Studying complex molecular assemblies interactively is becoming an increasingly appealing approach to molecular modeling. Here we focus on interactive molecular dynamics (IMD) as a textbook example for interactive simulation methods. Such simulations can be useful in exploring and generating hypotheses about the structural and mechanical aspects of biomolecular interactions. For the first time, we carry out low-resolution coarse-grain IMD simulations. Such simplified modeling methods currently appear to be more suitable for interactive experiments and represent a well-balanced compromise between an important gain in computational speed versus a moderate loss in modeling accuracy compared to higher resolution all-atom simulations. This is particularly useful for initial exploration and hypothesis development for rare molecular interaction events. We evaluate which applications are currently feasible using molecular assemblies from 1900 to over 300,000 particles. Three biochemical systems are discussed: the guanylate kinase (GK) enzyme, the outer membrane protease T and the soluble N-ethylmaleimide-sensitive factor attachment protein receptors complex involved in membrane fusion. We induce large conformational changes, carry out interactive docking experiments, probe lipid-protein interactions and are able to sense the mechanical properties of a molecular model. Furthermore, such interactive simulations facilitate exploration of modeling parameters for method improvement. For the purpose of these simulations, we have developed a freely available software library called MDDriver. It uses the IMD protocol from NAMD and facilitates the implementation and application of interactive simulations. With MDDriver it becomes very easy to render any particle-based molecular simulation engine interactive. Here we use its implementation in the Gromacs software as an example. PMID:19353597

  4. Teaching Noncovalent Interactions Using Protein Molecular Evolution

    ERIC Educational Resources Information Center

    Fornasari, Maria Silvina; Parisi, Gustavo; Echave, Julian

    2008-01-01

    Noncovalent interactions and physicochemical properties of amino acids are important topics in biochemistry courses. Here, we present a computational laboratory where the capacity of each of the 20 amino acids to maintain different noncovalent interactions are used to investigate the stabilizing forces in a set of proteins coming from organisms…

  5. Learning contextual gene set interaction networks of cancer with condition specificity

    PubMed Central

    2013-01-01

    Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further

  6. Molecular microenvironments: Solvent interactions with nucleic acid bases and ions

    NASA Technical Reports Server (NTRS)

    Macelroy, R. D.; Pohorille, A.

    1986-01-01

    The possibility of reconstructing plausible sequences of events in prebiotic molecular evolution is limited by the lack of fossil remains. However, with hindsight, one goal of molecular evolution was obvious: the development of molecular systems that became constituents of living systems. By understanding the interactions among molecules that are likely to have been present in the prebiotic environment, and that could have served as components in protobiotic molecular systems, plausible evolutionary sequences can be suggested. When stable aggregations of molecules form, a net decrease in free energy is observed in the system. Such changes occur when solvent molecules interact among themselves, as well as when they interact with organic species. A significant decrease in free energy, in systems of solvent and organic molecules, is due to entropy changes in the solvent. Entropy-driven interactioins played a major role in the organization of prebiotic systems, and understanding the energetics of them is essential to understanding molecular evolution.

  7. Aripiprazole salts IV. Anionic plus solvato networks defining molecular conformation

    NASA Astrophysics Data System (ADS)

    Freire, Eleonora; Polla, Griselda; Baggio, Ricardo

    2014-06-01

    Five new examples of aripiprazole (arip) salts are presented, viz., the Harip phthalate [Harip+·C8H5O4-(I)], homophthalate [Harip+·C9H7O4-(II)] and thiosalicilate [Harip+·C7H4O2S-(III)] salts on one side, and two different dihidrogenphosphates, Harip+·H2PO4-·2(H3PO4)·H2O (IV) and Harip+·H2PO4-·H3PO4(V). Regarding the internal structure of the aripH+ cations, they do not differ from the already known moieties in bond distances and angles, while interesting differences in conformation can be observed, setting them apart in two groups: those in I, II and III present similar conformations to those in the so far reported arip salts presenting the same centrosymmetric R(8)22 dimeric synthon, but different to those in IV and V. In parallel, the anion (+ acid) groups define bulky systems of different dimensionality (1D in the former group, 2D in the latter). The correlation between arip molecular conformation and anionic network type is discussed. An interesting feature arises with the water solvato molecule in IV, disordered around an inversion center, in regard with its interaction with an (also disordered) phosphato O-H, in a way that an “orderly disordered” H-bonding scheme arises, complying with the S.G. symmetry requirements only on average.

  8. Multiple tipping points and optimal repairing in interacting networks

    PubMed Central

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  9. Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis.

    PubMed

    Zhao, Zide; Zhang, Yingying; Gai, Fengchun; Wang, Ying

    2016-09-01

    Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis strategy. The biomarker-target interrelated molecules associated with diabetes and its complications were identified via the Comparative Toxicogenomics Database (CTD); the Search Tool for Recurring Instances of Neighboring Genes was utilized for network construction. Functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery software to investigate connections between diabetes and its complications. A total of 142 (including 122 biomarkers, 10 therapeutic targets and 10 overlapping molecules) biomarker-target interrelated molecules associated with diabetes and its complications were identified via the CTD database, and analysis of the network yielded 1,087 biological processes and fifteen Kyoto Encyclopedia of Genes and Genomes pathways with significant P‑values. Various critical aspects of the networks were examined in the present study: a) Intermolecular horizontal and vertical combinations in biomarkers and therapeutic targets associated with diabetes and its complicationb) network topology properties associated with molecular pathological responsec) contribution of key molecules to integrated regulation; and d) crosstalk between multiple pathways. Based on a multi-dimensional analysis, it was concluded that the integrated molecular pathological development of diabetes and its complications does not proceed randomly, which suggests a requirement for integrated, multi-target intervention. PMID:27430657

  10. Modelling refractive index changes due to molecular interactions

    NASA Astrophysics Data System (ADS)

    Varma, Manoj

    2016-03-01

    There are a large number of sensing techniques which use optical changes to monitor interactions between molecules. In the absence of fluorophores or other labels, the basic signal transduction mechanism relies on refractive index changes arising from the interactions of the molecules involved. A quantitative model incorporating molecular transport, reaction kinetics and optical mixing is presented which reveals important insights concerning the optimal detection of molecular interactions optically. Although conceptually simple, a comprehensive model such as this has not been reported anywhere. Specifically, we investigate the pros and cons of detecting molecular interactions in free solution relative to detecting molecular interactions on surfaces using surface bound receptor molecules such as antibodies. The model reveals that the refractive index change produced in surface based sensors is 2-3 orders of magnitude higher than that from interactions in free solution. On the other hand, the model also reveals that it is indeed possible to distinguish specific molecular interactions from non-specific ones based on free-solution bulk refractometry without any washing step necessary in surface based sensors. However, the refractive index change for free solution interactions predicted by the model is smaller than 10-7 RIU, even for large proteins such as IgG in sufficiently high concentrations. This value is smaller than the typical 10-6 RIU detection limit of most state of the art optical sensing techniques therefore requiring techniques with substantially higher index sensitivity such as Back Scattering Interferometry.

  11. Socioeconomic networks with long-range interactions

    NASA Astrophysics Data System (ADS)

    Carvalho, Rui; Iori, Giulia

    2008-07-01

    We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communication of information and allocation of goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights, parametrized by the variable δ , decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing δ , the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterized by shorter average path length, lower degree assortativity, and higher central point dominance. In the second part of the paper we compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.

  12. Interactive analysis of systems biology molecular expression data

    PubMed Central

    Zhang, Mingwu; Ouyang, Qi; Stephenson, Alan; Kane, Michael D; Salt, David E; Prabhakar, Sunil; Burgner, John; Buck, Charles; Zhang, Xiang

    2008-01-01

    Background Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Molecular correlations and comparative studies of molecular expression are crucial to establishing interdependent connections in systems biology. The existing software packages provide limited data mining capability. The user must first generate visualization data with a preferred data mining algorithm and then upload the resulting data into the visualization package for graphic visualization of molecular relations. Results Presented is a novel interactive visual data mining application, SysNet that provides an interactive environment for the analysis of high data volume molecular expression information of most any type from biological systems. It integrates interactive graphic visualization and statistical data mining into a single package. SysNet interactively presents intermolecular correlation information with circular and heatmap layouts. It is also applicable to comparative analysis of molecular expression data, such as time course data. Conclusion The SysNet program has been utilized to analyze elemental profile changes in response to an increasing concentration of iron (Fe) in growth media (an ionomics dataset). This study case demonstrates that the SysNet software is an effective platform for interactive analysis of molecular expression information in systems biology. PMID:18312669

  13. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    SciTech Connect

    Ba, Qian; Li, Junyang; Huang, Chao; Li, Jingquan; Chu, Ruiai; Wu, Yongning; Wang, Hui

    2015-03-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.

  14. Programming Molecular Association and Viscoelastic Behavior in Protein Networks.

    PubMed

    Dooling, Lawrence J; Buck, Maren E; Zhang, Wen-Bin; Tirrell, David A

    2016-06-01

    A set of recombinant artificial proteins that can be cross-linked, by either covalent bonds or association of helical domains or both, is described. The designed proteins can be used to construct molecular networks in which the mechanism of crosslinking determines the time-dependent responses to mechanical deformation. PMID:27061171

  15. Two-dimensional topological insulator molecular networks: dependence on structure, symmetry, and composition

    NASA Astrophysics Data System (ADS)

    Tan, Liang Z.; Louie, Steven G.

    2014-03-01

    2D molecular networks can be fabricated from a wide variety of molecular building blocks, arranged in many different configurations. Interactions between neighboring molecular building blocks result in the formation of new 2D materials. Examples of 2D organic topological insulators, that contain molecular building blocks and heavy elements arranged in a hexagonal lattice, have been recently proposed by Feng Liu and coworkers (Nano Lett., 13, 2842 (2013)). In this work, we present a systematic study of the design space of 2D molecular network topological insulators, elucidating the role of structure, symmetry, and composition of the networks. We show that the magnitude and presence of spin-orbit gaps in the electronic band structure is strongly dependent on the symmetry properties and arrangement of the individual components of the molecular lattice. We present general rules to maximize the magnitude of spin-orbit gaps and perform ab-initio calculations on promising structures derived from these guidelines. This work was supported by National Science Foundation Grant No. DMR10-1006184, the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Computational resources have been provided by the NSF through XSEDE resources at NICS.

  16. Study of molecular interactions with 13C DNP-NMR

    NASA Astrophysics Data System (ADS)

    Lerche, Mathilde H.; Meier, Sebastian; Jensen, Pernille R.; Baumann, Herbert; Petersen, Bent O.; Karlsson, Magnus; Duus, Jens Ø.; Ardenkjær-Larsen, Jan H.

    2010-03-01

    NMR spectroscopy is an established, versatile technique for the detection of molecular interactions, even when these interactions are weak. Signal enhancement by several orders of magnitude through dynamic nuclear polarization alleviates several practical limitations of NMR-based interaction studies. This enhanced non-equilibrium polarization contributes sensitivity for the detection of molecular interactions in a single NMR transient. We show that direct 13C NMR ligand binding studies at natural isotopic abundance of 13C gets feasible in this way. Resultant screens are easy to interpret and can be performed at 13C concentrations below μM. In addition to such ligand-detected studies of molecular interaction, ligand binding can be assessed and quantified with enzymatic assays that employ hyperpolarized substrates at varying enzyme inhibitor concentrations. The physical labeling of nuclear spins by hyperpolarization thus provides the opportunity to devise fast novel in vitro experiments with low material requirement and without the need for synthetic modifications of target or ligands.

  17. Nano-guided cell networks as conveyors of molecular communication

    PubMed Central

    Terrell, Jessica L.; Wu, Hsuan-Chen; Tsao, Chen-Yu; Barber, Nathan B.; Servinsky, Matthew D.; Payne, Gregory F.; Bentley, William E.

    2015-01-01

    Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and ‘binned' responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a ‘bio-litmus' in a manner read by simple optical means. PMID:26455828

  18. Nano-guided cell networks as conveyors of molecular communication.

    PubMed

    Terrell, Jessica L; Wu, Hsuan-Chen; Tsao, Chen-Yu; Barber, Nathan B; Servinsky, Matthew D; Payne, Gregory F; Bentley, William E

    2015-01-01

    Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and 'binned' responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a 'bio-litmus' in a manner read by simple optical means. PMID:26455828

  19. IntAct: an open source molecular interaction database

    PubMed Central

    Hermjakob, Henning; Montecchi-Palazzi, Luisa; Lewington, Chris; Mudali, Sugath; Kerrien, Samuel; Orchard, Sandra; Vingron, Martin; Roechert, Bernd; Roepstorff, Peter; Valencia, Alfonso; Margalit, Hanah; Armstrong, John; Bairoch, Amos; Cesareni, Gianni; Sherman, David; Apweiler, Rolf

    2004-01-01

    IntAct provides an open source database and toolkit for the storage, presentation and analysis of protein interactions. The web interface provides both textual and graphical representations of protein interactions, and allows exploring interaction networks in the context of the GO annotations of the interacting proteins. A web service allows direct computational access to retrieve interaction networks in XML format. IntAct currently contains ∼2200 binary and complex interactions imported from the literature and curated in collaboration with the Swiss-Prot team, making intensive use of controlled vocabularies to ensure data consistency. All IntAct software, data and controlled vocabularies are available at http://www.ebi.ac.uk/intact. PMID:14681455

  20. IntAct: an open source molecular interaction database.

    PubMed

    Hermjakob, Henning; Montecchi-Palazzi, Luisa; Lewington, Chris; Mudali, Sugath; Kerrien, Samuel; Orchard, Sandra; Vingron, Martin; Roechert, Bernd; Roepstorff, Peter; Valencia, Alfonso; Margalit, Hanah; Armstrong, John; Bairoch, Amos; Cesareni, Gianni; Sherman, David; Apweiler, Rolf

    2004-01-01

    IntAct provides an open source database and toolkit for the storage, presentation and analysis of protein interactions. The web interface provides both textual and graphical representations of protein interactions, and allows exploring interaction networks in the context of the GO annotations of the interacting proteins. A web service allows direct computational access to retrieve interaction networks in XML format. IntAct currently contains approximately 2200 binary and complex interactions imported from the literature and curated in collaboration with the Swiss-Prot team, making intensive use of controlled vocabularies to ensure data consistency. All IntAct software, data and controlled vocabularies are available at http://www.ebi.ac.uk/intact. PMID:14681455

  1. Joint clustering of protein interaction networks through Markov random walk

    PubMed Central

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  2. Joint clustering of protein interaction networks through Markov random walk.

    PubMed

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  3. Uncovering the molecular networks in periodontitis

    PubMed Central

    Trindade, Fábio; Oppenheim, Frank G.; Helmerhorst, Eva J.; Amado, Francisco; Gomes, Pedro S.; Vitorino, Rui

    2015-01-01

    Periodontitis is a complex immune-inflammatory disease that results from a preestablished infection in gingiva, mainly due to Gram-negative bacteria that colonize deeper in gingival sulcus and latter periodontal pocket. Host inflammatory and immune responses have both protective and destructive roles. Although cytokines, prostaglandins, and proteases struggle against microbial burden, these molecules promote connective tissue loss and alveolar bone resorption, leading to several histopathological changes, namely destruction of periodontal ligament, deepening of periodontal pocket, and bone loss, which can converge to attain tooth loss. Despite the efforts of genomics, transcriptomics, proteomics/peptidomics, and metabolomics, there is no available biomarker for periodontitis diagnosis, prognosis, and treatment evaluation, which could assist on the established clinical evaluation. Nevertheless, some genes, transcripts, proteins and metabolites have already shown a different expression in healthy subjects and in patients. Though, so far, ‘omics approaches only disclosed the host inflammatory response as a consequence of microbial invasion in periodontitis and the diagnosis in periodontitis still relies on clinical parameters, thus a molecular tool for assessing periodontitis lacks in current dental medicine paradigm. Saliva and gingival crevicular fluid have been attracting researchers due to their diagnostic potential, ease, and noninvasive nature of collection. Each one of these fluids has some advantages and disadvantages that are discussed in this review. PMID:24828325

  4. DockingShop: A Tool for Interactive Molecular Docking

    SciTech Connect

    Lu, Ting-Cheng; Max, Nelson L.; Ding, Jinhui; Bethel, E. Wes; Crivelli, Silvia N.

    2005-04-24

    Given two independently determined molecular structures, the molecular docking problem predicts the bound association, or best fit between them, while allowing for conformational changes of the individual molecules during construction of a molecular complex. Docking Shop is an integrated environment that permits interactive molecular docking by navigating a ligand or protein to an estimated binding site of a receptor with real-time graphical feedback of scoring factors as visual guides. Our program can be used to create initial configurations for a protein docking prediction process. Its output--the structure of aprotein-ligand or protein-protein complex--may serve as an input for aprotein docking algorithm, or an optimization process. This tool provides molecular graphics interfaces for structure modeling, interactive manipulation, navigation, optimization, and dynamic visualization to aid users steer the prediction process using their biological knowledge.

  5. Guaranteeing global synchronization in networks with stochastic interactions

    NASA Astrophysics Data System (ADS)

    Klinglmayr, Johannes; Kirst, Christoph; Bettstetter, Christian; Timme, Marc

    2012-07-01

    We design the interactions between oscillators communicating via variably delayed pulse coupling to guarantee their synchronization on arbitrary network topologies. We identify a class of response functions and prove convergence to network-wide synchrony from arbitrary initial conditions. Synchrony is achieved if the pulse emission is unreliable or intentionally probabilistic. These results support the design of scalable, reliable and energy-efficient communication protocols for fully distributed synchronization as needed, e.g., in mobile phone networks, embedded systems, sensor networks and autonomously interacting swarm robots.

  6. Domain-mediated protein interaction prediction: From genome to network.

    PubMed

    Reimand, Jüri; Hui, Shirley; Jain, Shobhit; Law, Brian; Bader, Gary D

    2012-08-14

    Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease. PMID:22561014

  7. Problem Solving Interactions on Electronic Networks.

    ERIC Educational Resources Information Center

    Waugh, Michael; And Others

    Arguing that electronic networking provides a medium which is qualitatively superior to the traditional classroom for conducting certain types of problem solving exercises, this paper details the Water Problem Solving Project, which was conducted on the InterCultural Learning Network in 1985 and 1986 with students from the United States, Mexico,…

  8. Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes.

    PubMed

    Liang, Yuting; Zhao, Huihui; Deng, Ye; Zhou, Jizhong; Li, Guanghe; Sun, Bo

    2016-01-01

    With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential "keystone" genes, defined as either "hubs" or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions. PMID:26870020

  9. Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

    PubMed Central

    Liang, Yuting; Zhao, Huihui; Deng, Ye; Zhou, Jizhong; Li, Guanghe; Sun, Bo

    2016-01-01

    With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential “keystone” genes, defined as either “hubs” or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions. PMID:26870020

  10. Coiled-coil networking shapes cell molecular machinery

    PubMed Central

    Wang, Yongqiang; Zhang, Xinlei; Zhang, Hong; Lu, Yi; Huang, Haolong; Dong, Xiaoxi; Chen, Jinan; Dong, Jiuhong; Yang, Xiao; Hang, Haiying; Jiang, Taijiao

    2012-01-01

    The highly abundant α-helical coiled-coil motif not only mediates crucial protein–protein interactions in the cell but is also an attractive scaffold in synthetic biology and material science and a potential target for disease intervention. Therefore a systematic understanding of the coiled-coil interactions (CCIs) at the organismal level would help unravel the full spectrum of the biological function of this interaction motif and facilitate its application in therapeutics. We report the first identified genome-wide CCI network in Saccharomyces cerevisiae, which consists of 3495 pair-wise interactions among 598 predicted coiled-coil regions. Computational analysis revealed that the CCI network is specifically and functionally organized and extensively involved in the organization of cell machinery. We further show that CCIs play a critical role in the assembly of the kinetochore, and disruption of the CCI network leads to defects in kinetochore assembly and cell division. The CCI network identified in this study is a valuable resource for systematic characterization of coiled coils in the shaping and regulation of a host of cellular machineries and provides a basis for the utilization of coiled coils as domain-based probes for network perturbation and pharmacological applications. PMID:22875988

  11. Structure and interactions in isotropic and liquid crystalline neurofilament networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna Bea

    2007-12-01

    Neurofilaments (NFs) are cytoskeletal proteins that are localized within nerve cells, which form long oriented bundles running the length of axons. While abnormal aggregations of these proteins have been implicated in several neurological disorders including Parkinson's disease and ALS, interfilament interactions in both the normal and diseased states are not well understood. In vivo, NFs are supramolecular structures composed of three subunit proteins of low (NF-L), medium (NF-M), and high molecular (NF-H) weight that assemble into a 10 nm diameter rod with radiating sidearms, forming a bottle-brush conformation. In this study we alter the subunit composition and probe the resulting networks with polarized microscopy and synchrotron small angle x-ray scattering (SAXS), in order to isolate the role of each subunit in interfilament interactions. By reassembling NFs in vitro from varying ratios of the subunit proteins, purified from bovine spinal cord, we form filaments with controlled subunit compositions. The resulting filaments, at a high volume fraction, are nematic liquid crystalline gels with a well defined spacing, determined with SAXS. Upon dilution the difference between the subunits is realized with NF-M grafted filaments being dominated by attractive interactions and remaining aligned, while those flanked with NF-H sidearms repel and become isotropic gels. Interplay between these forces is seen in the ternary system composed of all three subunit proteins (NF-LMH). The polyampholytic subunits have a charge distribution that varies along the length of the sidearm, which forms the brush layer, and the distribution is different for each subunit. The interfilament interactions are highly dependent on environmental conditions including salt concentration, pH, and osmotic pressure. Increasing ionic strength induces attractive interactions and a stabilization of the nematic phase in filaments that were repulsive at lower monovalent salt concentration. The

  12. Protein-protein interaction network analysis of cirrhosis liver disease

    PubMed Central

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  13. Molecular contamination study by interaction of a molecular beam with a platinum surface

    NASA Technical Reports Server (NTRS)

    Nuss, H. E.

    1976-01-01

    The capability of molecular beam scattering from a solid surface is analyzed for identification of molecular contamination of the surface. The design and setup of the molecular beam source and the measuring setup for the application of a phase sensitive measuring technique for the determination of the scattered beam intensity are described. The scattering distributions of helium and nitrogen molecular beams interacting with a platinum surface were measured for different amounts of contamination from diffusion pump oil for surface temperatures ranging from 30 to 400 C. The results indicate the scattering of molecular beams from a platinum surface is a very sensitive method for detecting surface contamination.

  14. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks

    PubMed Central

    Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E.

    2013-01-01

    Multi-component signal transduction pathways and gene regulatory circuits underpin integrated cellular responses to perturbations. A recurring set of network motifs serve as the basic building blocks of these molecular signalling networks. This review focuses on ultrasensitive response motifs (URMs) that amplify small percentage changes in the input signal into larger percentage changes in the output response. URMs generally possess a sigmoid input–output relationship that is steeper than the Michaelis–Menten type of response and is often approximated by the Hill function. Six types of URMs can be commonly found in intracellular molecular networks and each has a distinct kinetic mechanism for signal amplification. These URMs are: (i) positive cooperative binding, (ii) homo-multimerization, (iii) multistep signalling, (iv) molecular titration, (v) zero-order covalent modification cycle and (vi) positive feedback. Multiple URMs can be combined to generate highly switch-like responses. Serving as basic signal amplifiers, these URMs are essential for molecular circuits to produce complex nonlinear dynamics, including multistability, robust adaptation and oscillation. These dynamic properties are in turn responsible for higher-level cellular behaviours, such as cell fate determination, homeostasis and biological rhythm. PMID:23615029

  15. Interaction Networks: Generating High Level Hints Based on Network Community Clustering

    ERIC Educational Resources Information Center

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

    We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

  16. Empirical evaluation of neutral interactions in host-parasite networks.

    PubMed

    Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

    2014-04-01

    While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization. PMID:24642492

  17. Molecular transport network security using multi-wavelength optical spins.

    PubMed

    Tunsiri, Surachai; Thammawongsa, Nopparat; Mitatha, Somsak; Yupapin, Preecha P

    2016-01-01

    Multi-wavelength generation system using an optical spin within the modified add-drop optical filter known as a PANDA ring resonator for molecular transport network security is proposed. By using the dark-bright soliton pair control, the optical capsules can be constructed and applied to securely transport the trapped molecules within the network. The advantage is that the dark and bright soliton pair (components) can securely propagate for long distance without electromagnetic interference. In operation, the optical intensity from PANDA ring resonator is fed into gold nano-antenna, where the surface plasmon oscillation between soliton pair and metallic waveguide is established. PMID:25058032

  18. On the stability of surface-confined nanoporous molecular networks

    SciTech Connect

    Ghijsens, Elke; Adisoejoso, Jinne E-mail: tobe@chem.es.osaka-u.ac.jp Van Gorp, Hans; Destoop, Iris; Ivasenko, Oleksandr; Van der Auweraer, Mark; De Feyter, Steven E-mail: tobe@chem.es.osaka-u.ac.jp; Noguchi, Aya; Tahara, Kazukuni; Tobe, Yoshito E-mail: tobe@chem.es.osaka-u.ac.jp

    2015-03-14

    Self-assembly of molecular building blocks into two-dimensional nanoporous networks has been a topic of broad interest for many years. However, various factors govern the specific outcome of the self-assembly process, and understanding and controlling these are key to successful creation. In this work, the self-assembly of two alkylated dehydrobenzo[12]annulene building blocks was compared at the liquid-solid interface. It turned out that only a small chemical modification within the building blocks resulted in enhanced domain sizes and stability of the porous packing relative to the dense linear packing. Applying a thermodynamic model for phase transition revealed some key aspects for network formation.

  19. Systems Analysis of Plant Functional, Transcriptional, Physical Interaction, and Metabolic Networks

    PubMed Central

    Bassel, George W.; Gaudinier, Allison; Brady, Siobhan M.; Hennig, Lars; Rhee, Seung Y.; De Smet, Ive

    2012-01-01

    Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach. PMID:23110892

  20. Quantitative and logic modelling of gene and molecular networks

    PubMed Central

    Le Novère, Nicolas

    2015-01-01

    Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms. PMID:25645874

  1. TeloPIN: a database of telomeric proteins interaction network in mammalian cells

    PubMed Central

    Luo, Zhenhua; Dai, Zhiming; Xie, Xiaowei; Feng, Xuyang; Liu, Dan; Songyang, Zhou; Xiong, Yuanyan

    2015-01-01

    Interaction network surrounding telomeres has been intensively studied during the past two decades. However, no specific resource by integrating telomere interaction information data is currently available. To facilitate the understanding of the molecular interaction network by which telomeres are associated with biological process and diseases, we have developed TeloPIN (Telomeric Proteins Interaction Network) database (http://songyanglab.sysu.edu.cn/telopin/), a novel database that points to provide comprehensive information on protein–protein, protein–DNA and protein–RNA interaction of telomeres. TeloPIN database contains four types of interaction data, including (i) protein–protein interaction (PPI) data, (ii) telomeric proteins ChIP-seq data, (iii) telomere-associated proteins data and (iv) telomeric repeat-containing RNAs (TERRA)-interacting proteins data. By analyzing these four types of interaction data, we found that 358 and 199 proteins have more than one type of interaction information in human and mouse cells, respectively. We also developed table browser and TeloChIP genome browser to help researchers with better integrated visualization of interaction data from different studies. The current release of TeloPIN database includes 1111 PPI, eight telomeric protein ChIP-seq data sets, 1391 telomere-associated proteins and 183 TERRA-interacting proteins from 92 independent studies in mammalian cells. The interaction information provided by TeloPIN database will greatly expand our knowledge of telomeric proteins interaction network. Database URL: TeloPIN database address is http://songyanglab.sysu.edu.cn/telopin. TeloPIN database is freely available to non-commercial use. PMID:25792605

  2. Speeding up biomolecular interactions by molecular sledding

    SciTech Connect

    Turkin, Alexander; Zhang, Lei; Marcozzi, Alessio; Mangel, Walter F.; Herrmann, Andreas; van Oijen, Antoine M.

    2015-10-07

    In numerous biological processes associations involve a protein with its binding partner, an event that is preceded by a diffusion-mediated search bringing the two partners together. Often hindered by crowding in biologically relevant environments, three-dimensional diffusion can be slow and result in long bimolecular association times. Moreover, the initial association step between two binding partners often represents a rate-limiting step in biotechnologically relevant reactions. We also demonstrate the practical use of an 11-a.a. DNA-interacting peptide derived from adenovirus to reduce the dimensionality of diffusional search processes and speed up associations between biological macromolecules. We functionalize binding partners with the peptide and demonstrate that the ability of the peptide to one-dimensionally diffuse along DNA results in a 20-fold reduction in reaction time. We also show that modifying PCR primers with the peptide sled enables significant acceleration of standard PCR reactions.

  3. Speeding up biomolecular interactions by molecular sledding

    DOE PAGESBeta

    Turkin, Alexander; Zhang, Lei; Marcozzi, Alessio; Mangel, Walter F.; Herrmann, Andreas; van Oijen, Antoine M.

    2015-10-07

    In numerous biological processes associations involve a protein with its binding partner, an event that is preceded by a diffusion-mediated search bringing the two partners together. Often hindered by crowding in biologically relevant environments, three-dimensional diffusion can be slow and result in long bimolecular association times. Moreover, the initial association step between two binding partners often represents a rate-limiting step in biotechnologically relevant reactions. We also demonstrate the practical use of an 11-a.a. DNA-interacting peptide derived from adenovirus to reduce the dimensionality of diffusional search processes and speed up associations between biological macromolecules. We functionalize binding partners with the peptidemore » and demonstrate that the ability of the peptide to one-dimensionally diffuse along DNA results in a 20-fold reduction in reaction time. We also show that modifying PCR primers with the peptide sled enables significant acceleration of standard PCR reactions.« less

  4. Molecular interactions of graphene oxide with human blood plasma proteins

    NASA Astrophysics Data System (ADS)

    Kenry, Affa Affb Affc; Loh, Kian Ping; Lim, Chwee Teck

    2016-04-01

    We investigate the molecular interactions between graphene oxide (GO) and human blood plasma proteins. To gain an insight into the bio-physico-chemical activity of GO in biological and biomedical applications, we performed a series of biophysical assays to quantify the molecular interactions between GO with different lateral size distributions and the three essential human blood plasma proteins. We elucidate the various aspects of the GO-protein interactions, particularly, the adsorption, binding kinetics and equilibrium, and conformational stability, through determination of quantitative parameters, such as GO-protein association constants, binding cooperativity, and the binding-driven protein structural changes. We demonstrate that the molecular interactions between GO and plasma proteins are significantly dependent on the lateral size distribution and mean lateral sizes of the GO nanosheets and their subtle variations may markedly influence the GO-protein interactions. Consequently, we propose the existence of size-dependent molecular interactions between GO nanosheets and plasma proteins, and importantly, the presence of specific critical mean lateral sizes of GO nanosheets in achieving very high association and fluorescence quenching efficiency of the plasma proteins. We anticipate that this work will provide a basis for the design of graphene-based and other related nanomaterials for a plethora of biological and biomedical applications.

  5. Development of Attention Networks and Their Interactions in Childhood

    ERIC Educational Resources Information Center

    Pozuelos, Joan P.; Paz-Alonso, Pedro M.; Castillo, Alejandro; Fuentes, Luis J.; Rueda, M. Rosario

    2014-01-01

    In the present study, we investigated developmental trajectories of alerting, orienting, and executive attention networks and their interactions over childhood. Two cross-sectional experiments were conducted with different samples of 6-to 12-year-old children using modified versions of the attention network task (ANT). In Experiment 1 (N = 106),…

  6. Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.

    ERIC Educational Resources Information Center

    Odell, Kerry S.; And Others

    1994-01-01

    Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric Technology" (Berry). (JOW)

  7. Percolation on networks with antagonistic and dependent interactions

    NASA Astrophysics Data System (ADS)

    Kotnis, Bhushan; Kuri, Joy

    2015-03-01

    Drawing inspiration from real world interacting systems, we study a system consisting of two networks that exhibit antagonistic and dependent interactions. By antagonistic and dependent interactions we mean that a proportion of functional nodes in a network cause failure of nodes in the other, while failure of nodes in the other results in failure of links in the first. In contrast to interdependent networks, which can exhibit first-order phase transitions, we find that the phase transitions in such networks are continuous. Our analysis shows that, compared to an isolated network, the system is more robust against random attacks. Surprisingly, we observe a region in the parameter space where the giant connected components of both networks start oscillating. Furthermore, we find that for Erdős-Rényi and scale-free networks the system oscillates only when the dependence and antagonism between the two networks are very high. We believe that this study can further our understanding of real world interacting systems.

  8. CancerNet: a database for decoding multilevel molecular interactions across diverse cancer types.

    PubMed

    Meng, X; Wang, J; Yuan, C; Li, X; Zhou, Y; Hofestädt, R; Chen, M

    2015-01-01

    Protein-protein interactions (PPIs) and microRNA (miRNA)-target interactions are important for deciphering the mechanisms of tumorigenesis. However, current PPI databases do not support cancer-specific analysis. Also, no available databases can be used to retrieve cancer-associated miRNA-target interactions. As the pathogenesis of human cancers is affected by several miRNAs rather than a single miRNA, it is needed to uncover miRNA synergism in a systems level. Here for each cancer type, we constructed a miRNA-miRNA functionally synergistic network based on the functions of miRNA targets and their topological features in that cancer PPI network. And for the first time, we report the cancer-specific database CancerNet (http://bis.zju.edu.cn/CancerNet), which contains information about PPIs, miRNA-target interactions and functionally synergistic miRNA-miRNA pairs across 33 human cancer types. In addition, PPI information across 33 main normal tissues and cell types are included. Flexible query methods are allowed to retrieve cancer molecular interactions. Network viewer can be used to visualize interactions that users are interested in. Enrichment analysis tool was designed to detect significantly overrepresented Gene Ontology categories of miRNA targets. Thus, CancerNet serves as a comprehensive platform for assessing the roles of proteins and miRNAs, as well as their interactions across human cancers. PMID:26690544

  9. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    SciTech Connect

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  10. Studying Interactions by Molecular Dynamics Simulations at High Concentration

    PubMed Central

    Fogolari, Federico; Corazza, Alessandra; Toppo, Stefano; Tosatto, Silvio C. E.; Viglino, Paolo; Ursini, Fulvio; Esposito, Gennaro

    2012-01-01

    Molecular dynamics simulations have been used to study molecular encounters and recognition. In recent works, simulations using high concentration of interacting molecules have been performed. In this paper, we consider the practical problems for setting up the simulation and to analyse the results of the simulation. The simulation of beta 2-microglobulin association and the simulation of the binding of hydrogen peroxide by glutathione peroxidase are provided as examples. PMID:22500085

  11. Molecular signals in the interactions between plants and microbes.

    PubMed

    Clarke, H R; Leigh, J A; Douglas, C J

    1992-10-16

    The field of plant-microbe interactions has witnessed several recent breakthroughs, such as the molecular details of vir gene induction, identification of Nod factors, and the cloning and characterization of avr genes. Other breakthroughs, such as the cloning and characterization of R genes, appear imminent. Parallels to mammalian systems are emerging in the world of plant-microbe interactions, for example, ion channels formed by Rhizobium proteins, similarities of hrp genes to pathogenicity genes of mammalian pathogens, and plant signal transduction via calcium and protein phosphorylation. We remain, however, largely ignorant of many facets of signaling in plant-microbe interactions. We know little about how microbial signals are perceived by plants or how subsequent signal transduction occurs within plant cells and are probably unaware of many of the microbe-generated signals to which plants respond or of plant-generated signals to which bacteria and fungi respond. Contributions from those working on the genetics, molecular biology, and physiology of bacteria, fungi, and plants will be required to address these questions. The many nonpathogenic plant-microbe interactions in addition to the Rhizobium-plant interaction remain relatively unexplored. Genetic and molecular approaches are being initiated to investigate the signaling that is likely to underlie interactions such as those between mycorrhizal fungi and plant roots and between epiphytic bacteria and plant leaf surfaces. The importance of these interactions to plant growth and development makes it likely that they will figure more prominently at future symposia.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:1423587

  12. Obtain osteoarthritis related molecular signature genes through regulation network.

    PubMed

    Li, Yawei; Wang, Bing; Lv, Guohua; Xiong, Guangzhong; Liu, Wei Dong; Li, Lei

    2012-01-01

    Osteoarthritis (OA), also known as degenerative joint disease or osteoarthrosis, is the most common form of arthritis. OA occurs when cartilage in the joints wears down over time. We used the GSE1919 series to identify potential genes that correlated to OA. The aim of our study was to obtain a molecular signature of OA through the regulation network based on differentially expressed genes. From the result of regulation network construction in OA, a number of transcription factors (TFs) and pathways closely related to OA were linked by our method. Peroxisome proliferator-activated receptor γ also arises as hub nodes in our transcriptome network and certain TFs containing CEBPD, EGR2 and ETS2 were shown to be related to OA by a previous study. PMID:21946934

  13. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  14. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

    PubMed Central

    Xia, Jianguo; Benner, Maia J.; Hancock, Robert E. W.

    2014-01-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  15. Origin of molecular conformational stability: Perspectives from molecular orbital interactions and density functional reactivity theory

    SciTech Connect

    Liu, Shubin E-mail: schauer@unc.edu; Schauer, Cynthia K. E-mail: schauer@unc.edu

    2015-02-07

    To have a quantitative understanding about the origin of conformation stability for molecular systems is still an unaccomplished task. Frontier orbital interactions from molecular orbital theory and energy partition schemes from density functional reactivity theory are the two approaches available in the literature that can be used for this purpose. In this work, we compare the performance of these approaches for a total of 48 simple molecules. We also conduct studies to flexibly bend bond angles for water, carbon dioxide, borane, and ammonia molecules to obtain energy profiles for these systems over a wide range of conformations. We find that results from molecular orbital interactions using frontier occupied orbitals such as the highest occupied molecular orbital and its neighbors are only qualitatively, at most semi-qualitatively, trustworthy. To obtain quantitative insights into relative stability of different conformations, the energy partition approach from density functional reactivity theory is much more reliable. We also find that the electrostatic interaction is the dominant descriptor for conformational stability, and steric and quantum effects are smaller in contribution but their contributions are indispensable. Stable molecular conformations prefer to have a strong electrostatic interaction, small molecular size, and large exchange-correlation effect. This work should shed new light towards establishing a general theoretical framework for molecular stability.

  16. Model of mobile agents for sexual interactions networks

    NASA Astrophysics Data System (ADS)

    González, M. C.; Lind, P. G.; Herrmann, H. J.

    2006-02-01

    We present a novel model to simulate real social networks of complex interactions, based in a system of colliding particles (agents). The network is build by keeping track of the collisions and evolves in time with correlations which emerge due to the mobility of the agents. Therefore, statistical features are a consequence only of local collisions among its individual agents. Agent dynamics is realized by an event-driven algorithm of collisions where energy is gained as opposed to physical systems which have dissipation. The model reproduces empirical data from networks of sexual interactions, not previously obtained with other approaches.

  17. Properties of interaction networks underlying the minority game

    NASA Astrophysics Data System (ADS)

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N ) and the agent's capacity to process information (m ), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results.

  18. Properties of interaction networks underlying the minority game.

    PubMed

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N) and the agent's capacity to process information (m), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results. PMID:25493843

  19. Optimization of an interactive distributive computer network

    NASA Technical Reports Server (NTRS)

    Frederick, V.

    1985-01-01

    The activities under a cooperative agreement for the development of a computer network are briefly summarized. Research activities covered are: computer operating systems optimization and integration; software development and implementation of the IRIS (Infrared Imaging of Shuttle) Experiment; and software design, development, and implementation of the APS (Aerosol Particle System) Experiment.

  20. Recurrent interactions in spiking networks with arbitrary topology.

    PubMed

    Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan

    2012-03-01

    The population activity of random networks of excitatory and inhibitory leaky integrate-and-fire neurons has been studied extensively. In particular, a state of asynchronous activity with low firing rates and low pairwise correlations emerges in sparsely connected networks. We apply linear response theory to evaluate the influence of detailed network structure on neuron dynamics. It turns out that pairwise correlations induced by direct and indirect network connections can be related to the matrix of direct linear interactions. Furthermore, we study the influence of the characteristics of the neuron model. Interpreting the reset as self-inhibition, we examine its influence, via the spectrum of single-neuron activity, on network autocorrelation functions and the overall correlation level. The neuron model also affects the form of interaction kernels and consequently the time-dependent correlation functions. We find that a linear instability of networks with Erdös-Rényi topology coincides with a global transition to a highly correlated network state. Our work shows that recurrent interactions have a profound impact on spike train statistics and provides tools to study the effects of specific network topologies. PMID:22587132

  1. CyTargetLinker: A Cytoscape App to Integrate Regulatory Interactions in Network Analysis

    PubMed Central

    Kutmon, Martina; Kelder, Thomas; Mandaviya, Pooja; Evelo, Chris T. A.; Coort, Susan L.

    2013-01-01

    Introduction The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes. Implementation We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers. Results Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation. Conclusions CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network

  2. AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES

    PubMed Central

    DARABOS, CHRISTIAN; QIU, JINGYA; MOORE, JASON H.

    2015-01-01

    Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single “tripartite” network. Despite extensive research, the mechanisms by which chemical agents disrupt biological pathways are still poorly understood. In this study, we use our integrated network model to identify specific biological pathway candidates possibly disrupted by environmental agents. We conjecture that a higher number of co-occurrences between an environmental substance and biological pathway pair can be associated with a higher likelihood that the substance is involved in disrupting that pathway. We validate our model by demonstrating its ability to detect known arsenic and signal transduction pathway interactions and speculate on candidate cell-cell junction organization pathways disrupted by cadmium. The validation was supported by distinct publications of cell biology and genetic studies that associated environmental exposure to pathway disruption. The integrated network approach is a novel method for detecting the biological effects of environmental exposures. A better understanding of the molecular processes associated with specific environmental exposures will help in developing targeted molecular therapies for patients who have been exposed to the toxicity of environmental chemicals. PMID:26776169

  3. Modeling the dynamical interaction between epidemics on overlay networks

    NASA Astrophysics Data System (ADS)

    Marceau, Vincent; Noël, Pierre-André; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2011-08-01

    Epidemics seldom occur as isolated phenomena. Typically, two or more viral agents spread within the same host population and may interact dynamically with each other. We present a general model where two viral agents interact via an immunity mechanism as they propagate simultaneously on two networks connecting the same set of nodes. By exploiting a correspondence between the propagation dynamics and a dynamical process performing progressive network generation, we develop an analytical approach that accurately captures the dynamical interaction between epidemics on overlay networks. The formalism allows for overlay networks with arbitrary joint degree distribution and overlap. To illustrate the versatility of our approach, we consider a hypothetical delayed intervention scenario in which an immunizing agent is disseminated in a host population to hinder the propagation of an undesirable agent (e.g., the spread of preventive information in the context of an emerging infectious disease).

  4. Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction

    PubMed Central

    Escalera, Sergio; Baró, Xavier; Vitrià, Jordi; Radeva, Petia; Raducanu, Bogdan

    2012-01-01

    Social interactions are a very important component in people’s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links’ weights are a measure of the “influence” a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. PMID:22438733

  5. 2004 Atomic and Molecular Interactions Gordon Research Conference

    SciTech Connect

    Dr. Paul J. Dagdigian

    2004-10-25

    The 2004 Gordon Research Conference on Atomic and Molecular Interactions was held July 11-16 at Colby-Sawyer College, New London, New Hampshire. This latest edition in a long-standing conference series featured invited talks and contributed poster papers on dynamics and intermolecular interactions in a variety of environments, ranging from the gas phase through surfaces and condensed media. A total of 90 conferees participated in the conference.

  6. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    ERIC Educational Resources Information Center

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  7. Behavioural phenotype affects social interactions in an animal network

    PubMed Central

    Pike, Thomas W; Samanta, Madhumita; Lindström, Jan; Royle, Nick J

    2008-01-01

    Animal social networks can be extremely complex and are characterized by highly non-random interactions between group members. However, very little is known about the underlying factors affecting interaction preferences, and hence network structure. One possibility is that behavioural differences between individuals, such as how bold or shy they are, can affect the frequency and distribution of their interactions within a network. We tested this using individually marked three-spined sticklebacks (Gasterosteus aculeatus), and found that bold individuals had fewer overall interactions than shy fish, but tended to distribute their interactions more evenly across all group members. Shy fish, on the other hand, tended to associate preferentially with a small number of other group members, leading to a highly skewed distribution of interactions. This was mediated by the reduced tendency of shy fish to move to a new location within the tank when they were interacting with another individual; bold fish showed no such tendency and were equally likely to move irrespective of whether they were interacting or not. The results show that animal social network structure can be affected by the behavioural composition of group members and have important implications for understanding the spread of information and disease in social groups. PMID:18647713

  8. Functional interactions between large-scale networks during memory search.

    PubMed

    Kragel, James E; Polyn, Sean M

    2015-03-01

    Neuroimaging studies have identified two major large-scale brain networks, the default mode network (DMN) and the dorsal attention network (DAN), which are engaged for internally and externally directed cognitive tasks respectively, and which show anticorrelated activity during cognitively demanding tests and at rest. We identified these brain networks using independent component analysis (ICA) of functional magnetic resonance imaging data, and examined their interactions during the free-recall task, a self-initiated memory search task in which retrieval is performed in the absence of external cues. Despite the internally directed nature of the task, the DAN showed transient engagement in the seconds leading up to successful retrieval. ICA revealed a fractionation of the DMN into 3 components. A posteromedial network increased engagement during memory search, while the two others showed suppressed activity during memory search. Cooperative interactions between this posteromedial network, a right-lateralized frontoparietal control network, and a medial prefrontal network were maintained during memory search. The DAN demonstrated heterogeneous task-dependent shifts in functional coupling with various subnetworks within the DMN. This functional reorganization suggests a broader role of the DAN in the absence of externally directed cognition, and highlights the contribution of the posteromedial network to episodic retrieval. PMID:24084128

  9. Major component analysis of dynamic networks of physiologic organ interactions

    NASA Astrophysics Data System (ADS)

    Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

    2015-09-01

    The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

  10. Network of immune-neuroendocrine interactions.

    PubMed Central

    Besedovsky, H; Sorkin, E

    1977-01-01

    In order to bring the self-regulated immune system into conformity with other body systems its functioning within the context of an immune-neuroendocrine network is proposed. This hypothesis is based on the existence of afferent--efferent pathways between immune and neuroendocrine structures. Major endocrine responses occur as a consequence of antigenic stimulation and changes in the electrical activity of the hypothalamus also take place; both of these alterations are temporally related to the immune response itself. This endocrine response has meaningful implications for immunoregulation and for immunospecificity. During ontogeny, there is also evidence for the operations of a complex network between the endocrine and immune system, a bidirectional interrelationship that may well affect each developmental stage of both functions. As sequels the functioning of the immune system and the outcome of this interrelation could be decisive in lymphoid cell homeostasis, self-tolerance, and could also have significant implications for pathology. PMID:849642

  11. Modeling Human Dynamics of Face-to-Face Interaction Networks

    NASA Astrophysics Data System (ADS)

    Starnini, Michele; Baronchelli, Andrea; Pastor-Satorras, Romualdo

    2013-04-01

    Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of interconversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents that perform a random walk in a two-dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.

  12. How do oncoprotein mutations rewire protein-protein interaction networks?

    PubMed

    Bowler, Emily H; Wang, Zhenghe; Ewing, Rob M

    2015-01-01

    The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype. PMID:26325016

  13. Gesture Interaction Browser-Based 3D Molecular Viewer.

    PubMed

    Virag, Ioan; Stoicu-Tivadar, Lăcrămioara; Crişan-Vida, Mihaela

    2016-01-01

    The paper presents an open source system that allows the user to interact with a 3D molecular viewer using associated hand gestures for rotating, scaling and panning the rendered model. The novelty of this approach is that the entire application is browser-based and doesn't require installation of third party plug-ins or additional software components in order to visualize the supported chemical file formats. This kind of solution is suitable for instruction of users in less IT oriented environments, like medicine or chemistry. For rendering various molecular geometries our team used GLmol (a molecular viewer written in JavaScript). The interaction with the 3D models is made with Leap Motion controller that allows real-time tracking of the user's hand gestures. The first results confirmed that the resulting application leads to a better way of understanding various types of translational bioinformatics related problems in both biomedical research and education. PMID:27350455

  14. Microstructural modeling of collagen network mechanics and interactions with the proteoglycan gel in articular cartilage.

    PubMed

    Quinn, T M; Morel, V

    2007-01-01

    Cartilage matrix mechanical function is largely determined by interactions between the collagen fibrillar network and the proteoglycan gel. Although the molecular physics of these matrix constituents have been characterized and modern imaging methods are capable of localized measurement of molecular densities and orientation distributions, theoretical tools for using this information for prediction of cartilage mechanical behavior are lacking. We introduce a means to model collagen network contributions to cartilage mechanics based upon accessible microstructural information (fibril density and orientation distributions) and which self-consistently follows changes in microstructural geometry with matrix deformations. The interplay between the molecular physics of the collagen network and the proteoglycan gel is scaled up to determine matrix material properties, with features such as collagen fibril pre-stress in free-swelling cartilage emerging naturally and without introduction of ad hoc parameters. Methods are developed for theoretical treatment of the collagen network as a continuum-like distribution of fibrils, such that mechanical analysis of the network may be simplified by consideration of the spherical harmonic components of functions of the fibril orientation, strain, and stress distributions. Expressions for the collagen network contributions to matrix stress and stiffness tensors are derived, illustrating that only spherical harmonic components of orders 0 and 2 contribute to the stress, while orders 0, 2, and 4 contribute to the stiffness. Depth- and compression-dependent equilibrium mechanical properties of cartilage matrix are modeled, and advantages of the approach are illustrated by exploration of orientation and strain distributions of collagen fibrils in compressed cartilage. Results highlight collagen-proteoglycan interactions, especially for very small physiological strains where experimental data are relatively sparse. These methods for

  15. Conservation and topology of protein interaction networks under duplication-divergence evolution

    PubMed Central

    Evlampiev, Kirill; Isambert, Hervé

    2008-01-01

    Genomic duplication-divergence processes are the primary source of new protein functions and thereby contribute to the evolutionary expansion of functional molecular networks. Yet, it is still unclear to what extent such duplication-divergence processes also restrict by construction the emerging properties of molecular networks, regardless of any specific cellular functions. We address this question, here, focusing on the evolution of protein–protein interaction (PPI) networks. We solve a general duplication-divergence model, based on the statistically necessary deletions of protein–protein interactions arising from stochastic duplications at various genomic scales, from single-gene to whole-genome duplications. Major evolutionary scenarios are shown to depend on two global parameters only: (i) a protein conservation index (M), which controls the evolutionary history of PPI networks, and (ii) a distinct topology index (M′) controlling their resulting structure. We then demonstrate that conserved, nondense networks, which are of prime biological relevance, are also necessarily scale-free by construction, irrespective of any evolutionary variations or fluctuations of the model parameters. It is shown to result from a fundamental linkage between individual protein conservation and network topology under general duplication-divergence evolution. By contrast, we find that conservation of network motifs with two or more proteins cannot be indefinitely preserved under general duplication-divergence evolution (independently from any network rewiring dynamics), in broad agreement with empirical evidence between phylogenetically distant species. All in all, these evolutionary constraints, inherent to duplication-divergence processes, appear to have largely controlled the overall topology and scale-dependent conservation of PPI networks, regardless of any specific biological function. PMID:18632555

  16. Microbial interaction networks in soil and in silico

    NASA Astrophysics Data System (ADS)

    Vetsigian, Kalin

    2012-02-01

    Soil harbors a huge number of microbial species interacting through secretion of antibiotics and other chemicals. What patterns of species interactions allow for this astonishing biodiversity to be sustained, and how do these interactions evolve? I used a combined experimental-theoretical approach to tackle these questions. Focusing on bacteria from the genus Steptomyces, known for their diverse secondary metabolism, I isolated 64 natural strains from several individual grains of soil and systematically measured all pairwise interactions among them. Quantitative measurements on such scale were enabled by a novel experimental platform based on robotic handling, a custom scanner array and automatic image analysis. This unique platform allowed the simultaneous capturing of ˜15,000 time-lapse movies of growing colonies of each isolate on media conditioned by each of the other isolates. The data revealed a rich network of strong negative (inhibitory) and positive (stimulating) interactions. Analysis of this network and the phylogeny of the isolates, together with mathematical modeling of microbial communities, revealed that: 1) The network of interactions has three special properties: ``balance'', ``bi- modality'' and ``reciprocity''; 2) The interaction network is fast evolving; 3) Mathematical modeling explains how rapid evolution can give rise to the three special properties through an interplay between ecology and evolution. These properties are not a result of stable co-existence, but rather of continuous evolutionary turnover of strains with different production and resistance capabilities.

  17. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. PMID:23889048

  18. Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

    PubMed Central

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109

  19. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers.

    PubMed

    Liu, Rui; Wang, Xiangdong; Aihara, Kazuyuki; Chen, Luonan

    2014-05-01

    Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high

  20. How does the molecular network structure influence PDMS elastomer wettability?

    NASA Astrophysics Data System (ADS)

    Melillo, Matthew; Genzer, Jan

    Poly(dimethylsiloxane) (PDMS) is one of the most common elastomers, with applications ranging from medical devices to absorbents for water treatment. Fundamental understanding of how liquids spread on the surface of and absorb into PDMS networks is of critical importance for the design and use of another application - microfluidic devices. We have systematically studied the effects of polymer molecular weight, loading of tetra-functional crosslinker, end-group chemical functionality, and the extent of dilution of the curing mixture on the mechanical and surface properties of end-linked PDMS networks. The gel and sol fractions, storage and loss moduli, liquid swelling ratios, and water contact angles have all been shown to vary greatly based on the aforementioned variables. Similar trends were observed for the commercial PDMS material, Sylgard-184. Our results have confirmed theories predicting the relationships between modulus and swelling. Furthermore, we have provided new evidence for the strong influence that substrate modulus and molecular network structure have on the wettability of PDMS elastomers. These findings will aid in the design and implementation of efficient microfluidics and other PDMS-based materials that involve the transport of liquids.

  1. Using Molecular Networking for Microbial Secondary Metabolite Bioprospecting

    PubMed Central

    Purves, Kevin; Macintyre, Lynsey; Brennan, Debra; Hreggviðsson, Guðmundur Ó.; Kuttner, Eva; Ásgeirsdóttir, Margrét E.; Young, Louise C.; Green, David H.; Edrada-Ebel, Ruangelie; Duncan, Katherine R.

    2016-01-01

    The oceans represent an understudied resource for the isolation of bacteria with the potential to produce novel secondary metabolites. In particular, actinomyces are well known to produce chemically diverse metabolites with a wide range of biological activities. This study characterised spore-forming bacteria from both Scottish and Antarctic sediments to assess the influence of isolation location on secondary metabolite production. Due to the selective isolation method used, all 85 isolates belonged to the phyla Firmicutes and Actinobacteria, with the majority of isolates belonging to the genera Bacillus and Streptomyces. Based on morphology, thirty-eight isolates were chosen for chemical investigation. Molecular networking based on chemical profiles (HR-MS/MS) of fermentation extracts was used to compare complex metabolite extracts. The results revealed 40% and 42% of parent ions were produced by Antarctic and Scottish isolated bacteria, respectively, and only 8% of networked metabolites were shared between these locations, implying a high degree of biogeographic influence upon secondary metabolite production. The resulting molecular network contained over 3500 parent ions with a mass range of m/z 149–2558 illustrating the wealth of metabolites produced. Furthermore, seven fermentation extracts showed bioactivity against epithelial colon adenocarcinoma cells, demonstrating the potential for the discovery of novel bioactive compounds from these understudied locations. PMID:26761036

  2. Quantum Theory of Atomic and Molecular Structures and Interactions

    NASA Astrophysics Data System (ADS)

    Makrides, Constantinos

    This dissertation consists of topics in two related areas of research that together provide quantum mechanical descriptions of atomic and molecular interactions and reactions. The first is the ab initio electronic structure calculation that provides the atomic and molecular interaction potential, including the long-range potential. The second is the quantum theory of interactions that uses such potentials to understand scattering, long-range molecules, and reactions. In ab initio electronic structure calculations, we present results of dynamic polarizabilities for a variety of atoms and molecules, and the long-range dispersion coefficients for a number of atom-atom and atom-molecule cases. We also present results of a potential energy surface for the triatomic lithium-ytterbium-lithium system, aimed at understanding the related chemical reactions. In the quantum theory of interactions, we present a multichannel quantum-defect theory (MQDT) for atomic interactions in a magnetic field. This subject, which is complex especially for atoms with hyperfine structure, is essential for the understanding and the realization of control and tuning of atomic interactions by a magnetic field: a key feature that has popularized cold atom physics in its investigations of few-body and many-body quantum systems. Through the example of LiK, we show how MQDT provides a systematic and an efficient understanding of atomic interaction in a magnetic field, especially magnetic Feshbach resonances in nonzero partial waves.

  3. Networked Interactive Video for Group Training

    ERIC Educational Resources Information Center

    Eary, John

    2008-01-01

    The National Computing Centre (NCC) has developed an interactive video training system for the Scottish Police College to help train police supervisory officers in crowd control at major spectator events, such as football matches. This approach involves technology-enhanced training in a group-learning environment, and may have significant impact…

  4. Speech networks at rest and in action: interactions between functional brain networks controlling speech production

    PubMed Central

    Fuertinger, Stefan

    2015-01-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  5. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    PubMed

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  6. Dispersion Interactions in High-Density Molecular Crystals

    NASA Astrophysics Data System (ADS)

    Csernica, Peter; Maitra, Rahul; Distasio, Robert

    Dispersion interactions are ubiquitous quantum mechanical phenomena arising from correlated electron density fluctuations in molecules and materials. As a key component of non-bonded interactions, dispersion forces play a critical role in determining the structure and stability of molecular crystals. Due to the relative intermolecular separation in high-density molecular crystals, an accurate description of these non-bonded interactions requires the inclusion of terms beyond the asymptotic induced-dipole-induced-dipole (C6 /R6) contribution. In this work, we have developed a first principles based approach within the framework of Density Functional Theory (i.e., that only depends on the charge density n (r)) for capturing the higher-order induced multipolar contributions to the correlation energy. As a first application of this method, we have investigated the structure and stability of the high-density ice molecular crystal polymorphs at the ice VI--ice VII--ice VIII triple point (278K, 2.1GPa) using ab-initio molecular dynamics in the isobaric-isothermal (NpT) ensemble.

  7. Detection of Binding Site Molecular Interaction Field Similarities.

    PubMed

    Chartier, Matthieu; Najmanovich, Rafael

    2015-08-24

    Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available. PMID:26158641

  8. Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

    PubMed Central

    Liu, Mei; Chen, Xue-wen; Jothi, Raja

    2009-01-01

    Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs. Results: Here, we describe a novel method called K-GIDDI (knowledge-guided inference of DDIs) to narrow down the PPI sites to smaller regions/domains. K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges. Our results indicate that K-GIDDI can reliably predict DDIs. Most importantly, K-GIDDI's novel network expansion procedure allows prediction of DDIs that are otherwise not identifiable by methods that rely only on PPI data. Contact: xwchen@ku.edu Availability: http://www.ittc.ku.edu/∼xwchen/domainNetwork/ddinet.html Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19667081

  9. Towards synthetic molecular motors: a model elastic-network study

    NASA Astrophysics Data System (ADS)

    Sarkar, Amartya; Flechsig, Holger; Mikhailov, Alexander S.

    2016-04-01

    Protein molecular motors play a fundamental role in biological cells and development of their synthetic counterparts is a major challenge. Here, we show how a model motor system with the operation mechanism resembling that of muscle myosin can be designed at the concept level, without addressing the implementation aspects. The model is constructed as an elastic network, similar to the coarse-grained descriptions used for real proteins. We show by numerical simulations that the designed synthetic motor can operate as a deterministic or Brownian ratchet and that there is a continuous transition between such two regimes. The motor operation under external load, approaching the stall condition, is also analysed.

  10. Prediction and Annotation of Plant Protein Interaction Networks

    SciTech Connect

    McDermott, Jason E.; Wang, Jun; Yu, Jun; Wong, Gane Ka-Shu; Samudrala, Ram

    2009-02-01

    Large-scale experimental studies of interactions between components of biological systems have been performed for a variety of eukaryotic organisms. However, there is a dearth of such data for plants. Computational methods for prediction of relationships between proteins, primarily based on comparative genomics, provide a useful systems-level view of cellular functioning and can be used to extend information about other eukaryotes to plants. We have predicted networks for Arabidopsis thaliana, Oryza sativa indica and japonica and several plant pathogens using the Bioverse (http://bioverse.compbio.washington.edu) and show that they are similar to experimentally-derived interaction networks. Predicted interaction networks for plants can be used to provide novel functional annotations and predictions about plant phenotypes and aid in rational engineering of biosynthesis pathways.

  11. Construction of polycythemia vera protein interaction network and prediction of related biological functions.

    PubMed

    Liu, L-J; Cao, X-J; Zhou, C; Sun, Y; Lv, Q-L; Feng, F-B; Zhang, Y-Y; Sun, C-G

    2016-01-01

    Here, polycythemia vera (PV)-related genes were screened by the Online Mendelian Inheritance in Man (OMIM), and literature pertaining to the identified genes was extracted and a protein-protein interaction network was constructed using various Cytoscape plugins. Various molecular complexes were detected using the Clustervize plugin and a gene ontology-enrichment analysis of the biological pathways, molecular functions, and cellular components of the selected molecular complexes were identified using the BiNGo plugin. Fifty-four PV-related genes were identified in OMIM. The protein-protein interaction network contains 5 molecular complexes with correlation integral values >4. These complexes regulated various biological processes (peptide tyrosinase acidification, cell metabolism, and macromolecular biosynthesis), molecular functions (kinase activity, receptor binding, and cytokine activity), and the cellular components were mainly concentrated in the nucleus, intracellular membrane-bounded organelles, and extracellular region. These complexes were associated with the JAK-STAT signal transduction pathway, neurotrophic factor signaling pathway, and Wnt signaling pathway, which were correlated with chronic myeloid leukemia and acute myeloid leukemia. PMID:26909922

  12. Challenges in calculating molecular systems with Coulomb interactions

    NASA Astrophysics Data System (ADS)

    Kirnosov, Nikita; Sharkey, Keeper; Adamowicz, Ludwik

    2014-03-01

    The highly accurate quantum mechanical calculations are not only crucial for high-resolution experimental data verification, but may also serve as a guide in the field of exotic systems exploration. Including all non-relativistic effects in a single-step variational approach and rigorously separating out the center of mass motion allows us to build a reliable model for calculating bound states of molecular systems with Coulomb interactions. In these calculations the wave function of the system is expanded in terms of explicitly correlated Gaussian (ECG) basis functions. Examples of calculations of energies and other properties of some molecular systems will be presented.

  13. VNP: Interactive Visual Network Pharmacology of Diseases, Targets, and Drugs

    PubMed Central

    Hu, Q-N; Deng, Z; Tu, W; Yang, X; Meng, Z-B; Deng, Z-X; Liu, J

    2014-01-01

    In drug discovery, promiscuous targets, multifactorial diseases, and “dirty” drugs construct complex network relationships. Network pharmacology description and analysis not only give a systems-level understanding of drug action and disease complexity but can also help to improve the efficiency of target selection and drug design. Visual network pharmacology (VNP) is developed to visualize network pharmacology of targets, diseases, and drugs with a graph network by using disease, target or drug names, chemical structures, or protein sequence. To our knowledge, VNP is the first free interactive VNP server that should be very helpful for systems pharmacology research. VNP is freely available at http://cadd.whu.edu.cn/ditad/vnpsearch. PMID:24622768

  14. ComPPI: a cellular compartment-specific database for protein–protein interaction network analysis

    PubMed Central

    Veres, Daniel V.; Gyurkó, Dávid M.; Thaler, Benedek; Szalay, Kristóf Z.; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein–protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein–protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein–protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design. PMID:25348397

  15. Graph spectral analysis of protein interaction network evolution.

    PubMed

    Thorne, Thomas; Stumpf, Michael P H

    2012-10-01

    We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data. PMID:22552917

  16. Conserved molecular interactions in centriole-to-centrosome conversion.

    PubMed

    Fu, Jingyan; Lipinszki, Zoltan; Rangone, Hélène; Min, Mingwei; Mykura, Charlotte; Chao-Chu, Jennifer; Schneider, Sandra; Dzhindzhev, Nikola S; Gottardo, Marco; Riparbelli, Maria Giovanna; Callaini, Giuliano; Glover, David M

    2016-01-01

    Centrioles are required to assemble centrosomes for cell division and cilia for motility and signalling. New centrioles assemble perpendicularly to pre-existing ones in G1-S and elongate throughout S and G2. Fully elongated daughter centrioles are converted into centrosomes during mitosis to be able to duplicate and organize pericentriolar material in the next cell cycle. Here we show that centriole-to-centrosome conversion requires sequential loading of Cep135, Ana1 (Cep295) and Asterless (Cep152) onto daughter centrioles during mitotic progression in both Drosophila melanogaster and human. This generates a molecular network spanning from the inner- to outermost parts of the centriole. Ana1 forms a molecular strut within the network, and its essential role can be substituted by an engineered fragment providing an alternative linkage between Asterless and Cep135. This conserved architectural framework is essential for loading Asterless or Cep152, the partner of the master regulator of centriole duplication, Plk4. Our study thus uncovers the molecular basis for centriole-to-centrosome conversion that renders daughter centrioles competent for motherhood. PMID:26595382

  17. Conserved Molecular Interactions in Centriole-to-Centrosome Conversion

    PubMed Central

    Fu, Jingyan; Lipinszki, Zoltan; Rangone, Hélène; Min, Mingwei; Mykura, Charlotte; Chao-Chu, Jennifer; Schneider, Sandra; Dzhindzhev, Nikola S.; Gottardo, Marco; Riparbelli, Maria Giovanna; Callaini, Giuliano; Glover, David M.

    2015-01-01

    Centrioles are required to assemble centrosomes for cell division and cilia for motility and signaling. New centrioles assemble perpendicularly to pre-existing ones in G1-S and elongate throughout S and G2. Fully-elongated daughter centrioles are converted into centrosomes during mitosis to be able to duplicate and organize pericentriolar material in the next cell cycle. Here we show that centriole-to-centrosome conversion requires sequential loading of Cep135, Ana1:Cep295 and Asterless:Cep152 onto daughter centrioles during mitotic progression. This generates a molecular network spanning from inner- to outer-most parts of the centriole. Ana1 forms a molecular strut within the network and its essential role can be substituted by an engineered fragment providing an alternative linkage between Asterless and Cep135. This conserved architectural framework is essential for loading Asterless:Cep152, partner of the master regulator of centriole duplication, Plk4. Our study thus uncovers the molecular basis for centriole-to-centrosome conversion that renders daughter centrioles competent for motherhood. PMID:26595382

  18. NACE: A web-based tool for prediction of intercompartmental efficiency of human molecular genetic networks.

    PubMed

    Popik, Olga V; Ivanisenko, Timofey V; Saik, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-06-15

    Molecular genetic processes generally involve proteins from distinct intracellular localisations. Reactions that follow the same process are distributed among various compartments within the cell. In this regard, the reaction rate and the efficiency of biological processes can depend on the subcellular localisation of proteins. Previously, the authors proposed a method of evaluating the efficiency of biological processes based on the analysis of the distribution of protein subcellular localisation (Popik et al., 2014). Here, NACE is presented, which is an open access web-oriented program that implements this method and allows the user to evaluate the intercompartmental efficiency of human molecular genetic networks. The method has been extended by a new feature that provides the evaluation of the tissue-specific efficiency of networks for more than 2800 anatomical structures. Such assessments are important in cases when molecular genetic pathways in different tissues proceed with the participation of various proteins with a number of intracellular localisations. For example, an analysis of KEGG pathways, conducted using the developed program, showed that the efficiencies of many KEGG pathways are tissue-specific. Analysis of efficiencies of regulatory pathways in the liver, linking proteins of the hepatitis C virus with human proteins involved in the KEGG apoptosis pathway, showed that intercompartmental efficiency might play an important role in host-pathogen interactions. Thus, the developed tool can be useful in the study of the effectiveness of functioning of various molecular genetic networks, including metabolic, regulatory, host-pathogen interactions and others taking into account tissue-specific gene expression. The tool is available via the following link: http://www-bionet.sscc.ru/nace/. PMID:27109913

  19. Simultaneous and coordinated rotational switching of all molecular rotors in a network

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Kersell, H.; Stefak, R.; Echeverria, J.; Iancu, V.; Perera, U. G. E.; Li, Y.; Deshpande, A.; Braun, K.-F.; Joachim, C.; Rapenne, G.; Hla, S.-W.

    2016-08-01

    A range of artificial molecular systems has been created that can exhibit controlled linear and rotational motion. In the further development of such systems, a key step is the addition of communication between molecules in a network. Here, we show that a two-dimensional array of dipolar molecular rotors can undergo simultaneous rotational switching when applying an electric field from the tip of a scanning tunnelling microscope. Several hundred rotors made from porphyrin-based double-decker complexes can be simultaneously rotated when in a hexagonal rotor network on a Cu(111) surface by applying biases above 1 V at 80 K. The phenomenon is observed only in a hexagonal rotor network due to the degeneracy of the ground-state dipole rotational energy barrier of the system. Defects are essential to increase electric torque on the rotor network and to stabilize the switched rotor domains. At low biases and low initial rotator angles, slight reorientations of individual rotors can occur, resulting in the rotator arms pointing in different directions. Analysis reveals that the rotator arm directions are not random, but are coordinated to minimize energy via crosstalk among the rotors through dipolar interactions.

  20. Core level regulatory network of osteoblast as molecular mechanism for osteoporosis and treatment.

    PubMed

    Yuan, Ruoshi; Ma, Shengfei; Zhu, Xiaomei; Li, Jun; Liang, Yuhong; Liu, Tao; Zhu, Yanxia; Zhang, Bingbing; Tan, Shuang; Guo, Huajie; Guan, Shuguang; Ao, Ping; Zhou, Guangqian

    2016-01-26

    To develop and evaluate the long-term prophylactic treatment for chronic diseases such as osteoporosis requires a clear view of mechanism at the molecular and systems level. While molecular signaling pathway studies for osteoporosis are extensive, a unifying mechanism is missing. In this work, we provide experimental and systems-biology evidences that a tightly connected top-level regulatory network may exist, which governs the normal and osteoporotic phenotypes of osteoblast. Specifically, we constructed a hub-like interaction network from well-documented cross-talks among estrogens, glucocorticoids, retinoic acids, peroxisome proliferator-activated receptor, vitamin D receptor and calcium-signaling pathways. The network was verified with transmission electron microscopy and gene expression profiling for bone tissues of ovariectomized (OVX) rats before and after strontium gluconate (GluSr) treatment. Based on both the network structure and the experimental data, the dynamical modeling predicts calcium and glucocorticoids signaling pathways as targets for GluSr treatment. Modeling results further reveal that in the context of missing estrogen signaling, the GluSr treated state may be an outcome that is closest to the healthy state. PMID:26783964

  1. Simultaneous and coordinated rotational switching of all molecular rotors in a network.

    PubMed

    Zhang, Y; Kersell, H; Stefak, R; Echeverria, J; Iancu, V; Perera, U G E; Li, Y; Deshpande, A; Braun, K-F; Joachim, C; Rapenne, G; Hla, S-W

    2016-08-01

    A range of artificial molecular systems has been created that can exhibit controlled linear and rotational motion. In the further development of such systems, a key step is the addition of communication between molecules in a network. Here, we show that a two-dimensional array of dipolar molecular rotors can undergo simultaneous rotational switching when applying an electric field from the tip of a scanning tunnelling microscope. Several hundred rotors made from porphyrin-based double-decker complexes can be simultaneously rotated when in a hexagonal rotor network on a Cu(111) surface by applying biases above 1 V at 80 K. The phenomenon is observed only in a hexagonal rotor network due to the degeneracy of the ground-state dipole rotational energy barrier of the system. Defects are essential to increase electric torque on the rotor network and to stabilize the switched rotor domains. At low biases and low initial rotator angles, slight reorientations of individual rotors can occur, resulting in the rotator arms pointing in different directions. Analysis reveals that the rotator arm directions are not random, but are coordinated to minimize energy via crosstalk among the rotors through dipolar interactions. PMID:27159740

  2. Interface-Resolved Network of Protein-Protein Interactions

    PubMed Central

    Johnson, Margaret E.; Hummer, Gerhard

    2013-01-01

    We define an interface-interaction network (IIN) to capture the specificity and competition between protein-protein interactions (PPI). This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME) exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked in the coarser

  3. Supramolecular Interactions in Secondary Plant Cell Walls: Effect of Lignin Chemical Composition Revealed with the Molecular Theory of Solvation.

    PubMed

    Silveira, Rodrigo L; Stoyanov, Stanislav R; Gusarov, Sergey; Skaf, Munir S; Kovalenko, Andriy

    2015-01-01

    Plant biomass recalcitrance, a major obstacle to achieving sustainable production of second generation biofuels, arises mainly from the amorphous cell-wall matrix containing lignin and hemicellulose assembled into a complex supramolecular network that coats the cellulose fibrils. We employed the statistical-mechanical, 3D reference interaction site model with the Kovalenko-Hirata closure approximation (or 3D-RISM-KH molecular theory of solvation) to reveal the supramolecular interactions in this network and provide molecular-level insight into the effective lignin-lignin and lignin-hemicellulose thermodynamic interactions. We found that such interactions are hydrophobic and entropy-driven, and arise from the expelling of water from the mutual interaction surfaces. The molecular origin of these interactions is carbohydrate-π and π-π stacking forces, whose strengths are dependent on the lignin chemical composition. Methoxy substituents in the phenyl groups of lignin promote substantial entropic stabilization of the ligno-hemicellulosic matrix. Our results provide a detailed molecular view of the fundamental interactions within the secondary plant cell walls that lead to recalcitrance. PMID:26263115

  4. Collective transport of weakly interacting molecular motors with Langmuir kinetics

    NASA Astrophysics Data System (ADS)

    Chandel, Sameep; Chaudhuri, Abhishek; Muhuri, Sudipto

    2015-04-01

    Filament-based intracellular transport involves the collective action of molecular motor proteins. Experimental evidences suggest that microtubule (MT) filament bound motor proteins such as kinesins weakly interact among themselves during transport and with the surrounding cellular environment. Motivated by these observations we study a driven lattice gas model for collective unidirectional transport of molecular motors on open filament. This model incorporates short-range next-nearest-neighbour (NNN) interactions between the motors and couples the transport process on filament with surrounding cellular environment through adsorption-desorption Langmuir kinetics (LK) of the motors. We analyse this model within the framework of a mean-field (MF) theory in the limit of weak interactions between the motors. We point to the mapping of this model with the non-conserved version of the Katz-Lebowitz-Spohn (KLS) model. The system exhibits rich phase behavior with a variety of inhomogeneous phases including localized shocks in the bulk of the filament. We obtain the steady-state density and current profiles, analyse their variation as a function of the strength of interaction and construct the non-equilibrium MF phase diagram. We compare these MF results with Monte Carlo simulations and find that the MF analysis shows reasonably good agreement with simulation results as long as the motors are weakly interacting. For sufficently strong NNN interaction between the motors, the mean-field results deviate significantly, and for very strong NNN interaction in the absence of LK, the current in the lattice is determined solely by the NNN interaction parameter and it becomes independent of entry and exit rates of motors at the filament boundaries.

  5. Strong interactions between spinal cord networks for locomotion and scratching.

    PubMed

    Hao, Zhao-Zhe; Spardy, Lucy E; Nguyen, Edward B L; Rubin, Jonathan E; Berkowitz, Ari

    2011-10-01

    Distinct rhythmic behaviors involving a common set of motoneurons and muscles can be generated by separate central nervous system (CNS) networks, a single network, or partly overlapping networks in invertebrates. Less is known for vertebrates. Simultaneous activation of two networks can reveal overlap or interactions between them. The turtle spinal cord contains networks that generate locomotion and three forms of scratching (rostral, pocket, and caudal), having different knee-hip synergies. Here, we report that in immobilized spinal turtles, simultaneous delivery of types of stimulation, which individually evoked forward swimming and one form of scratching, could 1) increase the rhythm frequency; 2) evoke switches, hybrids, and intermediate motor patterns; 3) recruit a swim motor pattern even when the swim stimulation was reduced to subthreshold intensity; and 4) disrupt rhythm generation entirely. The strength of swim stimulation could influence the result. Thus even pocket scratching and caudal scratching, which do not share a knee-hip synergy with forward swimming, can interact with swim stimulation to alter both rhythm and pattern generation. Model simulations were used to explore the compatibility of our experimental results with hypothetical network architectures for rhythm generation. Models could reproduce experimental observations only if they included interactions between neurons involved in swim and scratch rhythm generation, with maximal consistency between simulations and experiments attained using a model architecture in which certain neurons participated actively in both swim and scratch rhythmogenesis. Collectively, these findings suggest that the spinal cord networks that generate locomotion and scratching have important shared components or strong interactions between them. PMID:21734103

  6. Enlightening molecular mechanisms through study of protein interactions

    PubMed Central

    Rizo, Josep; Rosen, Michael K.; Gardner, Kevin H.

    2012-01-01

    The investigation of molecular mechanisms is a fascinating area of current biological research that unites efforts from scientists with very diverse expertise. This review provides a perspective on the characterization of protein interactions as a central aspect of this research. We discuss case studies on the neurotransmitter release machinery that illustrate a variety of principles and emphasize the power of combining nuclear magnetic resonance (NMR) spectroscopy with other biophysical techniques, particularly X-ray crystallography. These studies have shown that: (i) the soluble SNAP receptor (SNARE) proteins form a tight complex that brings the synaptic vesicle and plasma membranes together, which is key for membrane fusion; (ii) the SNARE syntaxin-1 adopts an autoinhibitory closed conformation; (iii) Munc18-1 plays crucial functions through interactions with closed syntaxin-1 and with the SNARE complex; (iv) Munc13s mediate the opening of syntaxin-1; (v) complexins play dual roles through distinct interactions with the SNARE complex; (vi) synaptotagmin-1 acts a Ca2+ sensor, interacting simultaneously with the membranes and the SNAREs; and (vii) a Munc13 homodimer to Munc13-RIM heterodimer switch modulates neurotransmitter release. Overall, this research underlines the complexities involved in elucidating molecular mechanisms and how these mechanisms can depend critically on an interplay between strong and weak protein interactions. PMID:22735643

  7. Controlling single-molecule junction conductance by molecular interactions

    PubMed Central

    Kitaguchi, Y.; Habuka, S.; Okuyama, H.; Hatta, S.; Aruga, T.; Frederiksen, T.; Paulsson, M.; Ueba, H.

    2015-01-01

    For the rational design of single-molecular electronic devices, it is essential to understand environmental effects on the electronic properties of a working molecule. Here we investigate the impact of molecular interactions on the single-molecule conductance by accurately positioning individual molecules on the electrode. To achieve reproducible and precise conductivity measurements, we utilize relatively weak π-bonding between a phenoxy molecule and a STM-tip to form and cleave one contact to the molecule. The anchoring to the other electrode is kept stable using a chalcogen atom with strong bonding to a Cu(110) substrate. These non-destructive measurements permit us to investigate the variation in single-molecule conductance under different but controlled environmental conditions. Combined with density functional theory calculations, we clarify the role of the electrostatic field in the environmental effect that influences the molecular level alignment. PMID:26135251

  8. Controlling single-molecule junction conductance by molecular interactions.

    PubMed

    Kitaguchi, Y; Habuka, S; Okuyama, H; Hatta, S; Aruga, T; Frederiksen, T; Paulsson, M; Ueba, H

    2015-01-01

    For the rational design of single-molecular electronic devices, it is essential to understand environmental effects on the electronic properties of a working molecule. Here we investigate the impact of molecular interactions on the single-molecule conductance by accurately positioning individual molecules on the electrode. To achieve reproducible and precise conductivity measurements, we utilize relatively weak π-bonding between a phenoxy molecule and a STM-tip to form and cleave one contact to the molecule. The anchoring to the other electrode is kept stable using a chalcogen atom with strong bonding to a Cu(110) substrate. These non-destructive measurements permit us to investigate the variation in single-molecule conductance under different but controlled environmental conditions. Combined with density functional theory calculations, we clarify the role of the electrostatic field in the environmental effect that influences the molecular level alignment. PMID:26135251

  9. Ecological Networks: Structure, Interaction Strength, and Stability

    NASA Astrophysics Data System (ADS)

    Bhattacharyya, Samit; Sinha, Somdatta

    The fundamental building blocks of any ecosystem, the food webs, which are assemblages of species through various interconnections, provide a central concept in ecology. The study of a food web allows abstractions of the complexity and interconnectedness of natural communities that transcend the specific details of the underlying systems. For example, Fig. 1 shows a typical food web, where the species are connected through their feeding relationships. The top predator, Heliaster (starfish) feeds on many gastropods like Hexaplex, Morula, Cantharus, etc., some of whom predate on each other [129]. Interactions between species in a food web can be of many types, such as predation, competition, mutualism, commensalism, and ammensalism (see Section 1.1, Fig. 2).

  10. The Kinetochore Interaction Network (KIN) of ascomycetes

    PubMed Central

    Freitag, Michael

    2016-01-01

    Chromosome segregation relies on coordinated activity of a large assembly of proteins, the “Kinetochore Interaction Network” (KIN). How conserved the underlying mechanisms driving the epigenetic phenomenon of centromere and kinetochore assembly and maintenance are remains unclear, even though various eukaryotic models have been studied. More than 50 different proteins, many in multiple copies, comprise the KIN or are associated with fungal centromeres and kinetochores. Proteins isolated from immune sera recognized centromeric regions on chromosomes and were thus named centromere proteins (“CENPs”). CENP-A, sometimes called “centromere-specific H3” (CenH3), is incorporated into nucleosomes within or near centromeres. The “constitutive centromere-associated network” (CCAN) assembles on this specialized chromatin, likely based on specific interactions with and requiring presence of CENP-C. The outer kinetochore comprises the Knl1-Mis12-Ndc80 (“KMN”) protein complexes that connect the CCAN to spindles, accomplished by binding and stabilizing microtubules (MTs) and in the process generating load-bearing assemblies for chromatid segregation. In most fungi the Dam1/DASH complex connects the KMN complexes to MTs. Fungi present a rich resource to investigate mechanistic commonalities but also differences in kinetochore architecture. While ascomycetes have sets of CCAN and KMN proteins that are conserved with those of either budding yeast or metazoans, searching other major branches of the fungal kingdom revealed that CCAN proteins are poorly conserved at the primary sequence level. Several conserved binding motifs or domains within KMN complexes have been described recently, and these features of ascomycete KIN proteins are shared with most metazoan proteins. In addition, several ascomycete-specific domains have been identified here. PMID:26908646

  11. Empirical temporal networks of face-to-face human interactions

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Cattuto, C.; Colizza, V.; Gesualdo, F.; Isella, L.; Pandolfi, E.; Pinton, J.-F.; Ravà, L.; Rizzo, C.; Romano, M.; Stehlé, J.; Tozzi, A. E.; Van den Broeck, W.

    2013-09-01

    The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.

  12. Bone regeneration: molecular and cellular interactions with calcium phosphate ceramics

    PubMed Central

    Barrère, Florence; van Blitterswijk, Clemens A; de Groot, Klaas

    2006-01-01

    Calcium phosphate bioceramics are widely used in orthopedic and dental applications and porous scaffolds made of them are serious candidates in the field of bone tissue engineering. They have superior properties for the stimulation of bone formation and bone bonding, both related to the specific interactions of their surface with the extracellular fluids and cells, ie, ionic exchanges, superficial molecular rearrangement and cellular activity. PMID:17717972

  13. Supernova remnant masers: Shock interactions with molecular clouds

    NASA Astrophysics Data System (ADS)

    Hewitt, John William

    Maser emission from the 1720-MHz transition of hydroxyl(OH) has identified shock interactions in 10% of all supernova remnants(SNRs). Such maser-emitting SNRs are also bright in molecular line emission. Though somewhat rare, SNRs interacting with dense molecular clouds are an important class in which to study cosmic ray acceleration, SNR evolution, and effects on the energetics and chemistry of the interstellar medium. To study molecular shocks via a multiwavelength approach, the VLA, GBT, Spitzer Space Telescope have been used in the following ways: (i) With the GBT widespread OH(1720 MHz) emission and absorption in other OH lines is observed across the interaction site. Observations of all four ground-state transitions at 1720, 1667/5 and 1612 MHz allows us to model OH excitation, yielding the temperature, density and OH abundance in the post-shock gas. Maser emission is found to have a higher flux density with the GBT than with high-resolution VLA observations for 10 of 15 observed remnants, suggesting maser emission is present on large spatial scales. (ii) Sensitive VLA observations of select SNRs (W44, IC 443, Kes 69, 3C 391, G357.7+0.3) reveal the nature of enhanced 1720 MHz emission. Numerous weak compact masers as well as diffuse extended emission are detected tracing the shock-front. Zeeman splitting of masers permits the post-shock magnetic field strength and the line of sight field direction to be directly measured. (iii) Rotational lines of molecular hydrogen are detected at the position of several masers with Spitzer IRS spectroscopy between 5 and 35 mm. Excitation of the hydrogen lines requires the passage of a C-type shock through dense molecular gas, in agreement with the conditions derived from OH excitation. The presence of bright ionic lines requires multiple shocks present at the interaction site. (iv) A new survey for SNR-masers has identified four new interacting SNRs within 10 degrees of the Galactic Center. Maser-emitting SNRs are found to

  14. Many-Body Dispersion Interactions in Molecular Materials

    NASA Astrophysics Data System (ADS)

    Distasio, Robert A., Jr.

    2015-03-01

    In this work, we have developed an efficient method for obtaining an accurate theoretical description of van der Waals (vdW) interactions that includes both long-range Coulomb electrodynamic response screening effects as well as treatment of the many-body vdW energy to infinite order. This method goes beyond the standard C6 /R6 pairwise additive approximation and can easily be coupled to a wide array of theoretical methods, ranging from classical force fields to higher-level quantum chemical calculations. To demonstrate the increasingly important role played by many-body vdW interactions in large, structurally complex molecular systems, we use this method to investigate several pertinent molecular properties, such as binding energies/affinities in gas-phase molecular dimers and supramolecular complexes, relative conformational energetics in small polypeptides, and thermodynamic stabilities among competing molecular crystal polymorphs. This work received funding from the Department of Energy under Grant Nos.: DOE DE-SC0008626 and DOE DE-FG02ER46201 and the European Research Council (ERC Starting Grant VDW-CMAT).

  15. Fanconi anemia proteins and their interacting partners: a molecular puzzle.

    PubMed

    Kaddar, Tagrid; Carreau, Madeleine

    2012-01-01

    In recent years, Fanconi anemia (FA) has been the subject of intense investigations, primarily in the DNA repair research field. Many discoveries have led to the notion of a canonical pathway, termed the FA pathway, where all FA proteins function sequentially in different protein complexes to repair DNA cross-link damages. Although a detailed architecture of this DNA cross-link repair pathway is emerging, the question of how a defective DNA cross-link repair process translates into the disease phenotype is unresolved. Other areas of research including oxidative metabolism, cell cycle progression, apoptosis, and transcriptional regulation have been studied in the context of FA, and some of these areas were investigated before the fervent enthusiasm in the DNA repair field. These other molecular mechanisms may also play an important role in the pathogenesis of this disease. In addition, several FA-interacting proteins have been identified with roles in these "other" nonrepair molecular functions. Thus, the goal of this paper is to revisit old ideas and to discuss protein-protein interactions related to other FA-related molecular functions to try to give the reader a wider perspective of the FA molecular puzzle. PMID:22737580

  16. Interactions between Distant ceRNAs in Regulatory Networks

    PubMed Central

    Nitzan, Mor; Steiman-Shimony, Avital; Altuvia, Yael; Biham, Ofer; Margalit, Hanah

    2014-01-01

    Competing endogenous RNAs (ceRNAs) were recently introduced as RNA transcripts that affect each other’s expression level through competition for their microRNA (miRNA) coregulators. This stems from the bidirectional effects between miRNAs and their target RNAs, where a change in the expression level of one target affects the level of the miRNA regulator, which in turn affects the level of other targets. By the same logic, miRNAs that share targets compete over binding to their common targets and therefore also exhibit ceRNA-like behavior. Taken together, perturbation effects could propagate in the posttranscriptional regulatory network through a path of coregulated targets and miRNAs that share targets, suggesting the existence of distant ceRNAs. Here we study the prevalence of distant ceRNAs and their effect in cellular networks. Analyzing the network of miRNA-target interactions deciphered experimentally in HEK293 cells, we show that it is a dense, intertwined network, suggesting that many nodes can act as distant ceRNAs of one another. Indeed, using gene expression data from a perturbation experiment, we demonstrate small, yet statistically significant, changes in gene expression caused by distant ceRNAs in that network. We further characterize the magnitude of the propagated perturbation effect and the parameters affecting it by mathematical modeling and simulations. Our results show that the magnitude of the effect depends on the generation and degradation rates of involved miRNAs and targets, their interaction rates, the distance between the ceRNAs and the topology of the network. Although demonstrated for a miRNA-mRNA regulatory network, our results offer what to our knowledge is a new view on various posttranscriptional cellular networks, expanding the concept of ceRNAs and implying possible distant cross talk within the network, with consequences for the interpretation of indirect effects of gene perturbation. PMID:24853754

  17. Epidemic spreading in networks with nonrandom long-range interactions

    NASA Astrophysics Data System (ADS)

    Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

    2011-09-01

    An “infection,” understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both “close” contacts and “casual” encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called “conductance” controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

  18. Stabilization of perturbed Boolean network attractors through compensatory interactions

    PubMed Central

    2014-01-01

    Background Understanding and ameliorating the effects of network damage are of significant interest, due in part to the variety of applications in which network damage is relevant. For example, the effects of genetic mutations can cascade through within-cell signaling and regulatory networks and alter the behavior of cells, possibly leading to a wide variety of diseases. The typical approach to mitigating network perturbations is to consider the compensatory activation or deactivation of system components. Here, we propose a complementary approach wherein interactions are instead modified to alter key regulatory functions and prevent the network damage from triggering a deregulatory cascade. Results We implement this approach in a Boolean dynamic framework, which has been shown to effectively model the behavior of biological regulatory and signaling networks. We show that the method can stabilize any single state (e.g., fixed point attractors or time-averaged representations of multi-state attractors) to be an attractor of the repaired network. We show that the approach is minimalistic in that few modifications are required to provide stability to a chosen attractor and specific in that interventions do not have undesired effects on the attractor. We apply the approach to random Boolean networks, and further show that the method can in some cases successfully repair synchronous limit cycles. We also apply the methodology to case studies from drought-induced signaling in plants and T-LGL leukemia and find that it is successful in both stabilizing desired behavior and in eliminating undesired outcomes. Code is made freely available through the software package BooleanNet. Conclusions The methodology introduced in this report offers a complementary way to manipulating node expression levels. A comprehensive approach to evaluating network manipulation should take an "all of the above" perspective; we anticipate that theoretical studies of interaction modification

  19. Molecular pharmacology of the interaction of anthracyclines with iron.

    PubMed

    Xu, X; Persson, H L; Richardson, D R

    2005-08-01

    Although anthracyclines such as doxorubicin are widely used antitumor agents, a major limitation for their use is the development of cardiomyopathy at high cumulative doses. This severe adverse side effect may be due to interactions with cellular iron metabolism, because iron loading promotes anthracycline-induced cell damage. On the other hand, anthracycline-induced cardiotoxicity is significantly alleviated by iron chelators (e.g., desferrioxamine and dexrazoxane). The molecular mechanisms by which anthracyclines interfere with cellular iron trafficking are complex and still unclear. Doxorubicin can directly bind iron and can perturb iron metabolism by interacting with multiple molecular targets, including the iron regulatory proteins (IRP) 1 and 2. The RNA-binding activity of these molecules regulates synthesis of the transferrin receptor 1 and ferritin, which are crucial proteins involved in iron uptake and storage, respectively. At present, it is not clear whether doxorubicin affects IRP1-RNA-binding activity by intracellular formation of doxorubicinol and/or by generation of the doxorubicin-iron(III) complex. Furthermore, doxorubicin prevents the mobilization of iron from ferritin by a mechanism that may involve lysosomal degradation of this protein. Prevention of iron mobilization from ferritin would probably disturb vital cellular functions as a result of inhibition of essential iron-dependent proteins, such as ribonucleotide reductase. This review discusses the molecular interactions of anthracyclines with iron metabolism and the development of cardioprotective strategies such as iron chelators. PMID:15883202

  20. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model.

    PubMed

    Huang, Xiao-Tai; Zhu, Yuan; Chan, Leanne Lai Hang; Zhao, Zhongying; Yan, Hong

    2016-01-01

    In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at . PMID:26555698

  1. Hazard Interactions and Interaction Networks (Cascades) within Multi-Hazard Methodologies

    NASA Astrophysics Data System (ADS)

    Gill, Joel; Malamud, Bruce D.

    2016-04-01

    Here we combine research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between 'multi-layer single hazard' approaches and 'multi-hazard' approaches that integrate such interactions. This synthesis suggests that ignoring interactions could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. We proceed to present an enhanced multi-hazard framework, through the following steps: (i) describe and define three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment; (ii) outline three types of interaction relationship (triggering, increased probability, and catalysis/impedance); and (iii) assess the importance of networks of interactions (cascades) through case-study examples (based on literature, field observations and semi-structured interviews). We further propose visualisation frameworks to represent these networks of interactions. Our approach reinforces the importance of integrating interactions between natural hazards, anthropogenic processes and technological hazards/disasters into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential, and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

  2. Optimizing a global alignment of protein interaction networks

    PubMed Central

    Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie

    2013-01-01

    Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352

  3. Ecological interaction and phylogeny, studying functionality on composed networks

    NASA Astrophysics Data System (ADS)

    Cruz, Claudia P. T.; Fonseca, Carlos Roberto; Corso, Gilberto

    2012-02-01

    We study a class of composed networks that are formed by two tree networks, TP and TA, whose end points touch each other through a bipartite network BPA. We explore this network using a functional approach. We are interested in how much the topology, or the structure, of TX (X=A or P) determines the links of BPA. This composed structure is a useful model in evolutionary biology, where TP and TA are the phylogenetic trees of plants and animals that interact in an ecological community. We make use of ecological networks of dispersion of fruits, which are formed by frugivorous animals and plants with fruits; the animals, usually birds, eat fruits and disperse their seeds. We analyse how the phylogeny of TX determines or is correlated with BPA using a Monte Carlo approach. We use the phylogenetic distance among elements that interact with a given species to construct an index κ that quantifies the influence of TX over BPA. The algorithm is based on the assumption that interaction matrices that follows a phylogeny of TX have a total phylogenetic distance smaller than the average distance of an ensemble of Monte Carlo realisations. We find that the effect of phylogeny of animal species is more pronounced in the ecological matrix than plant phylogeny.

  4. Simulating market dynamics: interactions between consumer psychology and social networks.

    PubMed

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network). PMID:14761255

  5. Methods for Mapping of Interaction Networks Involving Membrane Proteins

    SciTech Connect

    Hooker, Brian S.; Bigelow, Diana J.; Lin, Chiann Tso

    2007-11-23

    Numerous approaches have been taken to study protein interactions, such as tagged protein complex isolation followed by mass spectrometry, yeast two-hybrid methods, fluorescence resonance energy transfer, surface plasmon resonance, site-directed mutagenesis, and crystallography. Membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is not suitable for the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.

  6. An integrated text mining framework for metabolic interaction network reconstruction.

    PubMed

    Patumcharoenpol, Preecha; Doungpan, Narumol; Meechai, Asawin; Shen, Bairong; Chan, Jonathan H; Vongsangnak, Wanwipa

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module-MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module-MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual

  7. An integrated text mining framework for metabolic interaction network reconstruction

    PubMed Central

    Doungpan, Narumol; Meechai, Asawin; Shen, Bairong

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and

  8. Brain Network Interactions in Auditory, Visual and Linguistic Processing

    ERIC Educational Resources Information Center

    Horwitz, Barry; Braun, Allen R.

    2004-01-01

    In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are…

  9. Characterizing interactions in online social networks during exceptional events

    NASA Astrophysics Data System (ADS)

    Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

    2015-08-01

    Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

  10. Identifying the interactions in a colored dynamical network

    NASA Astrophysics Data System (ADS)

    Wu, Zhao-Yan; Gong, Xiao-Li

    2015-11-01

    The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupling matrices are assumed to be unknown and need to be identified. A corresponding network estimator is designed for identifying the unknown interactions by adopting proper adaptive laws. Based on the Lyapunov function method and Barbalat’s lemma, the obtained result is analytically proved. A colored network coupled with chaotic Lorenz, Chen, and Lü systems is considered as a numerical example to illustrate the effectiveness of the proposed method. Project supported by the National Natural Science Foundation of China (Grant No. 61463022), the Natural Science Foundation of Jiangxi Educational Committee, China (Grant No. GJJ14273), and the Graduate Innovation Fund of Jiangxi Normal University, China (Grant No. YJS2014061).

  11. Molecular interactions of pesticides at the soil-water interface.

    PubMed

    Shirzadi, Azadeh; Simpson, Myrna J; Kumar, Rajeev; Baer, Andrew J; Xu, Yunping; Simpson, André J

    2008-08-01

    High-resolution magic angle spinning (HR-MAS) NMR spectroscopy combined with saturation-transfer double difference (STDD) NMR can be used to analyze the molecular-level interactions of pesticides and whole soils occurring at the soil-water interface. Here 1H HR-MAS STDD NMR has been applied to some common pesticides (trifluralin, acifluorfen, and (4-nitro-3-(trifluoromethyl) phenol) and a pesticide degradation product (1-naphthol). Results indicate that dipolar interactions, H-bonding, hydrophobic associations, and potentially pi-pi interactions are the predominant sorption mechanisms for these molecules at the soil-aqueous interface. It is evident that the physical and chemical characteristics of soil are highly influential in determining the mechanisms of pesticide sorption, as they significantly affect soil conformation. In particular, different binding mechanisms were observed for 1-naphthol in soil swollen using a buffer versus D2O, indicating that the K(oc) alone may not be enough to accurately predict the behavior of a molecule in a real soil environment. Preliminary kinetic-based studies suggest that both the swelling solvent and soil moisture content significantly influence the sequestration of trifluralin. These studies demonstrate that HR-MAS and STDD NMR are powerful and versatile tools which can be applied to expand our knowledge of the mechanistic interactions of agrochemicals at the molecular level. PMID:18754469

  12. Prediction of substrate-enzyme-product interaction based on molecular descriptors and physicochemical properties.

    PubMed

    Niu, Bing; Huang, Guohua; Zheng, Linfeng; Wang, Xueyuan; Chen, Fuxue; Zhang, Yuhui; Huang, Tao

    2013-01-01

    It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test. PMID:24455714

  13. Reactive molecular dynamics of network polymers: Generation, characterization and mechanical properties

    NASA Astrophysics Data System (ADS)

    Shankar, Chandrashekar

    The goal of this research was to gain a fundamental understanding of the properties of networks created by the ring opening metathesis polymerization (ROMP) of dicyclopentadiene (DCPD) used in self-healing materials. To this end we used molecular simulation methods to generate realistic structures of DCPD networks, characterize their structures, and determine their mechanical properties. Density functional theory (DFT) calculations, complemented by structural information derived from molecular dynamics simulations were used to reconstruct experimental Raman spectra and differential scanning calorimetry (DSC) data. We performed coarse-grained simulations comparing networks generated via the ROMP reaction process and compared them to those generated via a RANDOM process, which led to the fundamental realization that the polymer topology has a unique influence on the network properties. We carried out fully atomistic simulations of DCPD using a novel algorithm for recreating ROMP reactions of DCPD molecules. Mechanical properties derived from these atomistic networks are in excellent agreement with those obtained from coarse-grained simulations in which interactions between nodes are subject to angular constraints. This comparison provides self-consistent validation of our simulation results and helps to identify the level of detail necessary for the coarse-grained interaction model. Simulations suggest networks can classified into three stages: fluid-like, rubber-like or glass-like delineated by two thresholds in degree of reaction alpha: The onset of finite magnitudes for the Young's modulus, alphaY, and the departure of the Poisson ration from 0.5, alphaP. In each stage the polymer exhibits a different predominant mechanical response to deformation. At low alpha < alphaY it flows. At alpha Y < alpha < alphaP the response is entropic with no change in internal energy. At alpha > alphaP the response is enthalpic change in internal energy. We developed graph theory

  14. How People Interact in Evolving Online Affiliation Networks

    NASA Astrophysics Data System (ADS)

    Gallos, Lazaros K.; Rybski, Diego; Liljeros, Fredrik; Havlin, Shlomo; Makse, Hernán A.

    2012-07-01

    The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.

  15. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

    PubMed

    Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

    2012-01-21

    : Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry. PMID:22037044

  16. Visualizing Gene - Interactions within the Rice and Maize Network

    NASA Astrophysics Data System (ADS)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  17. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed. PMID:25958956

  18. Evaluating Molecular Interactions in Polycaprolactone-Biomineralized Hydroxyapatite Nanocomposites using Steered Molecular Dynamics

    NASA Astrophysics Data System (ADS)

    Sharma, Anurag; Payne, Scott; Katti, Kalpana S.; Katti, Dinesh R.

    2015-04-01

    An experimental and modeling study of a complex nanoclay-based polymeric scaffold system is presented here. A representative molecular model of polymeric nanocomposite scaffold system for bone tissue engineering applications was developed. Polymeric scaffolds were synthesized using organically modified montmorillonite clay (OMMT) with biomineralized hydroxyapatite and polycaprolactone (OMMT-HAP-PCL). The OMMT-HAP-PCL representative model was constructed and validated using transmission electron microscopy, x-ray diffraction and material density results. We observed strong molecular interactions between OMMT, hydroxyapatite (HAP) and polycaprolactone (PCL) in the OMMT-HAP-PCL system. Attractive and repulsive interactions between PCL and different constituents of OMMT and HAP indicate influence of OMMT-HAP on PCL. Polymeric scaffolds were found to have improved nanomechanical properties as compared to pristine PCL due to the introduction of OMMT-HAP. Stress-strain response for the representative OMMT-HAP-PCL model was evaluated using constant force steered molecular dynamics (SMD) simulations. Two distinct stress-strain responses observed in the system indicate a two-phase nanomechanical behavior of OMMT-HAP-PCL obtained at low and high applied stresses. The results obtained from the MD and SMD simulations provide quantitative understanding of molecular interactions between different constituents of OMMT, HAP and PCL and mechanical response in the OMMT-HAP-PCL system.

  19. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring

    PubMed Central

    Jiang, Xia; Jao, Jeremy; Neapolitan, Richard

    2015-01-01

    Background The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS) datasets, which involve millions of single nucleotide polymorphism (SNPs), where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects. Methodology/Findings We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer’s dataset, we investigated the performance of MBS-IGain. Conclusions/Significance When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer’s dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is

  20. Cancer Missense Mutations Alter Binding Properties of Proteins and Their Interaction Networks

    PubMed Central

    Nishi, Hafumi; Tyagi, Manoj; Teng, Shaolei; Shoemaker, Benjamin A.; Hashimoto, Kosuke; Alexov, Emil; Wuchty, Stefan; Panchenko, Anna R.

    2013-01-01

    Many studies have shown that missense mutations might play an important role in carcinogenesis. However, the extent to which cancer mutations might affect biomolecular interactions remains unclear. Here, we map glioblastoma missense mutations on the human protein interactome, model the structures of affected protein complexes and decipher the effect of mutations on protein-protein, protein-nucleic acid and protein-ion binding interfaces. Although some missense mutations over-stabilize protein complexes, we found that the overall effect of mutations is destabilizing, mostly affecting the electrostatic component of binding energy. We also showed that mutations on interfaces resulted in more drastic changes of amino acid physico-chemical properties than mutations occurring outside the interfaces. Analysis of glioblastoma mutations on interfaces allowed us to stratify cancer-related interactions, identify potential driver genes, and propose two dozen additional cancer biomarkers, including those specific to functions of the nervous system. Such an analysis also offered insight into the molecular mechanism of the phenotypic outcomes of mutations, including effects on complex stability, activity, binding and turnover rate. As a result of mutated protein and gene network analysis, we observed that interactions of proteins with mutations mapped on interfaces had higher bottleneck properties compared to interactions with mutations elsewhere on the protein or unaffected interactions. Such observations suggest that genes with mutations directly affecting protein binding properties are preferably located in central network positions and may influence critical nodes and edges in signal transduction networks. PMID:23799087

  1. Quantitative analysis of genomic element interactions by molecular colony technique

    PubMed Central

    Gavrilov, Alexey A.; Chetverina, Helena V.; Chermnykh, Elina S.; Razin, Sergey V.; Chetverin, Alexander B.

    2014-01-01

    Distant genomic elements were found to interact within the folded eukaryotic genome. However, the used experimental approach (chromosome conformation capture, 3C) enables neither determination of the percentage of cells in which the interactions occur nor demonstration of simultaneous interaction of >2 genomic elements. Each of the above can be done using in-gel replication of interacting DNA segments, the technique reported here. Chromatin fragments released from formaldehyde–cross-linked cells by sodium dodecyl sulfate extraction and sonication are distributed in a polyacrylamide gel layer followed by amplification of selected test regions directly in the gel by multiplex polymerase chain reaction. The fragments that have been cross-linked and separate fragments give rise to multi- and monocomponent molecular colonies, respectively, which can be distinguished and counted. Using in-gel replication of interacting DNA segments, we demonstrate that in the material from mouse erythroid cells, the majority of fragments containing the promoters of active β-globin genes and their remote enhancers do not form complexes stable enough to survive sodium dodecyl sulfate extraction and sonication. This indicates that either these elements do not interact directly in the majority of cells at a given time moment, or the formed DNA–protein complex cannot be stabilized by formaldehyde cross-linking. PMID:24369423

  2. The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction.

    PubMed

    Fang, Yi; Sun, Mengtian; Dai, Guoxian; Ramain, Karthik

    2016-01-01

    Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods. PMID:26886733

  3. Comprehensive Characterization of Molecular Interactions Based on Nanomechanics

    PubMed Central

    Lang, Hans-Peter; Gerber, Christoph; Hegner, Martin

    2008-01-01

    Molecular interaction is a key concept in our understanding of the biological mechanisms of life. Two physical properties change when one molecular partner binds to another. Firstly, the masses combine and secondly, the structure of at least one binding partner is altered, mechanically transducing the binding into subsequent biological reactions. Here we present a nanomechanical micro-array technique for bio-medical research, which not only monitors the binding of effector molecules to their target but also the subsequent effect on a biological system in vitro. This label-free and real-time method directly and simultaneously tracks mass and nanomechanical changes at the sensor interface using micro-cantilever technology. To prove the concept we measured lipid vesicle (∼748*106 Da) adsorption on the sensor interface followed by subsequent binding of the bee venom peptide melittin (2840 Da) to the vesicles. The results show the high dynamic range of the instrument and that measuring the mass and structural changes simultaneously allow a comprehensive discussion of molecular interactions. PMID:18978938

  4. Interaction between Cassiopeia A and nearby molecular clouds

    SciTech Connect

    Kilpatrick, C. D.; Bieging, J. H.; Rieke, G. H.

    2014-12-01

    We present spectroscopy of the supernova remnant Cassiopeia A (Cas A) observed at infrared wavelengths from 10 to 40 μm with the Spitzer Space Telescope and at millimeter wavelengths in {sup 12}CO and {sup 13}CO J =2-1 (230 and 220 GHz) with the Heinrich Hertz Submillimeter Telescope. The IR spectra demonstrate high-velocity features toward a molecular cloud coincident with a region of bright radio continuum emission along the northern shock front of Cas A. The millimeter observations indicate that CO emission is broadened by a factor of two in some clouds toward Cas A, particularly to the south and west. We believe that these features trace interactions between the Cas A shock front and nearby molecular clouds. In addition, some of the molecular clouds that exhibit broadening in CO lie 1'-2' away from the furthest extent of the supernova remnant shock front. We propose that this material may be accelerated by ejecta with velocity significantly larger than the observed free-expansion velocity of the Cas A shock front. These observations may trace cloud interactions with fast-moving outflows such as the bipolar outflow along the southwest to northeast axis of the Cas A supernova remnant, as well as fast-moving knots seen emerging in other directions.

  5. Confinement properties of 2D porous molecular networks on metal surfaces

    NASA Astrophysics Data System (ADS)

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-01

    Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article.

  6. Confinement properties of 2D porous molecular networks on metal surfaces.

    PubMed

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-20

    Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article

  7. Graphical Features of Functional Genes in Human Protein Interaction Network.

    PubMed

    Wang, Pei; Chen, Yao; Lü, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  8. Digital Ecology: Coexistence and Domination among Interacting Networks

    PubMed Central

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-01-01

    The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations. PMID:25988318

  9. Digital Ecology: Coexistence and Domination among Interacting Networks

    NASA Astrophysics Data System (ADS)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-05-01

    The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.

  10. Graph theory and stability analysis of protein complex interaction networks.

    PubMed

    Huang, Chien-Hung; Chen, Teng-Hung; Ng, Ka-Lok

    2016-04-01

    Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov-Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological-based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree-based, betweenness-based and brokering-coefficient-based perturbations have the largest effect on network stability. PMID:26997661

  11. PhIN: A Protein Pharmacology Interaction Network Database

    PubMed Central

    Wang, Z; Li, J; Dang, R; Liang, L; Lin, J

    2015-01-01

    Network pharmacology is a new and hot concept in drug discovery for its ability to investigate the complexity of polypharmacology, and becomes more and more important in drug development. Here we report a protein pharmacology interaction network database (PhIN), aiming to assist multitarget drug discovery by providing comprehensive and flexible network pharmacology analysis. Overall, PhIN contains 1,126,060 target–target interaction pairs in terms of shared compounds and 3,428,020 pairs in terms of shared scaffolds, which involve 12,419,700 activity data, 9,414 targets, 314 viral targets, 652 pathways, 1,359,400 compounds, and 309,556 scaffolds. Using PhIN, users can obtain interacting target networks within or across human pathways, between human and virus, by defining the number of shared compounds or scaffolds under an activity cutoff. We expect PhIN to be a useful tool for multitarget drug development. PhIN is freely available at http://cadd.pharmacy.nankai.edu.cn/phin/. PMID:26225242

  12. Antituberculosis Activity of the Molecular Libraries Screening Center Network Library

    PubMed Central

    MADDRY, JOSEPH A.; ANANTHAN, SUBRAMANIAM; GOLDMAN, ROBERT C.; HOBRATH, JUDITH V.; KWONG, CECIL D.; MADDOX, CLINTON; RASMUSSEN, LYNN; REYNOLDS, ROBERT C.; SECRIST, JOHN A.; SOSA, MELINDA I.; WHITE, E. LUCILE; ZHANG, WEI

    2009-01-01

    SUMMARY There is an urgent need for the discovery and development of new antitubercular agents that target novel biochemical pathways and treat drug-resistant forms of the disease. One approach to addressing this need is through high-throughput screening of drug-like small molecule libraries against the whole bacterium in order to identify a variety of new, active scaffolds that will stimulate additional biological research and drug discovery. Through the Molecular Libraries Screening Center Network, the NIAID Tuberculosis Antimicrobial Acquisition and Coordinating Facility tested a 215,110-compound library against M. tuberculosis strain H37Rv. A medicinal chemistry survey of the results from the screening campaign is reported herein. PMID:19783214

  13. Genetic variants in Alzheimer disease - molecular and brain network approaches.

    PubMed

    Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher J; De Jager, Philip L; Bennett, David A

    2016-07-01

    Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

  14. Information theory and signal transduction systems: from molecular information processing to network inference.

    PubMed

    Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H

    2014-11-01

    Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. PMID:24953199

  15. Dissolution mechanism of cellulose in N,N-dimethylacetamide/lithium chloride: revisiting through molecular interactions.

    PubMed

    Zhang, Chao; Liu, Ruigang; Xiang, Junfeng; Kang, Hongliang; Liu, Zhijing; Huang, Yong

    2014-08-01

    Understanding the interactions between solvent molecules and cellulose at a molecular level is still not fully achieved in cellulose/N,N-dimethylacetamide (DMAc)/LiCl system. In this paper, cellobiose was used as the model compound of cellulose to investigate the interactions in cellulose/DMAc/LiCl solution by using Fourier transform infrared spectroscopy (FTIR), (13)C, (35)Cl, and (7)Li nuclear magnetic resonance (NMR) spectroscopy and conductivity measurements. It was found that when cellulose is dissolved in DMAc/LiCl cosolvent system, the hydroxyl protons of cellulose form strong hydrogen bonds with the Cl(-), during which the intermolecular hydrogen bonding networks of cellulose is broken with simultaneous splitting of the Li(+)-Cl(-) ion pairs. Simultaneously, the Li(+) cations are further solvated by free DMAc molecules, which accompany the hydrogen-bonded Cl(-) to meet electric balance. Thereafter, the cellulose chains are dispersed in molecular level in the solvent system to form homogeneous solution. This work clarifies the interactions in the cellulose/DMAc/LiCl solution at molecular level and the dissolution mechanism of cellulose in DMAc/LiCl, which is important for understanding the principle for selecting and designing new cellulose solvent systems. PMID:25026263

  16. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

    PubMed

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  17. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    PubMed Central

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  18. A general, accurate procedure for calculating molecular interaction force.

    PubMed

    Yang, Pinghai; Qian, Xiaoping

    2009-09-15

    The determination of molecular interaction forces, e.g., van der Waals force, between macroscopic bodies is of fundamental importance for understanding sintering, adhesion and fracture processes. In this paper, we develop an accurate, general procedure for van der Waals force calculation. This approach extends a surface formulation that converts a six-dimensional (6D) volume integral into a 4D surface integral for the force calculation. It uses non-uniform rational B-spline (NURBS) surfaces to represent object surfaces. Surface integrals are then done on the parametric domain of the NURBS surfaces. It has combined advantages of NURBS surface representation and surface formulation, including (1) molecular interactions between arbitrary-shaped objects can be represented and evaluated by the NURBS model further common geometries such as spheres, cones, planes can be represented exactly and interaction forces are thus calculated accurately; (2) calculation efficiency is improved by converting the volume integral to the surface integral. This approach is implemented and validated via its comparison with analytical solutions for simple geometries. Calculation of van der Waals force between complex geometries with surface roughness is also demonstrated. A tutorial on the NURBS approach is given in Appendix A. PMID:19596335

  19. Battle and Ballet: Molecular Interactions between the Sexes in Drosophila

    PubMed Central

    2009-01-01

    Varied and fascinating interactions occur between males and females to lead to the production of progeny. Interactions between the sexes continue even after the act of mating—but at the molecular and cellular level instead of between individual animals. Molecules transferred from males to females during mating (via the seminal fluid) exert potent effects on females’ physiology and (at least in some animals) on behavior. Taking advantage of genetic, genomic, and biochemical tools for Drosophila, we investigate molecular interactions that underlie this form of chemical communication. Recent data show that molecules and cells from both sexes participate in this “ballet,” facilitating the mutually beneficial outcome of increased progeny production. Examples to be presented include the storage and utilization of sperm in the mated female, and a proteolytic pathway that begins in the male but ends in the female and involves both male and female contributions. Despite the joint benefit of increased progeny production, the “interests” of the mating male can differ from those of his mate. Over evolutionary time this disconnect can, in theory, precipitate a “battle” between the sexes, potentially leading to the rapid sequence changes that have been observed for some seminal proteins across species. PMID:19349638

  20. Rapid molecular evolution across amniotes of the IIS/TOR network

    PubMed Central

    McGaugh, Suzanne E.; Bronikowski, Anne M.; Kuo, Chih-Horng; Reding, Dawn M.; Addis, Elizabeth A.; Flagel, Lex E.; Janzen, Fredric J.

    2015-01-01

    The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades. PMID:25991861

  1. Rapid molecular evolution across amniotes of the IIS/TOR network.

    PubMed

    McGaugh, Suzanne E; Bronikowski, Anne M; Kuo, Chih-Horng; Reding, Dawn M; Addis, Elizabeth A; Flagel, Lex E; Janzen, Fredric J; Schwartz, Tonia S

    2015-06-01

    The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades. PMID:25991861

  2. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

    PubMed Central

    Di Roberto, Raphaël B.; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G.

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  3. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    PubMed Central

    Chakraborty, Sandip

    2016-01-01

    Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes' adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another. PMID:27119079

  4. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions.

    PubMed

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  5. [Molecular mechanisms of the plague pathogenic agent interaction with invertebrates].

    PubMed

    Kutyrev, V V; Eroshenko, G A; Popov, N V; Vidiaeva, N A; Konnov, N P

    2009-01-01

    Microbe Russian Anti-Plague Research Institute, Saratov, Russia The literature data and experimental results of the authors on the molecular basis of plague agent interaction with invertebrates are discussed. The details of the plague agent life cycle, its genome organization, and molecular genetic mechanisms of its survival in flea vector and on the nematode cuticule are discussed. The experimental data about the ability to form biofilms at abiotic and biotic surfaces in the Yersinia pestis strains of the main and non-main subspecies are presented. Mechanisms of horizontal and vertical transmission of plague agent are considered. The suggestion about participation of the new member in the complex parasitic biocenosis (nematode, vector parasite) is put forward. PMID:20050160

  6. Electron-phonon interaction within classical molecular dynamics

    NASA Astrophysics Data System (ADS)

    Tamm, A.; Samolyuk, G.; Correa, A. A.; Klintenberg, M.; Aabloo, A.; Caro, A.

    2016-07-01

    We present a model for nonadiabatic classical molecular dynamics simulations that captures with high accuracy the wave-vector q dependence of the phonon lifetimes, in agreement with quantum mechanics calculations. It is based on a local view of the e -ph interaction where individual atom dynamics couples to electrons via a damping term that is obtained as the low-velocity limit of the stopping power of a moving ion in a host. The model is parameter free, as its components are derived from ab initio-type calculations, is readily extended to the case of alloys, and is adequate for large-scale molecular dynamics computer simulations. We also show how this model removes some oversimplifications of the traditional ionic damped dynamics commonly used to describe situations beyond the Born-Oppenheimer approximation.

  7. Molecular Indicators of Soil Humification and Interaction with Heavy Metals

    SciTech Connect

    Fan, Teresa W.-M.; Higashi, Richard M.; Cassel, Teresa; Green, Peter; Lane, Andrew N.

    2003-03-26

    For stabilization of heavy metals at contaminated sites, interaction of soil organic matter (SOM) with heavy metal ions is critically important for long-term sustainability, a factor that is poorly understood at the molecular level. Using 13C- and 15N-labeled soil humates (HS), we investigated the turnover of five organic amendments (celluose, wheat straw, pine shavings, chitin and bone meal) in relation to heavy metal ion leaching in soil column experiments. The labeled molecular substructures in HS were examined by multinuclear 2-D NMR and pyrolysis GC-MS while the element profile in the leachates was analyzed by ICP-MS. Preliminary analysis revealed that peptidic and polysaccharidic structures were highly enriched, which suggests their microbial origin. Cd(II) leaching was significantly attenuated with humification of lignocellulosic materials. Correlation of 13C and 15N turnovers of HS substructures to metal leaching is underway.

  8. Electron-phonon interaction within classical molecular dynamics

    DOE PAGESBeta

    Tamm, A.; Samolyuk, G.; Correa, A. A.; Klintenberg, M.; Aabloo, A.; Caro, A.

    2016-07-14

    Here, we present a model for nonadiabatic classical molecular dynamics simulations that captures with high accuracy the wave-vector q dependence of the phonon lifetimes, in agreement with quantum mechanics calculations. It is based on a local view of the e-ph interaction where individual atom dynamics couples to electrons via a damping term that is obtained as the low-velocity limit of the stopping power of a moving ion in a host. The model is parameter free, as its components are derived from ab initio-type calculations, is readily extended to the case of alloys, and is adequate for large-scale molecular dynamics computermore » simulations. We also show how this model removes some oversimplifications of the traditional ionic damped dynamics commonly used to describe situations beyond the Born-Oppenheimer approximation.« less

  9. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters

    PubMed Central

    Hanna, Eileen Marie; Zaki, Nazar; Amin, Amr

    2015-01-01

    Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present “DyCluster”, a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster. PMID:26641660

  10. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.

    PubMed

    Hanna, Eileen Marie; Zaki, Nazar; Amin, Amr

    2015-01-01

    Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present "DyCluster", a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster. PMID:26641660

  11. Research on Single Nucleotide Polymorphisms Interaction Detection from Network Perspective

    PubMed Central

    Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun

    2015-01-01

    Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions. PMID:25763929

  12. Research on single nucleotide polymorphisms interaction detection from network perspective.

    PubMed

    Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun

    2015-01-01

    Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions. PMID:25763929

  13. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates

    PubMed Central

    Boloc, Daniel; Castillo-Lara, Sergio; Marfany, Gemma; Gonzàlez-Duarte, Roser; Abril, Josep F.

    2015-01-01

    Background Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. Methodology We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. Conclusions In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates. PMID:26267445

  14. Molecular nutrition: Interaction of nutrients, gene regulations and performances.

    PubMed

    Sato, Kan

    2016-07-01

    Nutrition deals with ingestion of foods, digestion, absorption, transport of nutrients, intermediary metabolism, underlying anabolism and catabolism, and excretion of unabsorbed nutrients and metabolites. In addition, nutrition interacts with gene expressions, which are involved in the regulation of animal performances. Our laboratory is concerned with the improvement of animal productions, such as milks, meats and eggs, with molecular nutritional aspects. The present review shows overviews on the nutritional regulation of metabolism, physiological functions and gene expressions to improve animal production in chickens and dairy cows. PMID:27110862

  15. MOLVIE: an interactive visualization environment for molecular structures.

    PubMed

    Sun, Huandong; Li, Ming; Xu, Ying

    2003-05-01

    A Molecular visualization interactive environment (MOLVIE), is designed to display three-dimensional (3D) structures of molecules and support the structural analysis and research on proteins. The paper presents the features, design considerations and applications of MOLVIE, especially the new functions used to compare the structures of two molecules and view the partial fragment of a molecule. Being developed in JAVA, MOLVIE is platform-independent. Moreover, it may run on a webpage as an applet for remote users. MOLVIE is available at http://www.cs.ucsb.edu/~mli/Bioinf/software/index.html. PMID:12725967

  16. Interactions between halide anions and a molecular hydrophobic interface.

    PubMed

    Rankin, Blake M; Hands, Michael D; Wilcox, David S; Fega, K Rebecca; Slipchenko, Lyudmila V; Ben-Amotz, Dor

    2013-01-01

    Interactions between halide ions (fluoride and iodide) and t-butyl alcohol (TBA) dissolved in water are probed using a recently developed hydration-shell spectroscopic technique and theoretical cluster and liquid calculations. High ignal-to-noise Raman spectroscopic measurements are combined with multivariate curve resolution (Raman-MCR) to reveal that while there is little interaction between aqueous fluoride ions and TBA, iodide ions break down the tetrahedral hydration-shell structure of TBA and produce a red-shift in its CH stretch frequency, in good agreement with the theoretical effective fragment potential (EFP) molecular dynamics simulations and hybrid quantum/EFP frequency calculations. The results imply that there is a significantly larger probability of finding iodide than fluoride in the first hydration shell of TBA, although the local iodide concentration is apparently not as high as in the surrounding bulk aqueous NaI solution. PMID:23795504

  17. The Intraviral Protein Interaction Network of Hepatitis C Virus*

    PubMed Central

    Hagen, Nicole; Bayer, Karen; Rösch, Kathrin; Schindler, Michael

    2014-01-01

    Hepatitis C virus (HCV) is a global health problem and one of the main reasons for chronic liver diseases such as cirrhosis and hepatocellular carcinoma. The HCV genome is translated into a polyprotein which is proteolytically processed into 10 viral proteins. The interactome of the HCV proteins with the host cell has been worked out; however, it remains unclear how viral proteins interact with each other. We aimed to generate the interaction network of these 10 HCV proteins using a flow-cytometry-based FRET assay established in our laboratory (Banning, C., Votteler, J., Hoffmann, D., Koppensteiner, H., Warmer, M., Reimer, R., Kirchhoff, F., Schubert, U., Hauber, J., and Schindler, M. (2010) A flow cytometry-based FRET assay to identify and analyse protein-protein interactions in living cells. PLoS One 5, e9344). HCV proteins were constructed as fusions with the chromophores CFP and YFP. All HCV fusions were expressed and localized to specific subcellular compartments, indicating that they were functional. FACS-FRET measurements identified a total of 20 interactions; 13 of these were previously described and have now been confirmed in living cells via our method. Among the seven novel protein binding pairs, HCV p7 plays a pivotal role. It binds to the HCV capsid protein Core and the two glycoproteins E1 and E2. These interplays were further demonstrated in the relevant context of Huh7.5 liver cells expressing infectious HCV. Our work demonstrates the feasibility of rapidly generating small interaction networks via FACS-FRET and defines the network of intra-HCV protein interactions. Furthermore, our data support an important role of p7 in HCV assembly. PMID:24797426

  18. Auditing Medical Records Accesses via Healthcare Interaction Networks

    PubMed Central

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Healthcare organizations are deploying increasingly complex clinical information systems to support patient care. Traditional information security practices (e.g., role-based access control) are embedded in enterprise-level systems, but are insufficient to ensure patient privacy. This is due, in part, to the dynamic nature of healthcare, which makes it difficult to predict which care providers need access to what and when. In this paper, we show that modeling operations at a higher level of granularity (e.g., the departmental level) are stable in the context of a relational network, which may enable more effective auditing strategies. We study three months of access logs from a large academic medical center to illustrate that departmental interaction networks exhibit certain invariants, such as the number, strength, and reciprocity of relationships. We further show that the relations extracted from the network can be leveraged to assess the extent to which a patient’s care satisfies expected organizational behavior. PMID:23304277

  19. Auditing medical records accesses via healthcare interaction networks.

    PubMed

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Healthcare organizations are deploying increasingly complex clinical information systems to support patient care. Traditional information security practices (e.g., role-based access control) are embedded in enterprise-level systems, but are insufficient to ensure patient privacy. This is due, in part, to the dynamic nature of healthcare, which makes it difficult to predict which care providers need access to what and when. In this paper, we show that modeling operations at a higher level of granularity (e.g., the departmental level) are stable in the context of a relational network, which may enable more effective auditing strategies. We study three months of access logs from a large academic medical center to illustrate that departmental interaction networks exhibit certain invariants, such as the number, strength, and reciprocity of relationships. We further show that the relations extracted from the network can be leveraged to assess the extent to which a patient's care satisfies expected organizational behavior. PMID:23304277

  20. Network representation of protein interactions-Experimental results.

    PubMed

    Kurzbach, Dennis; Flamm, Andrea G; Sára, Tomáš

    2016-09-01

    A graph theoretical analysis of nuclear magnetic resonance (NMR) data of six different protein interactions has been presented. The representation of the protein interaction data as a graph or network reveals that all of the studied interactions are based on a common functional concept. They all involve a single densely packed hub of functionally correlated residues that mediate the ligand binding events. This is found independent of the kind of protein (folded or unfolded) or ligand (protein, polymer or small molecule). Furthermore, the power of the graph analysis is demonstrated at the examples of the Calmodulin (CaM)/Calcium and the Cold Shock Protein A (CspA)/RNA interaction. The presented approach enables the precise determination of multiple binding sites for the respective ligand molecules. PMID:27272395

  1. Computational analysis of protein interaction networks for infectious diseases.

    PubMed

    Pan, Archana; Lahiri, Chandrajit; Rajendiran, Anjana; Shanmugham, Buvaneswari

    2016-05-01

    Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases. PMID:26261187

  2. Reconstruction and Application of Protein–Protein Interaction Network

    PubMed Central

    Hao, Tong; Peng, Wei; Wang, Qian; Wang, Bin; Sun, Jinsheng

    2016-01-01

    The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms. PMID:27338356

  3. IAEA activities on atomic, molecular and plasma-material interaction data for fusion

    NASA Astrophysics Data System (ADS)

    Braams, Bastiaan J.; Chung, Hyun-Kyung

    2013-09-01

    The IAEA Atomic and Molecular Data Unit (http://www-amdis.iaea.org/) aims to provide internationally evaluated and recommended data for atomic, molecular and plasma-material interaction (A+M+PMI) processes in fusion research. The Unit organizes technical meetings and coordinates an A+M Data Centre Network (DCN) and a Code Centre Network (CCN). In addition the Unit organizes Coordinated Research Projects (CRPs), for which the objectives are mixed between development of new data and evaluation and recommendation of existing data. In the area of A+M data we are placing new emphasis in our meeting schedule on data evaluation and especially on uncertainties in calculated cross section data and the propagation of uncertainties through structure data and fundamental cross sections to effective rate coefficients. Following a recent meeting of the CCN it is intended to use electron scattering on Be, Ne and N2 as exemplars for study of uncertainties and uncertainty propagation in calculated data; this will be discussed further at the presentation. Please see http://www-amdis.iaea.org/CRP/ for more on our active and planned CRPs, which are concerned with atomic processes in core and edge plasma and with plasma interaction with beryllium-based surfaces and with irradiated tungsten.

  4. A visual review of the interactome of LRRK2: Using deep-curated molecular interaction data to represent biology.

    PubMed

    Porras, Pablo; Duesbury, Margaret; Fabregat, Antonio; Ueffing, Marius; Orchard, Sandra; Gloeckner, Christian Johannes; Hermjakob, Henning

    2015-04-01

    Molecular interaction databases are essential resources that enable access to a wealth of information on associations between proteins and other biomolecules. Network graphs generated from these data provide an understanding of the relationships between different proteins in the cell, and network analysis has become a widespread tool supporting -omics analysis. Meaningfully representing this information remains far from trivial and different databases strive to provide users with detailed records capturing the experimental details behind each piece of interaction evidence. A targeted curation approach is necessary to transfer published data generated by primarily low-throughput techniques into interaction databases. In this review we present an example highlighting the value of both targeted curation and the subsequent effective visualization of detailed features of manually curated interaction information. We have curated interactions involving LRRK2, a protein of largely unknown function linked to familial forms of Parkinson's disease, and hosted the data in the IntAct database. This LRRK2-specific dataset was then used to produce different visualization examples highlighting different aspects of the data: the level of confidence in the interaction based on orthogonal evidence, those interactions found under close-to-native conditions, and the enzyme-substrate relationships in different in vitro enzymatic assays. Finally, pathway annotation taken from the Reactome database was overlaid on top of interaction networks to bring biological functional context to interaction maps. PMID:25648416

  5. A visual review of the interactome of LRRK2: Using deep-curated molecular interaction data to represent biology

    PubMed Central

    Porras, Pablo; Duesbury, Margaret; Fabregat, Antonio; Ueffing, Marius; Orchard, Sandra; Gloeckner, Christian Johannes; Hermjakob, Henning

    2015-01-01

    Molecular interaction databases are essential resources that enable access to a wealth of information on associations between proteins and other biomolecules. Network graphs generated from these data provide an understanding of the relationships between different proteins in the cell, and network analysis has become a widespread tool supporting –omics analysis. Meaningfully representing this information remains far from trivial and different databases strive to provide users with detailed records capturing the experimental details behind each piece of interaction evidence. A targeted curation approach is necessary to transfer published data generated by primarily low-throughput techniques into interaction databases. In this review we present an example highlighting the value of both targeted curation and the subsequent effective visualization of detailed features of manually curated interaction information. We have curated interactions involving LRRK2, a protein of largely unknown function linked to familial forms of Parkinson's disease, and hosted the data in the IntAct database. This LRRK2-specific dataset was then used to produce different visualization examples highlighting different aspects of the data: the level of confidence in the interaction based on orthogonal evidence, those interactions found under close-to-native conditions, and the enzyme–substrate relationships in different in vitro enzymatic assays. Finally, pathway annotation taken from the Reactome database was overlaid on top of interaction networks to bring biological functional context to interaction maps. PMID:25648416

  6. Dynamical networks of person to person interactions from RFID sensor networks

    NASA Astrophysics Data System (ADS)

    Isella, Lorenzo; Cattuto, Ciro; Barrat, Alain

    2010-03-01

    We present a scalable experimental framework for gathering real-time data on face-to-face social interactions with tunable spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We show results on the analysis of the dynamical networks of person-to-person interaction obtained in four high- resolution experiments carried out at different orders of magnitude in community size.

  7. Discovering pathways by orienting edges in protein interaction networks

    PubMed Central

    Gitter, Anthony; Klein-Seetharaman, Judith; Gupta, Anupam; Bar-Joseph, Ziv

    2011-01-01

    Modern experimental technology enables the identification of the sensory proteins that interact with the cells’ environment or various pathogens. Expression and knockdown studies can determine the downstream effects of these interactions. However, when attempting to reconstruct the signaling networks and pathways between these sources and targets, one faces a substantial challenge. Although pathways are directed, high-throughput protein interaction data are undirected. In order to utilize the available data, we need methods that can orient protein interaction edges and discover high-confidence pathways that explain the observed experimental outcomes. We formalize the orientation problem in weighted protein interaction graphs as an optimization problem and present three approximation algorithms based on either weighted Boolean satisfiability solvers or probabilistic assignments. We use these algorithms to identify pathways in yeast. Our approach recovers twice as many known signaling cascades as a recent unoriented signaling pathway prediction technique and over 13 times as many as an existing network orientation algorithm. The discovered paths match several known signaling pathways and suggest new mechanisms that are not currently present in signaling databases. For some pathways, including the pheromone signaling pathway and the high-osmolarity glycerol pathway, our method suggests interesting and novel components that extend current annotations. PMID:21109539

  8. Graphics processing unit-based alignment of protein interaction networks.

    PubMed

    Xie, Jiang; Zhou, Zhonghua; Ma, Jin; Xiang, Chaojuan; Nie, Qing; Zhang, Wu

    2015-08-01

    Network alignment is an important bridge to understanding human protein-protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large-scale networks via sequential computing. In this study, the typical Hungarian-Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2-nearest neighbours (HGA-2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA-2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA-2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large-scale networks are considered. By using HGA-2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods. PMID:26243827

  9. Modeling of Interaction of Hydraulic Fractures in Complex Fracture Networks

    NASA Astrophysics Data System (ADS)

    Kresse, O. 2; Wu, R.; Weng, X.; Gu, H.; Cohen, C.

    2011-12-01

    A recently developed unconventional fracture model (UFM) is able to simulate complex fracture network propagation in a formation with pre-existing natural fractures. Multiple fracture branches can propagate at the same time and intersect/cross each other. Each open fracture exerts additional stresses on the surrounding rock and adjacent fractures, which is often referred to as "stress shadow" effect. The stress shadow can cause significant restriction of fracture width, leading to greater risk of proppant screenout. It can also alter the fracture propagation path and drastically affect fracture network patterns. It is hence critical to properly model the fracture interaction in a complex fracture model. A method for computing the stress shadow in a complex hydraulic fracture network is presented. The method is based on an enhanced 2D Displacement Discontinuity Method (DDM) with correction for finite fracture height. The computed stress field is compared to 3D numerical simulation in a few simple examples and shows the method provides a good approximation for the 3D fracture problem. This stress shadow calculation is incorporated in the UFM. The results for simple cases of two fractures are presented that show the fractures can either attract or expel each other depending on their initial relative positions, and compares favorably with an independent 2D non-planar hydraulic fracture model. Additional examples of both planar and complex fractures propagating from multiple perforation clusters are presented, showing that fracture interaction controls the fracture dimension and propagation pattern. In a formation with no or small stress anisotropy, fracture interaction can lead to dramatic divergence of the fractures as they tend to repel each other. However, when stress anisotropy is large, the fracture propagation direction is dominated by the stress field and fracture turning due to fracture interaction is limited. However, stress shadowing still has a strong effect

  10. DASMI: exchanging, annotating and assessing molecular interaction data

    PubMed Central

    Blankenburg, Hagen; Finn, Robert D.; Prlić, Andreas; Jenkinson, Andrew M.; Ramírez, Fidel; Emig, Dorothea; Schelhorn, Sven-Eric; Büch, Joachim; Lengauer, Thomas; Albrecht, Mario

    2009-01-01

    Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet. Results: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions. Availability: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/. Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data and all figures in color are available at Bioinformatics online. PMID:19420069

  11. Network of epistatic interactions within a yeast snoRNA.

    PubMed

    Puchta, Olga; Cseke, Botond; Czaja, Hubert; Tollervey, David; Sanguinetti, Guido; Kudla, Grzegorz

    2016-05-13

    Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural stability. Many mutations had small individual effects but had large effects in the context of additional mutations, which indicated negative epistasis. Clusters of negative interactions were explained by local thermodynamic threshold effects, whereas positive interactions were enriched among large-effect sites and between base-paired nucleotides. We conclude that high-throughput mapping of intragenic epistasis can identify key structural and functional features of macromolecules. PMID:27080103

  12. Collective prediction of protein functions from protein-protein interaction networks

    PubMed Central

    2014-01-01

    Background Automated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are time-consuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction. Results In this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data. Conclusion Experimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks. PMID:24564855

  13. Using graph-based assessments within socratic tutorials to reveal and refine students' analytical thinking about molecular networks.

    PubMed

    Trujillo, Caleb; Cooper, Melanie M; Klymkowsky, Michael W

    2012-01-01

    Biological systems, from the molecular to the ecological, involve dynamic interaction networks. To examine student thinking about networks we used graphical responses, since they are easier to evaluate for implied, but unarticulated assumptions. Senior college level molecular biology students were presented with simple molecular level scenarios; surprisingly, most students failed to articulate the basic assumptions needed to generate reasonable graphical representations; their graphs often contradicted their explicit assumptions. We then developed a tiered Socratic tutorial based on leading questions designed to provoke metacognitive reflection. The activity is characterized by leading questions (prompts) designed to provoke meta-cognitive reflection. When applied in a group or individual setting, there was clear improvement in targeted areas. Our results highlight the promise of using graphical responses and Socratic prompts in a tutorial context as both a formative assessment for students and an informative feedback system for instructors, in part because graphical responses are relatively easy to evaluate for implied, but unarticulated assumptions. PMID:22419590

  14. Targeting molecular interactions essential for Plasmodium sexual reproduction

    PubMed Central

    Vega-Rodriguez, Joel; Perez-Barreto, Davinia; Ruiz-Reyes, Antonio; Jacobs-Lorena, Marcelo

    2015-01-01

    Summary Malaria remains one of the most devastating infectious diseases, killing up to a million people every year. Whereas much progress has been made in understanding the life cycle of the parasite in the human host and in the mosquito vector, significant gaps of knowledge remain. Fertilization of malaria parasites, a process that takes place in the lumen of the mosquito midgut, is poorly understood and the molecular interactions (receptor–ligand) required for Plasmodium fertilization remain elusive. By use of a phage display library, we identified FG1 (Female Gamete peptide 1), a peptide that binds specifically to the surface of female Plasmodium berghei gametes. Importantly, FG1 but not a scrambled version of the peptide, strongly reduces P. berghei oocyst formation by interfering with fertilization. In addition, FG1 also inhibits P. falciparum oocyst formation suggesting that the peptide binds to a molecule on the surface of the female gamete whose structure is conserved. Identification of the molecular interactions disrupted by the FG1 peptide may lead to the development of novel malaria transmission-blocking strategies. PMID:25944054

  15. Molecular dynamics simulation of complex plasmas: interaction of nonlinear waves

    NASA Astrophysics Data System (ADS)

    Durniak, Celine; Samsonov, Dmitry

    2008-11-01

    Complex plasmas consist of micron sized microspheres immersed into ordinary ion-electron plasmas. They exist in solid, liquid, gaseous states and exhibit a range of dynamic phenomena such as waves, solitons, phase transitions, heat transfer. These phenomena can be modelled in complex plasmas at the microscopic or ``molecular'' scale, which is almost impossible in ordinary solids and liquids. We simulate a monolayer complex plasma consisting of 3000 negatively-charged particles (or grains) with the help of molecular dynamics computer simulations. The equations of grain motion are solved using a 5^th order Runge Kutta method taking into account interaction of every grain with each other via a Yukawa potential. The grains are confined more strongly in the vertical direction than in the horizontal. After seeding the grains randomly the code is run until the equilibrium is reached as the grain kinetics energy reduces due to damping force equal to the neutral friction in the experiments and a monolayer crystal lattice is formed. Then we investigate interactions between nonlinear waves in a monolayer strongly coupled complex plasma moving in three dimensions. Different excitations are applied during a short time symmetrically on both sides of the lattice. Structural properties and nonlinear waves characteristics are examined as the pulses propagate across the complex plasma in opposite directions.

  16. MiasDB: A Database of Molecular Interactions Associated with Alternative Splicing of Human Pre-mRNAs

    PubMed Central

    Xing, Yongqiang; Zhao, Xiujuan; Yu, Tao; Liang, Dong; Li, Jun; Wei, Guanyun; Liu, Guoqing; Cui, Xiangjun; Zhao, Hongyu; Cai, Lu

    2016-01-01

    Alternative splicing (AS) is pervasive in human multi-exon genes and is a major contributor to expansion of the transcriptome and proteome diversity. The accurate recognition of alternative splice sites is regulated by information contained in networks of protein-protein and protein-RNA interactions. However, the mechanisms leading to splice site selection are not fully understood. Although numerous databases have been built to describe AS, molecular interaction databases associated with AS have only recently emerged. In this study, we present a new database, MiasDB, that provides a description of molecular interactions associated with human AS events. This database covers 938 interactions between human splicing factors, RNA elements, transcription factors, kinases and modified histones for 173 human AS events. Every entry includes the interaction partners, interaction type, experimental methods, AS type, tissue specificity or disease-relevant information, a simple description of the functionally tested interaction in the AS event and references. The database can be queried easily using a web server (http://47.88.84.236/Miasdb). We display some interaction figures for several genes. With this database, users can view the regulation network describing AS events for 12 given genes. PMID:27167218

  17. Network Complexity and Parametric Simplicity for Cargo Transport by Two Molecular Motors

    NASA Astrophysics Data System (ADS)

    Keller, Corina; Berger, Florian; Liepelt, Steffen; Lipowsky, Reinhard

    2013-01-01

    Cargo transport by two molecular motors is studied by constructing a chemomechanical network for the whole transport system and analyzing the cargo and motor trajectories generated by this network. The theoretical description starts from the different nucleotide states of a single motor supplemented by chemical and mechanical transitions between these states. As an instructive example, we focus on kinesin-1, for which a detailed single-motor network has been developed previously. This network incorporates the chemical transitions arising from ATP hydrolysis on both motor heads. In addition, both the chemical and the mechanical transition rates of a single kinesin motor were found to depend on the load force experienced by the motor. When two such motors are attached via their stalks to a cargo particle, they become elastically coupled. This coupling can be effectively described by an elastic spring between the two motors. The spring extension, which is given by the deviation of the actual spring length from its rest length, determines the mutual interaction force between the motors and, thus, affects all chemical and mechanical transition rates of both motors. As a result, cargo transport by two motors leads to a combined chemomechanical network, which is quite complex and contains a large number of motor cycles. However, apart from the single motor parameters, this complex network involves only two additional parameters: (i) the spring constant of the elastic coupling between the motors and (ii) the rebinding rate for an unbound motor. We show that these two parameters can be determined directly from cargo trajectories and/or trajectories of individual motors. Both types of trajectories are accessible to experiment and, thus, can be used to obtain a complete set of parameters for cargo transport by two motors.

  18. Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

    PubMed Central

    Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

    2016-01-01

    A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

  19. Role of water states on water uptake and proton transport in Nafion using molecular simulations and bimodal network

    SciTech Connect

    Hwang, Gi Suk; Kaviany, Massoud; Gostick, Jeffrey T.; Kientiz, Brian; Weber, Adam Z.; Kim, Moo Hwan

    2011-04-07

    In this paper, using molecular simulations and a bimodal-domain network, the role of water state on Nafion water uptake and water and proton transport is investigated. Although the smaller domains provide moderate transport pathways, their effectiveness remains low due to strong, resistive water molecules/domain surface interactions. Finally, the water occupancy of the larger domains yields bulk-like water, and causes the observed transition in the water uptake and significant increases in transport properties.

  20. Damage to the Salience Network and Interactions with the Default Mode Network

    PubMed Central

    Jilka, Sagar R.; Scott, Gregory; Ham, Timothy; Pickering, Alan; Bonnelle, Valerie; Braga, Rodrigo M.; Leech, Robert

    2014-01-01

    Interactions between the Salience Network (SN) and the Default Mode Network (DMN) are thought to be important for cognitive control. However, evidence for a causal relationship between the networks is limited. Previously, we have reported that traumatic damage to white matter tracts within the SN predicts abnormal DMN function. Here we investigate the effect of this damage on network interactions that accompany changing motor control. We initially used fMRI of the Stop Signal Task to study response inhibition in humans. In healthy subjects, functional connectivity (FC) between the right anterior insula (rAI), a key node of the SN, and the DMN transiently increased during stopping. This change in FC was not seen in a group of traumatic brain injury (TBI) patients with impaired cognitive control. Furthermore, the amount of SN tract damage negatively correlated with FC between the networks. We confirmed these findings in a second group of TBI patients. Here, switching rather than inhibiting a motor response: (1) was accompanied by a similar increase in network FC in healthy controls; (2) was not seen in TBI patients; and (3) tract damage after TBI again correlated with FC breakdown. This shows that coupling between the rAI and DMN increases with cognitive control and that damage within the SN impairs this dynamic network interaction. This work provides compelling evidence for a model of cognitive control where the SN is involved in the attentional capture of salient external stimuli and signals the DMN to reduce its activity when attention is externally focused. PMID:25122883

  1. Calsyntenin-3 Molecular Architecture and Interaction with Neurexin 1α*

    PubMed Central

    Lu, Zhuoyang; Wang, Yun; Chen, Fang; Tong, Huimin; Reddy, M. V. V. V. Sekhar; Luo, Lin; Seshadrinathan, Suchithra; Zhang, Lei; Holthauzen, Luis Marcelo F.; Craig, Ann Marie; Ren, Gang; Rudenko, Gabby

    2014-01-01

    Calsyntenin 3 (Cstn3 or Clstn3), a recently identified synaptic organizer, promotes the development of synapses. Cstn3 localizes to the postsynaptic membrane and triggers presynaptic differentiation. Calsyntenin members play an evolutionarily conserved role in memory and learning. Cstn3 was recently shown in cell-based assays to interact with neurexin 1α (n1α), a synaptic organizer that is implicated in neuropsychiatric disease. Interaction would permit Cstn3 and n1α to form a trans-synaptic complex and promote synaptic differentiation. However, it is contentious whether Cstn3 binds n1α directly. To understand the structure and function of Cstn3, we determined its architecture by electron microscopy and delineated the interaction between Cstn3 and n1α biochemically and biophysically. We show that Cstn3 ectodomains form monomers as well as tetramers that are stabilized by disulfide bonds and Ca2+, and both are probably flexible in solution. We show further that the extracellular domains of Cstn3 and n1α interact directly and that both Cstn3 monomers and tetramers bind n1α with nanomolar affinity. The interaction is promoted by Ca2+ and requires minimally the LNS domain of Cstn3. Furthermore, Cstn3 uses a fundamentally different mechanism to bind n1α compared with other neurexin partners, such as the synaptic organizer neuroligin 2, because Cstn3 does not strictly require the sixth LNS domain of n1α. Our structural data suggest how Cstn3 as a synaptic organizer on the postsynaptic membrane, particularly in tetrameric form, may assemble radially symmetric trans-synaptic bridges with the presynaptic synaptic organizer n1α to recruit and spatially organize proteins into networks essential for synaptic function. PMID:25352602

  2. Interactive Querying over Large Network Data: Scalability, Visualization, and Interaction Design

    PubMed Central

    Pienta, Robert; Tamersoy, Acar; Tong, Hanghang; Endert, Alex; Chau, Duen Horng

    2015-01-01

    Given the explosive growth of modern graph data, new methods are needed that allow for the querying of complex graph structures without the need of a complicated querying languages; in short, interactive graph querying is desirable. We describe our work towards achieving our overall research goal of designing and developing an interactive querying system for large network data. We focus on three critical aspects: scalable data mining algorithms, graph visualization, and interaction design. We have already completed an approximate subgraph matching system called MAGE in our previous work that fulfills the algorithmic foundation allowing us to query a graph with hundreds of millions of edges. Our preliminary work on visual graph querying, Graphite, was the first step in the process to making an interactive graph querying system. We are in the process of designing the graph visualization and robust interaction needed to make truly interactive graph querying a reality. PMID:25859567

  3. Deconvolving molecular signatures of interactions between microbial colonies

    PubMed Central

    Harn, Y.-C.; Powers, M. J.; Shank, E. A.; Jojic, V.

    2015-01-01

    Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret. Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies. Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project. Contact: vjojic@cs.unc.edu Supplementary

  4. Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments

    ERIC Educational Resources Information Center

    Eagle, Michael; Hicks, Drew; Barnes, Tiffany

    2015-01-01

    Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…

  5. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions.

    PubMed

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P; Yao, Shuqiao

    2016-01-01

    Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network-mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  6. Spectroscopic investigations, molecular interactions, and molecular docking studies on the potential inhibitor "thiophene-2-carboxylicacid"

    NASA Astrophysics Data System (ADS)

    Karthick, T.; Balachandran, V.; Perumal, S.

    2015-04-01

    Thiophene derivatives have been focused in the past decades due to their remarkable biological and pharmacological activities. In connection with that the conformational stability, spectroscopic characterization, molecular (inter- and intra-) interactions, and molecular docking studies on thiophene-2-carboxylicacid have been performed in this work by experimental FT-IR and theoretical quantum chemical computations. Experimentally recorded FT-IR spectrum in the region 4000-400 cm-1 has been compared with the scaled theoretical spectrum and the spectral peaks have been assigned on the basis of potential energy distribution results obtained from MOLVIB program package. The conformational stability of monomer and dimer conformers has been examined. The presence of inter- and intramolecular interactions in the monomer and dimer conformers have been explained by natural bond orbital analysis. The UV-Vis spectra of the sample in different solvents have been simulated and solvent effects were predicted by polarisable continuum model with TD-DFT/B3LYP/6-31+G(d,p) method. To test the biological activity of the sample, molecular docking (ligand-protein) simulations have been performed using SWISSDOCK web server. The full fitness (FF) score and binding affinity values revealed that thiophene-2-carboxylicacid can act as potential inhibitor against inflammation.

  7. Neuronal oscillations and functional interactions between resting state networks.

    PubMed

    Lei, Xu; Wang, Yulin; Yuan, Hong; Mantini, Dante

    2014-07-01

    Functional magnetic imaging (fMRI) studies showed that resting state activity in the healthy brain is organized into multiple large-scale networks encompassing distant regions. A key finding of resting state fMRI studies is the anti-correlation typically observed between the dorsal attention network (DAN) and the default mode network (DMN), which - during task performance - are activated and deactivated, respectively. Previous studies have suggested that alcohol administration modulates the balance of activation/deactivation in brain networks, as well as it induces significant changes in oscillatory activity measured by electroencephalography (EEG). However, our knowledge of alcohol-induced changes in band-limited EEG power and their potential link with the functional interactions between DAN and DMN is still very limited. Here we address this issue, examining the neuronal effects of alcohol administration during resting state by using simultaneous EEG-fMRI. Our findings show increased EEG power in the theta frequency band (4-8 Hz) after administration of alcohol compared to placebo, which was prominent over the frontal cortex. More interestingly, increased frontal tonic EEG activity in this band was associated with greater anti-correlation between the DAN and the frontal component of the DMN. Furthermore, EEG theta power and DAN-DMN anti-correlation were relatively greater in subjects who reported a feeling of euphoria after alcohol administration, which may result from a diminished inhibition exerted by the prefrontal cortex. Overall, our findings suggest that slow brain rhythms are responsible for dynamic functional interactions between brain networks. They also confirm the applicability and potential usefulness of EEG-fMRI for central nervous system drug research. PMID:25050432

  8. Postiive tone resists based on network deploymerization of molecular resists

    NASA Astrophysics Data System (ADS)

    Lawson, Richard A.; Cheng, Jing; Cheshmehkani, Ameneh; Tolbert, Laren M.; Henderson, Clifford L.

    2013-03-01

    Conventional chemically amplified resists have several issues that can potentially limit their capability for sub-40 nm imaging. One of the major issues at this size scale is that the mechanical strength of positive tone CARs limits the amount of stress they can withstand during development, rinse, and drying, thus leading to problems with pattern collapse due to the high capillary forces generated during drying. This problem is exasperated by the fact that linear polymers show dramatically reduced modulus at sub-50 nm features sizes. To improve on this problem, we have made a positive tone resist based on network depolymerization of molecular resists. The resist thermally cross-links after being spin cast into thin film form through reactions between vinyl ether groups and carboxylic acid groups. By cross-linking the resist to form a dense three dimensional polymer network, the mechanical strength of the resist is greatly improved compared to linear polymers. The network is depolymerized using an acid catalyzed reaction to create development contrast that allows for patterning of the resist via development in either aqueous base or organic solvent. One drawback of the current resist design is that the free carboxylic acids on the resist molecule appear to react in solution at room temperature with both the vinyl ether groups on adjacent molecules and with any added base quencher. These reactions cause reduced effectiveness of the base quencher and produce a noticeable resist shelf life problem. Despite these limitations, the material was used to compare the effect of development in aqueous base versus organic solvent. The resist formulated in this work showed a DUV sensitivity of 7 mJ/cm2 and a contrast of 5.2 for development in either solvent or aqueous base. Under 100 keV e-beam imaging, the material showed 40 nm resolution for both development types. In standard 0.26 N TMAH, the dose-to-size was 84 μC/cm2 with 3σ LER of 14.2 nm. Using methyl isobutyl ketone

  9. Measuring Asymmetric Interactions in Resting State Brain Networks*

    PubMed Central

    Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

    2015-01-01

    Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  10. Measuring Asymmetric Interactions in Resting State Brain Networks.

    PubMed

    Joshi, Anand A; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M

    2015-01-01

    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  11. Interaction of microfractures: The mechanism of network building

    NASA Astrophysics Data System (ADS)

    Tentler, Tatiana; Amcoff, Örjan

    2010-04-01

    Microscopic study of fractures in polished sections of Precambrian metamorphic sulphide ore reveals that distinct crack morphologies such as dimensions, orientation, shape and distribution are controlled by mineral associations and temperature conditions during failure. Microfractures developed in a regime of low differential stress where hydraulic pressure triggered the episodic crack opening by pressurized fluids. Fracture arrays consist of segments of varied orientation and width resulting from initially isolated cracks. Linear geometry of fracture systems is complicated by interaction of their segments. Three distinguished types of interaction include in-line propagation of two cracks tip-to-tip (type 1), propagation of two cracks along curved paths towards each other (type 2) and propagation of single crack obliquely towards the other crack (type 3). Different interaction types result from variation in fracture spacing and relative position of their tips and distinguished by the angle of propagation. The ratio of overlap/spacing for interacting fractures is negative (- 0.25 to 0) for type 1 interaction and positive for type 2 (0 to 45), and type 3 (0 to 15) interactions. The mean value of the angle of fracture propagation in the onset of the interaction is obtained as 12° for type 1, 25° for type 2 and 38° for type 3 interactions. The mean value of the angle of fracture propagation accommodating linkage is 4° for type 1, 36° for type 2 and 12° for type 3 interactions. The initial crack distribution is crucial in determining whether their coalescence occurs resulting in mature fracture networks.

  12. Attractive interactions among intermediate filaments determine network mechanics in vitro.

    PubMed

    Pawelzyk, Paul; Mücke, Norbert; Herrmann, Harald; Willenbacher, Norbert

    2014-01-01

    Mechanical and structural properties of K8/K18 and vimentin intermediate filament (IF) networks have been investigated using bulk mechanical rheometry and optical microrheology including diffusing wave spectroscopy and multiple particle tracking. A high elastic modulus G0 at low protein concentration c, a weak concentration dependency of G0 (G0 ∼ c(0.5 ± 0.1)) and pronounced strain stiffening are found for these systems even without external crossbridgers. Strong attractive interactions among filaments are required to maintain these characteristic mechanical features, which have also been reported for various other IF networks. Filament assembly, the persistence length of the filaments and the network mesh size remain essentially unaffected when a nonionic surfactant is added, but strain stiffening is completely suppressed, G0 drops by orders of magnitude and exhibits a scaling G0 ∼ c(1.9 ± 0.2) in agreement with microrheological measurements and as expected for entangled networks of semi-flexible polymers. Tailless K8Δ/K18ΔT and various other tailless filament networks do not exhibit strain stiffening, but still show high G0 values. Therefore, two binding sites are proposed to exist in IF networks. A weaker one mediated by hydrophobic amino acid clusters in the central rod prevents stretched filaments between adjacent cross-links from thermal equilibration and thus provides the high G0 values. Another strong one facilitating strain stiffening is located in the tail domain with its high fraction of hydrophobic amino acid sequences. Strain stiffening is less pronounced for vimentin than for K8/K18 due to electrostatic repulsion forces partly compensating the strong attraction at filament contact points. PMID:24690778

  13. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions

    PubMed Central

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of “commonly accepted beliefs” seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node’s age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  14. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.

    PubMed

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  15. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

    PubMed

    Basha, Omer; Flom, Dvir; Barshir, Ruth; Smoly, Ilan; Tirman, Shoval; Yeger-Lotem, Esti

    2015-07-01

    The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet. PMID:25990735

  16. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

    PubMed Central

    Basha, Omer; Flom, Dvir; Barshir, Ruth; Smoly, Ilan; Tirman, Shoval; Yeger-Lotem, Esti

    2015-01-01

    The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet. PMID:25990735

  17. A computational molecular design framework for crosslinked polymer networks.

    PubMed

    Eslick, J C; Ye, Q; Park, J; Topp, E M; Spencer, P; Camarda, K V

    2009-05-21

    Crosslinked polymers are important in a very wide range of applications including dental restorative materials. However, currently used polymeric materials experience limited durability in the clinical oral environment. Researchers in the dental polymer field have generally used a time-consuming experimental trial-and-error approach to the design of new materials. The application of computational molecular design (CMD) to crosslinked polymer networks has the potential to facilitate development of improved polymethacrylate dental materials. CMD uses quantitative structure property relations (QSPRs) and optimization techniques to design molecules possessing desired properties. This paper describes a mathematical framework which provides tools necessary for the application of CMD to crosslinked polymer systems. The novel parts of the system include the data structures used, which allow for simple calculation of structural descriptors, and the formulation of the optimization problem. A heuristic optimization method, Tabu Search, is used to determine candidate monomers. Use of a heuristic optimization algorithm makes the system more independent of the types of QSPRs used, and more efficient when applied to combinatorial problems. A software package has been created which provides polymer researchers access to the design framework. A complete example of the methodology is provided for polymethacrylate dental materials. PMID:23904665

  18. Viral immune modulators perturb the human molecular network by common and unique strategies.

    PubMed

    Pichlmair, Andreas; Kandasamy, Kumaran; Alvisi, Gualtiero; Mulhern, Orla; Sacco, Roberto; Habjan, Matthias; Binder, Marco; Stefanovic, Adrijana; Eberle, Carol-Ann; Goncalves, Adriana; Bürckstümmer, Tilmann; Müller, André C; Fauster, Astrid; Holze, Cathleen; Lindsten, Kristina; Goodbourn, Stephen; Kochs, Georg; Weber, Friedemann; Bartenschlager, Ralf; Bowie, Andrew G; Bennett, Keiryn L; Colinge, Jacques; Superti-Furga, Giulio

    2012-07-26

    Viruses must enter host cells to replicate, assemble and propagate. Because of the restricted size of their genomes, viruses have had to evolve efficient ways of exploiting host cell processes to promote their own life cycles and also to escape host immune defence mechanisms. Many viral open reading frames (viORFs) with immune-modulating functions essential for productive viral growth have been identified across a range of viral classes. However, there has been no comprehensive study to identify the host factors with which these viORFs interact for a global perspective of viral perturbation strategies. Here we show that different viral perturbation patterns of the host molecular defence network can be deduced from a mass-spectrometry-based host-factor survey in a defined human cellular system by using 70 innate immune-modulating viORFs from 30 viral species. The 579 host proteins targeted by the viORFs mapped to an unexpectedly large number of signalling pathways and cellular processes, suggesting yet unknown mechanisms of antiviral immunity. We further experimentally verified the targets heterogeneous nuclear ribonucleoprotein U, phosphatidylinositol-3-OH kinase, the WNK (with-no-lysine) kinase family and USP19 (ubiquitin-specific peptidase 19) as vulnerable nodes in the host cellular defence system. Evaluation of the impact of viral immune modulators on the host molecular network revealed perturbation strategies used by individual viruses and by viral classes. Our data are also valuable for the design of broad and specific antiviral therapies. PMID:22810585

  19. miRTargetLink—miRNAs, Genes and Interaction Networks

    PubMed Central

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-01-01

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink. PMID:27089332

  20. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  1. Multifunctional proteins revealed by overlapping clustering in protein interaction network

    PubMed Central

    Chapple, Charles E.; Guénoche, Alain; Brun, Christine

    2012-01-01

    Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters. Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks. Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197 Contact: brun@tagc.univ-mrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22080466

  2. Network of Interactions Between Ciliates and Phytoplankton During Spring.

    PubMed

    Posch, Thomas; Eugster, Bettina; Pomati, Francesco; Pernthaler, Jakob; Pitsch, Gianna; Eckert, Ester M

    2015-01-01

    The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile) as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic/omnivorous species, and highlighted the role of Halteria/Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA) proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species. PMID:26635757

  3. Network of Interactions Between Ciliates and Phytoplankton During Spring

    PubMed Central

    Posch, Thomas; Eugster, Bettina; Pomati, Francesco; Pernthaler, Jakob; Pitsch, Gianna; Eckert, Ester M.

    2015-01-01

    The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile) as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic/omnivorous species, and highlighted the role of Halteria/Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA) proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species. PMID:26635757

  4. Real-time hierarchically distributed processing network interaction simulation

    NASA Technical Reports Server (NTRS)

    Zimmerman, W. F.; Wu, C.

    1987-01-01

    The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

  5. CaML: Camera Markup Language for Network Interaction

    NASA Astrophysics Data System (ADS)

    Sayles, Maxwell; Wu, Xiaojing; Boyd, Jeffrey E.

    2003-01-01

    As processor speeds increase and the cost of digital video technology falls, the use of video is expanding in a plethora of applications including video surveillance, human computer interaction, tele-instruction, and enhanced sports broadcasts. However, a major problem that now faces developers of video systems is the requirement to build the low-level video processing from the ground up for each application. This paper describes a camera system that acts not merely as a provider of pixels, but as a video information server. A video application interacts with the camera server using the Camera Markup Language (CaML, pronounced camel) proposed here. CaML is an XML-based (Extensible Markup Language) data format for exchanging video information with a server. It provides a layer of abstraction between the application and the pixels to simplify the development process and is well-suited to exchanging data over a network. Using a camera as a server on a network makes it a simple matter for a single application to use multiple cameras. Local- and wide-area networks (LANs and WANs) replace the need for conventional methods for routing video signals.

  6. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks

    PubMed Central

    Chambers, Brendan; MacLean, Jason N.

    2016-01-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  7. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

    PubMed

    Chambers, Brendan; MacLean, Jason N

    2016-08-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  8. From Topology to Phenotype in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Pržulj, Nataša

    We have recently witnessed an explosion in biological network data along with the development of computational approaches for their analyses. This new interdisciplinary research area is an integral part of systems biology, promising to provide new insights into organizational principles of life, as well as into evolution and disease. However, there is a danger that the area might become hindered by several emerging issues. In particular, there is typically a weak link between biological and computational scientists, resulting in the use of simple computational techniques of limited potential to explain these complex biological data. Hence, there is a danger that the community might view the topological features of network data as mere statistics, ignoring the value of the information contained in these data. This might result in the imposition of scientific doctrines, such as scale-free-centric (on the modelling side) and genome-centric (on the biological side) opinions onto this nascent research area. In this chapter, we take a network science perspective and present a brief, high-level overview of the area, commenting on possible challenges ahead. We focus on protein-protein interaction networks (PINs) in which nodes correspond to proteins in a cell and edges to physical bindings between the proteins.

  9. Molecular interaction and synergistic activation of a promoter by Six, Eya, and Dach proteins mediated through CREB binding protein.

    PubMed

    Ikeda, Keiko; Watanabe, Yoko; Ohto, Hiromi; Kawakami, Kiyoshi

    2002-10-01

    Drosophila sine oculis, eyes absent, and dachshund are essential for compound eye formation and form a gene network with direct protein interaction and genetic regulation. The vertebrate homologues of these genes, Six, Eya, and Dach, also form a similar genetic network during muscle formation. To elucidate the molecular mechanism underlying the network among Six, Eya, and Dach, we examined the molecular interactions among the encoded proteins. Eya interacted directly with Six but never with Dach. Dach transactivated a multimerized GAL4 reporter gene by coproduction of GAL4-Eya fusion proteins. Transactivation by Eya and Dach was repressed by overexpression of VP16 or E1A but not by E1A mutation, which is defective for CREB binding protein (CBP) binding. Recruitment of CBP to the immobilized chromatin DNA template was dependent on FLAG-Dach and GAL4-Eya3. These results indicate that CBP is a mediator of the interaction between Eya and Dach. Contrary to our expectations, Dach binds to chromatin DNA by itself, not being tethered by GAL4-Eya3. Dach also binds to naked DNA with lower affinity. The conserved DD1 domain is responsible for binding to DNA. Transactivation was also observed by coproduction of GAL4-Six, Eya, and Dach, indicating that Eya and Dach synergy is relevant when Eya is tethered to DNA through Six protein. Our results demonstrated that synergy is mediated through direct interaction of Six-Eya and through the interaction of Eya-Dach with CBP and explain the molecular basis for the genetic interactions among Six, Eya, and Dach. This work provides fundamental information on the role and the mechanism of action of this gene cassette in tissue differentiation and organogenesis. PMID:12215533

  10. Gap distance and interactions in a molecular tunnel junction.

    PubMed

    Chang, Shuai; He, Jin; Zhang, Peiming; Gyarfas, Brett; Lindsay, Stuart

    2011-09-14

    The distance between electrodes in a tunnel junction cannot be determined from the external movement applied to the electrodes because of interfacial forces that distort the electrode geometry at the nanoscale. These distortions become particularly complex when molecules are present in the junction, as demonstrated here by measurements of the AC response of a molecular junction over a range of conductivities from microsiemens to picosiemens. Specific chemical interactions within the junction lead to distinct features in break-junction data, and these have been used to determine the electrode separation in a junction functionalized with 4(5)-(2-mercaptoethyl)-1H-imidazole-2-carboxamide, a reagent developed for reading DNA sequences. PMID:21838292

  11. Interactive display of molecular models using a microcomputer system

    NASA Technical Reports Server (NTRS)

    Egan, J. T.; Macelroy, R. D.

    1980-01-01

    A simple, microcomputer-based, interactive graphics display system has been developed for the presentation of perspective views of wire frame molecular models. The display system is based on a TERAK 8510a graphics computer system with a display unit consisting of microprocessor, television display and keyboard subsystems. The operating system includes a screen editor, file manager, PASCAL and BASIC compilers and command options for linking and executing programs. The graphics program, written in USCD PASCAL, involves the centering of the coordinate system, the transformation of centered model coordinates into homogeneous coordinates, the construction of a viewing transformation matrix to operate on the coordinates, clipping invisible points, perspective transformation and scaling to screen coordinates; commands available include ZOOM, ROTATE, RESET, and CHANGEVIEW. Data file structure was chosen to minimize the amount of disk storage space. Despite the inherent slowness of the system, its low cost and flexibility suggests general applicability.

  12. Molecular and chemical dialogues in bacteria-protozoa interactions

    PubMed Central

    Song, Chunxu; Mazzola, Mark; Cheng, Xu; Oetjen, Janina; Alexandrov, Theodore; Dorrestein, Pieter; Watrous, Jeramie; van der Voort, Menno; Raaijmakers, Jos M.

    2015-01-01

    Protozoan predation of bacteria can significantly affect soil microbial community composition and ecosystem functioning. Bacteria possess diverse defense strategies to resist or evade protozoan predation. For soil-dwelling Pseudomonas species, several secondary metabolites were proposed to provide protection against different protozoan genera. By combining whole-genome transcriptome analyses with (live) imaging mass spectrometry (IMS), we observed multiple changes in the molecular and chemical dialogues between Pseudomonas fluorescens and the protist Naegleria americana. Lipopeptide (LP) biosynthesis was induced in Pseudomonas upon protozoan grazing and LP accumulation transitioned from homogeneous distributions across bacterial colonies to site-specific accumulation at the bacteria-protist interface. Also putrescine biosynthesis was upregulated in P. fluorescens upon predation. We demonstrated that putrescine induces protozoan trophozoite encystment and adversely affects cyst viability. This multifaceted study provides new insights in common and strain-specific responses in bacteria-protozoa interactions, including responses that contribute to bacterial survival in highly competitive soil and rhizosphere environments. PMID:26246193

  13. Microparadigms: chains of collective reasoning in publications about molecular interactions.

    PubMed

    Rzhetsky, Andrey; Iossifov, Ivan; Loh, Ji Meng; White, Kevin P

    2006-03-28

    We analyzed a very large set of molecular interactions that had been derived automatically from biological texts. We found that published statements, regardless of their verity, tend to interfere with interpretation of the subsequent experiments and, therefore, can act as scientific "microparadigms," similar to dominant scientific theories [Kuhn, T. S. (1996) The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago)]. Using statistical tools, we measured the strength of the influence of a single published statement on subsequent interpretations. We call these measured values the momentums of the published statements and treat separately the majority and minority of conflicting statements about the same molecular event. Our results indicate that, when building biological models based on published experimental data, we may have to treat the data as highly dependent-ordered sequences of statements (i.e., chains of collective reasoning) rather than unordered and independent experimental observations. Furthermore, our computations indicate that our data set can be interpreted in two very different ways (two "alternative universes"): one is an "optimists' universe" with a very low incidence of false results (<5%), and another is a "pessimists' universe" with an extraordinarily high rate of false results (>90%). Our computations deem highly unlikely any milder intermediate explanation between these two extremes. PMID:16543380

  14. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

    PubMed Central

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

    2016-01-01

    Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  15. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  16. Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

    PubMed Central

    2010-01-01

    Background Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming) experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible. Methods Here we report the development of a simple open source program module (OpenPPI_predictor) that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism. Results Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans. Availability The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/. PMID:20684769

  17. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    PubMed Central

    Chu, Liang-Hui; Chen, Bor-Sen

    2008-01-01

    Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs. PMID:18590547

  18. The Bilingual Language Interaction Network for Comprehension of Speech*

    PubMed Central

    Marian, Viorica

    2013-01-01

    During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

  19. Network-theoretic approach to model vortex interactions

    NASA Astrophysics Data System (ADS)

    Nair, Aditya; Taira, Kunihiko

    2014-11-01

    We present a network-theoretic approach to describe a system of point vortices in two-dimensional flow. By considering the point vortices as nodes, a complete graph is constructed with edges connecting each vortex to every other vortex. The interactions between the vortices are captured by the graph edge weights. We employ sparsification techniques on these graph representations based on spectral theory to construct sparsified models of the overall vortical interactions. The edge weights are redistributed through spectral sparsification of the graph such that the sum of the interactions associated with each vortex is maintained constant. In addition, sparse configurations maintain similar spectral properties as the original setup. Through the reduction in the number of interactions, key vortex interactions can be highlighted. Identification of vortex structures based on graph sparsification is demonstrated with an example of clusters of point vortices. We also evaluate the computational performance of sparsification for large collection of point vortices. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).

  20. Brain network interactions in auditory, visual and linguistic processing.

    PubMed

    Horwitz, Barry; Braun, Allen R

    2004-05-01

    In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are ideal for enabling one to assess interregional functional interactions. Two ways to use these types of data to assess network interactions are presented. First, using PET, we demonstrate that anterior and posterior perisylvian language areas have stronger functional connectivity during spontaneous narrative production than during other less linguistically demanding production tasks. Second, we show how one can use large-scale neural network modeling to relate neural activity to the hemodynamically-based data generated by fMRI and PET. We review two versions of a model of object processing - one for visual and one for auditory objects. The regions comprising the models include primary and secondary sensory cortex, association cortex in the temporal lobe, and prefrontal cortex. Each model incorporates specific assumptions about how neurons in each of these areas function, and how neurons in the different areas are interconnected with each other. Each model is able to perform a delayed match-to-sample task for simple objects (simple shapes for the visual model; tonal contours for the auditory model). We find that the simulated electrical activities in each region are similar to those observed in nonhuman primates performing analogous tasks, and the absolute values of the simulated integrated synaptic activity in each brain region match human fMRI/PET data. Thus, this type of modeling provides a way to understand the neural bases for the sensorimotor and cognitive tasks of interest. PMID:15068921

  1. Visualization of protein interaction networks: problems and solutions

    PubMed Central

    2013-01-01

    Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to

  2. Interactions of arabinoxylan and (1,3)(1,4)-β-glucan with cellulose networks.

    PubMed

    Mikkelsen, Deirdre; Flanagan, Bernadine M; Wilson, Sarah M; Bacic, Antony; Gidley, Michael J

    2015-04-13

    To identify interactions of relevance to the structure and properties of the primary cell walls of cereals and grasses, we used arabinoxylan and (1,3)(1,4)-β-glucan, major polymers in cereal/grass primary cell walls, to construct composites with cellulose produced by Gluconacetobacter xylinus. Both polymers associated prolifically with cellulose without becoming rigid or altering the nature or extent of cellulose crystallinity. Mechanical properties were modestly affected compared with xyloglucan or pectin (characteristic components of nongrass primary cell walls) composites with cellulose. In situ depletion of arabinoxylan arabinose side chains within preformed cellulose composites resulted in phase separation, with only limited enhancement of xylan-cellulose interactions. These results suggest that arabinoxylan and (1 → 3)(1 → 4)-β-d-glucan are not functional homologues for either xyloglucan or pectin in the way they interact with cellulose networks. Association of cell-wall polymers with cellulose driven by entropic amelioration of high energy cellulose/water interfaces should be considered as a third type of interaction within cellulose-based cell walls, in addition to molecular binding (enthalpic driving force) exhibited by, for example, xyloglucans or mannans, and interpenetrating networks based on, for example, pectins. PMID:25756836

  3. Temporal Networks of Face-to-Face Human Interactions

    NASA Astrophysics Data System (ADS)

    Barrat, Alain; Cattuto, Ciro

    The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.

  4. Phylogenetic distances are encoded in networks of interacting pathways

    PubMed Central

    Mazurie, Aurélien; Bonchev, Danail; Schwikowski, Benno; Buck, Gregory A.

    2008-01-01

    Motivation: Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange. Results: Our analysis shows that a small set of descriptors of the structure and complexity of the NIPs combined into regression models reproduce very accurately reference phylogenetic distances derived from 16S rRNA sequences (10-fold cross-validation correlation coefficient higher than 0.9). Our method also showed better scores than previous work on metabolism-based phylogenetic reconstructions, as assessed by branch distances score, topological similarity and second cousins score. Thus, our metabolome representation as network of overlapping metabolic pathways captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny. It is important to note that precise knowledge of all of the reactions in these pathways is not required for these reconstructions. These observations underscore the potential for the use of abstract, modular representations of metabolic reactions as tools in studying the evolution of species. Contact: aurelien.mazurie@pasteur.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18820265

  5. Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.

    PubMed

    Scholes, Natalie S; Weinzierl, Robert O J

    2016-05-01

    Transcriptional activation domains (ADs) are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD) simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bound to the GAL11 coactivator surface. We show that the AD-coactivator interactions are highly dynamic while obeying distinct rules. The data provide insights into the constant and variable aspects of orientation of ADs relative to the coactivator, changes in secondary structure and energetic contributions stabilizing the various conformers at different time points. We also demonstrate that a prediction of α-helical propensity correlates directly with the experimentally measured transactivation potential of a large set of mutagenized ADs. The link between α-helical propensity and the stimulatory activity of ADs has fundamental practical and theoretical implications concerning the recruitment of ADs to coactivators. PMID:27175900

  6. Developing a Molecular Roadmap of Drug-Food Interactions

    PubMed Central

    Jensen, Kasper; Ni, Yueqiong; Panagiotou, Gianni; Kouskoumvekaki, Irene

    2015-01-01

    Recent research has demonstrated that consumption of food -especially fruits and vegetables- can alter the effects of drugs by interfering either with their pharmacokinetic or pharmacodynamic processes. Despite the recognition of such drug-food associations as an important element for successful therapeutic interventions, a systematic approach for identifying, predicting and preventing potential interactions between food and marketed or novel drugs is not yet available. The overall objective of this work was to sketch a comprehensive picture of the interference of ∼ 4,000 dietary components present in ∼1800 plant-based foods with the pharmacokinetics and pharmacodynamics processes of medicine, with the purpose of elucidating the molecular mechanisms involved. By employing a systems chemical biology approach that integrates data from the scientific literature and online databases, we gained a global view of the associations between diet and dietary molecules with drug targets, metabolic enzymes, drug transporters and carriers currently deposited in DrugBank. Moreover, we identified disease areas and drug targets that are most prone to the negative effects of drug-food interactions, showcasing a platform for making recommendations in relation to foods that should be avoided under certain medications. Lastly, by investigating the correlation of gene expression signatures of foods and drugs we were able to generate a completely novel drug-diet interactome map. PMID:25668218

  7. Molecular interactions between (-)-epigallocatechin gallate analogs and pancreatic lipase.

    PubMed

    Wang, Shihui; Sun, Zeya; Dong, Shengzhao; Liu, Yang; Liu, Yun

    2014-01-01

    The molecular interactions between pancreatic lipase (PL) and four tea polyphenols (EGCG analogs), like (-)-epigallocatechin gallate (EGCG), (-)-gallocatechin gallate (GCG), (-)-epicatechin gallate (ECG), and (-)-epigallocatechin (EC), were studied from PL activity, conformation, kinetics and thermodynamics. It was observed that EGCG analogs inhibited PL activity, and their inhibitory rates decreased by the order of EGCG>GCG>ECG>EC. PL activity at first decreased rapidly and then slowly with the increase of EGCG analogs concentrations. α-Helix content of PL secondary structure decreased dependent on EGCG analogs concentration by the order of EGCG>GCG>ECG>EC. EGCG, ECG, and EC could quench PL fluorescence both dynamically and statically, while GCG only quenched statically. EGCG analogs would induce PL self-assembly into complexes and the hydrodynamic radii of the complexes possessed a close relationship with the inhibitory rates. Kinetics analysis showed that EGCG analogs non-competitively inhibited PL activity and did not bind to PL catalytic site. DSC measurement revealed that EGCG analogs decreased the transition midpoint temperature of PL enzyme, suggesting that these compounds reduced PL enzyme thermostability. In vitro renaturation through urea solution indicated that interactions between PL and EGCG analogs were weak and non-covalent. PMID:25365042

  8. Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions

    PubMed Central

    Scholes, Natalie S.; Weinzierl, Robert O. J.

    2016-01-01

    Transcriptional activation domains (ADs) are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD) simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bound to the GAL11 coactivator surface. We show that the AD-coactivator interactions are highly dynamic while obeying distinct rules. The data provide insights into the constant and variable aspects of orientation of ADs relative to the coactivator, changes in secondary structure and energetic contributions stabilizing the various conformers at different time points. We also demonstrate that a prediction of α-helical propensity correlates directly with the experimentally measured transactivation potential of a large set of mutagenized ADs. The link between α-helical propensity and the stimulatory activity of ADs has fundamental practical and theoretical implications concerning the recruitment of ADs to coactivators. PMID:27175900

  9. Multitargeting by curcumin as revealed by molecular interaction studies

    PubMed Central

    Gupta, Subash C.; Prasad, Sahdeo; Kim, Ji Hye; Patchva, Sridevi; Webb, Lauren J.; Priyadarsini, Indira K.

    2012-01-01

    Curcumin (diferuloylmethane), the active ingredient in turmeric (Curcuma longa), is a highly pleiotropic molecule with anti-inflammatory, anti-oxidant, chemopreventive, chemosensitization, and radiosensitization activities. The pleiotropic activities attributed to curcumin come from its complex molecular structure and chemistry, as well as its ability to influence multiple signaling molecules. Curcumin has been shown to bind by multiple forces directly to numerous signaling molecules, such as inflammatory molecules, cell survival proteins, protein kinases, protein reductases, histone acetyltransferase, histone deacetylase, glyoxalase I, xanthine oxidase, proteasome, HIV1 integrase, HIV1 protease, sarco (endo) plasmic reticulum Ca2+ ATPase, DNA methyltransferases 1, FtsZ protofilaments, carrier proteins, and metal ions. Curcumin can also bind directly to DNA and RNA. Owing to its β-diketone moiety, curcumin undergoes keto–enol tautomerism that has been reported as a favorable state for direct binding. The functional groups on curcumin found suitable for interaction with other macromolecules include the α, β-unsaturated β-diketone moiety, carbonyl and enolic groups of the β-diketone moiety, methoxy and phenolic hydroxyl groups, and the phenyl rings. Various biophysical tools have been used to monitor direct interaction of curcumin with other proteins, including absorption, fluorescence, Fourier transform infrared (FTIR) and circular dichroism (CD) spectroscopy, surface plasmon resonance, competitive ligand binding, Forster type fluorescence resonance energy transfer (FRET), radiolabeling, site-directed mutagenesis, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), immunoprecipitation, phage display biopanning, electron microscopy, 1-anilino-8-naphthalene-sulfonate (ANS) displacement, and co-localization. Molecular docking, the most commonly employed computational tool for calculating binding affinities and predicting

  10. Influence of molecular structure on the properties of out-of-equilibrium oscillating enzymatic reaction networks.

    PubMed

    Wong, Albert S Y; Postma, Sjoerd G J; Vialshin, Ilia N; Semenov, Sergey N; Huck, Wilhelm T S

    2015-09-30

    Our knowledge of the properties and dynamics of complex molecular reaction networks, for example those found in living systems, considerably lags behind the understanding of elementary chemical reactions. In part, this is because chemical reactions networks are nonlinear systems that operate under conditions far from equilibrium. Of particular interest is the role of individual reaction rates on the stability of the network output. In this research we use a rational approach combined with computational methods, to produce complex behavior (in our case oscillations) and show that small changes in molecular structure are sufficient to impart large changes in network behavior. PMID:26352485

  11. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    NASA Astrophysics Data System (ADS)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  12. Genotoxicants Target Distinct Molecular Networks in Neonatal Neurons

    PubMed Central

    Kisby, Glen E.; Olivas, Antoinette; Standley, Melissa; Lu, Xinfang; Pattee, Patrick; O’Malley, Jean; Li, Xiaorong; Muniz, Juan; Nagalla, Srinavasa R.

    2006-01-01

    Background Exposure of the brain to environmental agents during critical periods of neuronal development is considered a key factor underlying many neurologic disorders. Objectives In this study we examined the influence of genotoxicants on cerebellar function during early development by measuring global gene expression changes. Methods We measured global gene expression in immature cerebellar neurons (i.e., granule cells) after treatment with two distinct alkylating agents, methylazoxymethanol (MAM) and nitrogen mustard (HN2). Granule cell cultures were treated for 24 hr with MAM (10–1,000 μM) or HN2 (0.1–20 μM) and examined for cell viability, DNA damage, and markers of apoptosis. Results Neuronal viability was significantly reduced (p < 0.01) at concentrations > 500 μM for MAM and > 1.0 μM for HN2; this correlated with an increase in both DNA damage and markers of apoptosis. Neuronal cultures treated with sublethal concentrations of MAM (100 μM) or HN2 (1.0 μM) were then examined for gene expression using large-scale mouse cDNA microarrays (27,648). Gene expression results revealed that a) global gene expression was predominantly up-regulated by both genotoxicants; b) the number of down-regulated genes was approximately 3-fold greater for HN2 than for MAM; and c) distinct classes of molecules were influenced by MAM (i.e, neuronal differentiation, the stress and immune response, and signal transduction) and HN2 (i.e, protein synthesis and apoptosis). Conclusions These studies demonstrate that individual genotoxicants induce distinct gene expression signatures. Further study of these molecular networks may explain the variable response of the developing brain to different types of environmental genotoxicants. PMID:17107856

  13. Fractal and complex network analyses of protein molecular dynamics

    NASA Astrophysics Data System (ADS)

    Zhou, Yuan-Wu; Liu, Jin-Long; Yu, Zu-Guo; Zhao, Zhi-Qin; Anh, Vo

    2014-12-01

    Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h(q) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h(2) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H≈0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h(2) of MF-DFA on the time series, exponent λ of the exponential degree distribution and fractal dimension dB of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between (from MF-DFA on time series) and of the converted HVGs for different energy, pressure and volume.

  14. Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics.

    PubMed

    Karthikeyan, Bagavathy Shanmugam; Akbarsha, Mohammad Abdulkader; Parthasarathy, Subbiah

    2016-06-21

    Cytochrome P450 (CYP) enzymes that degrade xenobiotics play a critical role in the metabolism and biotransformation of drugs and xenobiotics in humans as well as experimental animal models such as mouse and rat. These proteins function as a network collectively as well as independently. Though there are several reports on the organization, regulation and functionality of various CYP enzymes at the molecular level, the understanding of organization and functionality of these proteins at the holistic level remain unclear. The objective of this study is to understand the organization and functionality of xenobiotic degrading CYP enzymes of human, mouse and rat using network theory approaches and to study species differences that exist among them at the holistic level. For our analysis, a protein-protein interaction (PPI) network for CYP enzymes of human, mouse and rat was constructed using the STRING database. Topology, centrality, modularity and robustness analyses were performed for our predicted CYP PPI networks that were then validated by comparison with randomly generated network models. Network centrality analyses of CYP PPI networks reveal the central/hub proteins in the network. Modular analysis of the CYP PPI networks of human, mouse and rat resulted in functional clusters. These clusters were subjected to ontology and pathway enrichment analysis. The analyses show that the cluster of the human CYP PPI network is enriched with pathways principally related to xenobiotic/drug metabolism. Endo-xenobiotic crosstalk dominated in mouse and rat CYP PPI networks, and they were highly enriched with endogenous metabolic and signaling pathways. Thus, cross-species comparisons and analyses of human, mouse and rat CYP PPI networks gave insights about species differences that existed at the holistic level. More investigations from both reductionist and holistic perspectives can help understand CYP metabolism and species extrapolation in a much better way. PMID:27194593

  15. Collective behavior of interacting locally synchronized oscillations in neuronal networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2012-10-01

    Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh-Rose type with electrical and/or chemical couplings. We construct large-scale models using networks of such units which capture the essential features of the dynamics of cells and their connectivity patterns. The profile of the spike synchronization is then investigated considering different model parameters such as strength and ratio of excitatory/inhibitory connections. We also show that transmission time-delay might enhance the spike synchrony. The influence of spike-timing-dependence-plasticity is also studies on the spike synchronization.

  16. Phospho-tyrosine dependent protein–protein interaction network

    PubMed Central

    Grossmann, Arndt; Benlasfer, Nouhad; Birth, Petra; Hegele, Anna; Wachsmuth, Franziska; Apelt, Luise; Stelzl, Ulrich

    2015-01-01

    Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes. PMID:25814554

  17. Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

    PubMed Central

    Emmert-Streib, Frank

    2012-01-01

    It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level. PMID:22530042

  18. Community structure of non-coding RNA interaction network.

    PubMed

    Nacher, Jose C

    2013-01-01

    Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between non-coding RNAs (ncRNAs) and proteins related bio-macromolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell. PMID:23545211

  19. Node similarity within subgraphs of protein interaction networks

    NASA Astrophysics Data System (ADS)

    Penner, Orion; Sood, Vishal; Musso, Gabriel; Baskerville, Kim; Grassberger, Peter; Paczuski, Maya

    2008-06-01

    We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. The twinness of a pair of nodes is the number of connected, labeled subgraphs of size n in which the two nodes possess identical neighbours. The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). These include an Escherichia coli PIN and three Saccharomyces cerevisiae PINs - each obtained using state-of-the-art high-throughput methods. In almost all cases, the average twinness of node pairs is vastly higher than that expected from a null model obtained by switching links. For all n, we observe a difference in the ratio of type A twins (which are unlinked pairs) to type B twins (which are linked pairs) distinguishing the prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is expected due to gene duplication, and whole genome duplication paralogues in S. cerevisiae have been reported to co-cluster into the same complexes. Indeed, we find that these paralogous proteins are over-represented as twins compared to pairs chosen at random. These results indicate that twinness can detect ancestral relationships from currently available PIN data.

  20. Functional and protein-protein interaction network analysis of colorectal cancer induced by ulcerative colitis

    PubMed Central

    DAI, YONG; JIANG, JIN-BO; WANG, YAN-LEI; JIN, ZU-TAO; HU, SAN-YUAN

    2015-01-01

    Colorectal cancer (CRC) is a well-recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome-wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was

  1. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    ERIC Educational Resources Information Center

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  2. Interpretation of Association Behavior and Molecular Interactions in Binary Mixtures from Thermoacoustics and Molecular Compression Data

    NASA Astrophysics Data System (ADS)

    Shukla, Rajeev K.; Kumar, Atul; Srivastava, Urvashi; Srivastava, Kirti; Pandey, Vivek K.

    2016-09-01

    Density and acoustic velocity were measured for binary liquid mixtures of formamide, N-methylacetamide (NMA), dimethylformamide (DMF), and dimethylacetamide (DMA) with acetonitrile at atmospheric pressure and 293.15 K, 298.15 K, 303.15 K, 308.15 K, or 313.15 K over the concentration range 0.12 to 0.97. Models assuming association and nonassociation of the components of the mixtures were used to predict the behavior of the studied liquids, which would typically show weak interactions. The measured properties were fitted to the Redlich-Kister polynomial to estimate the binary coefficients and standard errors. The data were used to study the molecular interactions in the binary mixtures. Furthermore, the McAllister multibody interaction model was used to correlate the properties of the binary liquid mixtures. Testing of the nonassociation and association models for the different systems showed that, compared with the nonassociation model theoretical results, the association model theoretical results were more consistent with the experimental results.

  3. Ranking of Molecular Biomarker Interaction with Targeted DNA Nucleobases via Full Atomistic Molecular Dynamics.

    PubMed

    Zhang, Wenjun; Wang, Ming L; Cranford, Steven W

    2016-01-01

    DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring. PMID:26750747

  4. Ranking of Molecular Biomarker Interaction with Targeted DNA Nucleobases via Full Atomistic Molecular Dynamics

    PubMed Central

    Zhang, Wenjun; Wang, Ming L.; Cranford, Steven W.

    2016-01-01

    DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring. PMID:26750747

  5. Ranking of Molecular Biomarker Interaction with Targeted DNA Nucleobases via Full Atomistic Molecular Dynamics

    NASA Astrophysics Data System (ADS)

    Zhang, Wenjun; Wang, Ming L.; Cranford, Steven W.

    2016-01-01

    DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring.

  6. Recent Coselection in Human Populations Revealed by Protein–Protein Interaction Network

    PubMed Central

    Qian, Wei; Zhou, Hang; Tang, Kun

    2015-01-01

    Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein–protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. PMID:25532814

  7. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation.

    PubMed

    Zhang, Ke K; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-03-15

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5(+/) (-), Osr1(+/-), Osr1(-/-) and Tbx5(+/-)/Osr1(+/-) mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene-gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny. PMID:26744331

  8. Topology of Protein Interaction Network Shapes Protein Abundances and Strengths of Their Functional and Nonspecific Interactions

    SciTech Connect

    Maslov, S.; Heo, M.; Shakhnovich, E.

    2011-03-08

    How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a 'frustration' effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.

  9. The Evolution and Origin of Animal Toll-Like Receptor Signaling Pathway Revealed by Network-Level Molecular Evolutionary Analyses

    PubMed Central

    Qin, Sheng; Chen, Liming; Ma, Fei

    2012-01-01

    Genes carry out their biological functions through pathways in complex networks consisting of many interacting molecules. Studies on the effect of network architecture on the evolution of individual proteins will provide valuable information for understanding the origin and evolution as well as functional conservation of signaling pathways. However, the relationship between the network architecture and the individual protein sequence evolution is yet little known. In current study, we carried out network-level molecular evolution analysis on TLR (Toll-like receptor ) signaling pathway, which plays an important role in innate immunity in insects and mammals, and we found that: 1) The selection constraint of genes was negatively correlated with its position along TLR signaling pathway; 2) all genes in TLR signaling pathway were highly conserved and underwent strong purifying selection; 3) the distribution of selective pressure along the pathway was driven by differential nonsynonymous substitution levels; 4) The TLR signaling pathway might present in a common ancestor of sponges and eumetazoa, and evolve via the TLR, IKK, IκB and NF-κB genes underwent duplication events as well as adaptor molecular enlargement, and gene structure and conservation motif of NF-κB genes shifted in their evolutionary history. Our results will improve our understanding on the evolutionary history of animal TLR signaling pathway as well as the relationship between the network architecture and the sequences evolution of individual protein. PMID:23236523

  10. Interaction of proteases with legume seed inhibitors. Molecular features.

    PubMed

    de Seidl, D S

    1996-12-01

    After having found that raw black beans (Phaseolus vulgaris) were toxic, while the cooked ones constitute the basic diet of the underdeveloped peoples of the world, in the sixties, our research directed by Dr. Jaffé, concentrated mainly around the detection and identification of the heat labile toxic factors in legume seeds. A micromethod for the detection of protease inhibitors (PI) in individual seeds was developed, for the purpose of establishing that the multiple trypsin inhibitors (TI) found in the Cubagua variety were expressions of single seeds and not a mixture of a non homogenous bean lot. Six isoinhibitors were isolated and purified, all of which were "double-headed" and interacted with trypsin (T) and chymotrypsin (CHT) independently and simultaneously, as shown by electrophoresis of their binary and ternary complexes with each and both enzymes. However, their affinity for the enzymes, including elastases, was rather variable, as well as their amino acid composition which consisted of 51 units for inhibitor V, the smallest, and 83 amino acids for inhibitor I, the largest. A low molecular weight protein fraction that inhibited subtilisin (S), but recognized neither T, CHT nor pancreatic elastase was detected in 63 varieties of Phaseolus vulgaris as well as in broad beans (Vicia faba), chick peas (Cicer arietinum), jack beans (Canavalia ensiformis), kidney beans (Vigna aureus), etc., It was absent though, in soybeans (Glycine max), lentils (Lens culinaris), green peas (Pisum sativum), cowpea (Vigna sinensis) and lupine seeds (Lupinus sp). Subtilisin inhibitors (SI) were isolated from black beans, broad beans, chick peas and jack beans. Their Mr is between 8-9KD and they show a rather high stability in the presence of denaturing agents. They are specific toward microbial proteases, in addition to subtilisins, Carlsberg and BPN', they inhibit the alkaline protease from Tritirachium album (Protease K), from Aspergillus oryzae and one isolated from

  11. Understanding Miltefosine-Membrane Interactions Using Molecular Dynamics Simulations.

    PubMed

    de Sá, Matheus Malta; Sresht, Vishnu; Rangel-Yagui, Carlota Oliveira; Blankschtein, Daniel

    2015-04-21

    Coarse-grained molecular dynamics simulations are used to calculate the free energies of transfer of miltefosine, an alkylphosphocholine anticancer agent, from water to lipid bilayers to study its mechanism of interaction with biological membranes. We consider bilayers containing lipids with different degrees of unsaturation: dipalmitoylphosphatidylcholine (DPPC, saturated, containing 0%, 10%, and 30% cholesterol), dioleoylphosphatidylcholine (DOPC, diunsaturated), palmitoyloleoylphosphatidylcholine (POPC, monounsaturated), diarachidonoylphosphatidylcholine (DAPC, polyunsaturated), and dilinoleylphosphatidylcholine (DUPC, polyunsaturated). These free energies, calculated using umbrella sampling, were used to compute the partition coefficients (K) of miltefosine between water and the lipid bilayers. The K values for the bilayers relative to that of pure DPPC were found to be 5.3 (DOPC), 7.0 (POPC), 1.0 (DAPC), 2.2 (DUPC), 14.9 (10% cholesterol), and 76.2 (30% cholesterol). Additionally, we calculated the free energy of formation of miltefosine-cholesterol complexes by pulling the surfactant laterally in the DPPC + 30% cholesterol system. The free energy profile that we obtained provides further evidence that miltefosine tends to associate with cholesterol and has a propensity to partition into lipid rafts. We also quantified the kinetics of the transport of miltefosine through the various bilayers by computing permeance values. The highest permeance was observed in DUPC bilayers (2.28 × 10(-2) m/s) and the lowest permeance in the DPPC bilayer with 30% cholesterol (1.10 × 10(-7) m/s). Our simulation results show that miltefosine does indeed interact with lipid rafts, has a higher permeability in polyunsaturated, loosely organized bilayers, and has higher flip-flop rates in specific regions of cellular membranes. PMID:25819781

  12. Molecular Ecological Insights into Neotropical Bird-Tick Interactions.

    PubMed

    Miller, Matthew J; Esser, Helen J; Loaiza, Jose R; Herre, Edward Allen; Aguilar, Celestino; Quintero, Diomedes; Alvarez, Eric; Bermingham, Eldredge

    2016-01-01

    In the tropics, ticks parasitize many classes of vertebrate hosts. However, because many tropical tick species are only identifiable in the adult stage, and these adults usually parasitize mammals, most attention on the ecology of tick-host interactions has focused on mammalian hosts. In contrast, immature Neotropical ticks are often found on wild birds, yet difficulties in identifying immatures hinder studies of birds' role in tropical tick ecology and tick-borne disease transmission. In Panama, we found immature ticks on 227 out of 3,498 individually-sampled birds representing 93 host species (24% of the bird species sampled, and 13% of the Panamanian land bird fauna). Tick parasitism rates did not vary with rainfall or temperature, but did vary significantly with several host ecological traits. Likewise, Neotropical-Nearctic migratory birds were significantly less likely to be infested than resident species. Using a molecular library developed from morphologically-identified adult ticks specifically for this study, we identified eleven tick species parasitizing birds, indicating that a substantial portion of the Panamanian avian species pool is parasitized by a diversity of tick species. Tick species that most commonly parasitized birds had the widest diversity of avian hosts, suggesting that immature tick species are opportunistic bird parasites. Although certain avian ecological traits are positively associated with parasitism, we found no evidence that individual tick species show specificity to particular avian host ecological traits. Finally, our data suggest that the four principal vectors of Rocky Mountain Spotted Fever in the Neotropics rarely, if ever, parasitize Panamanian birds. However, other tick species that harbor newly-discovered rickettsial parasites of unknown pathogenicity are frequently found on these birds. Given our discovery of broad interaction between Panamanian tick and avian biodiversity, future work on tick ecology and the dynamics of

  13. Molecular Ecological Insights into Neotropical Bird–Tick Interactions

    PubMed Central

    Esser, Helen J.; Loaiza, Jose R.; Herre, Edward Allen; Aguilar, Celestino; Quintero, Diomedes; Alvarez, Eric; Bermingham, Eldredge

    2016-01-01

    In the tropics, ticks parasitize many classes of vertebrate hosts. However, because many tropical tick species are only identifiable in the adult stage, and these adults usually parasitize mammals, most attention on the ecology of tick-host interactions has focused on mammalian hosts. In contrast, immature Neotropical ticks are often found on wild birds, yet difficulties in identifying immatures hinder studies of birds’ role in tropical tick ecology and tick-borne disease transmission. In Panama, we found immature ticks on 227 out of 3,498 individually–sampled birds representing 93 host species (24% of the bird species sampled, and 13% of the Panamanian land bird fauna). Tick parasitism rates did not vary with rainfall or temperature, but did vary significantly with several host ecological traits. Likewise, Neotropical–Nearctic migratory birds were significantly less likely to be infested than resident species. Using a molecular library developed from morphologically–identified adult ticks specifically for this study, we identified eleven tick species parasitizing birds, indicating that a substantial portion of the Panamanian avian species pool is parasitized by a diversity of tick species. Tick species that most commonly parasitized birds had the widest diversity of avian hosts, suggesting that immature tick species are opportunistic bird parasites. Although certain avian ecological traits are positively associated with parasitism, we found no evidence that individual tick species show specificity to particular avian host ecological traits. Finally, our data suggest that the four principal vectors of Rocky Mountain Spotted Fever in the Neotropics rarely, if ever, parasitize Panamanian birds. However, other tick species that harbor newly–discovered rickettsial parasites of unknown pathogenicity are frequently found on these birds. Given our discovery of broad interaction between Panamanian tick and avian biodiversity, future work on tick ecology and the

  14. The binary interacting network of the conserved oligomeric Golgi tethering complex.

    PubMed

    Loh, Eva; Hong, Wanjin

    2004-06-01

    Several recent studies have revealed the existence of a conserved oligomeric Golgi (COG) complex consisting of several novel proteins as well as known Golgi proteins that were identified by independent approaches. The mammalian COG complex contains eight subunits: COG1/LdlBp, COG2/LdlCp, COG3/Sec34, COG4/Cod1, COG5/GTC-90/Cod4, COG6/Cod2, COG7, and COG8/Dor1. COG1, COG2, and COG7 seem structurally unique to mammalian cells, whereas the other five subunits are structurally conserved in yeast, which also contains three other unique proteins (COG1/Sec36p/Cod3p, COG2/Sec35p, and COG7/Cod5p). We report here the network of intermolecular interactions of the COG complex, revealed by in vitro translation and co-immunoprecipitation approaches. Our results suggest that COG4 serves as a core component of the complex by interacting directly with COG1, COG2, COG5, and COG7. COG3 is incorporated by its direct interaction with COG1 and COG2, whereas COG6 and COG8 do not interact with any individual subunit. Incorporation of COG6 into the complex depends on the concerted interaction of both COG5 and COG7, whereas optimal incorporation of COG8 depends on the concerted interaction of COG5, COG6, and COG7. Because COG4 (together with COG1, COG2, and COG3) is among the four essential genes of the COG complex in yeast, this molecular network highlights the structural basis for a crucial role of COG4 in the assembly/function of the complex. A model for the assembly of the COG complex is presented. PMID:15047703

  15. Networks of Host Factors that Interact with NS1 Protein of Influenza A Virus

    PubMed Central

    Thulasi Raman, Sathya N.; Zhou, Yan

    2016-01-01

    Pigs are an important host of influenza A viruses due to their ability to generate reassortant viruses with pandemic potential. NS1 protein of influenza A viruses is a key virulence factor and a major antagonist of innate immune responses. It is also involved in enhancing viral mRNA translation and regulation of virus replication. Being a protein with pleiotropic functions, NS1 has a variety of cellular interaction partners. Hence, studies on swine influenza viruses (SIV) and identification of swine influenza NS1-interacting host proteins is of great interest. Here, we constructed a recombinant SIV carrying a Strep-tag in the NS1 protein and infected primary swine respiratory epithelial cells (SRECs) with this virus. The Strep-tag sequence in the NS1 protein enabled us to purify intact, the NS1 protein and its interacting protein complex specifically. We identified cellular proteins present in the purified complex by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and generated a dataset of these proteins. 445 proteins were identified by LC-MS/MS and among them 192 proteins were selected by setting up a threshold based on MS parameters. The selected proteins were analyzed by bioinformatics and were categorized as belonging to different functional groups including translation, RNA processing, cytoskeleton, innate immunity, and apoptosis. Protein interaction networks were derived using these data and the NS1 interactions with some of the specific host factors were verified by immunoprecipitation. The novel proteins and the networks revealed in our study will be the potential candidates for targeted study of the molecular interaction of NS1 with host proteins, which will provide insights into the identification of new therapeutic targets to control influenza infection and disease pathogenesis. PMID:27199973

  16. MOLECULAR INTERACTION POTENTIALS FOR THE DEVELOPMENT OF STRUCTURE-ACTIVITY RELATIONSHIPS

    EPA Science Inventory

    Abstract
    One reasonable approach to the analysis of the relationships between molecular structure and toxic activity is through the investigation of the forces and intermolecular interactions responsible for chemical toxicity. The interaction between the xenobiotic and the bio...

  17. In vitro induction and proteomics characterisation of a uranyl-protein interaction network in bovine serum.

    PubMed

    Szyrwiel, Łukasz; Liauchuk, Viktoryia; Chavatte, Laurent; Lobinski, Ryszard

    2015-12-01

    Uranyl ions (UO2(2+)) were shown to interact with a number of foetal serum proteins, leading to the formation of a complex that could be isolated by ultracentrifugation. The molecular weight of the complex was estimated based on size-exclusion chromatography as 650 000 Da. Online ICP AES detection indicated that UO2(2+) in the complex co-eluted with minor amounts of calcium and phosphorous, but not with magnesium. A 1D gel electrophoresis of the U-complex produced more than 10 bands of similar intensity compared with only 2-3 intense bands corresponding to the main serum proteins in the control serum, indicative of the specific interaction of UO2(2+) with minor proteins. A proteomics approach allowed for the identification of 74 proteins in the complex. Analysis of the protein-protein interaction network in the UO2(2+) complex identified 32 proteins responsible for protein-protein complex formation and 34 with demonstrated ion-binding function, suggesting that UO2(2+) stimulates the formation of protein functional networks rather than using a particular molecule as its target. PMID:26506398

  18. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning. PMID:27400769

  19. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    PubMed

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well. PMID:26243825

  20. CELLULAR AND MOLECULAR INTERACTIONS OF PHOSPHOINOSITIDES AND PERIPHERAL PROTEINS

    PubMed Central

    Stahelin, Robert V.; Scott, Jordan L.; Frick, Cary T.

    2015-01-01

    Anionic lipids act as signals for the recruitment of proteins containing cationic clusters to biological membranes. A family of anionic lipids known as the phosphoinositides (PIPs) are low in abundance, yet play a critical role in recruitment of peripheral proteins to the membrane interface. PIPs are mono-, bis-, or trisphosphorylated derivatives of phosphatidylinositol (PI) yielding seven species with different structure and anionic charge. The differential spatial distribution and temporal appearance of PIPs is key to their role in communicating information to target proteins. Selective recognition of PIPs came into play with the discovery that the substrate of protein kinase C termed pleckstrin possessed the first PIP binding region termed the pleckstrin homology (PH) domain. Since the discovery of the PH domain, more than ten PIP binding domains have been identified including PH, ENTH, FYVE, PX, and C2 domains. Representative examples of each of these domains have been thoroughly characterized to understand how they coordinate PIP headgroups in membranes, translocate to specific membrane docking sites in the cell, and function to regulate the activity of their full-length proteins. In addition, a number of novel mechanisms of PIP-mediated membrane association have emerged, such as coincidence detection – specificity for two distinct lipid headgroups. Other PIP-binding domains may also harbor selectivity for a membrane physical property such as charge or membrane curvature. This review summarizes the current understanding of the cellular distribution of PIPs and their molecular interaction with peripheral proteins. PMID:24556335

  1. Inferring causal molecular networks: empirical assessment through a community-based effort.

    PubMed

    Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

    2016-04-01

    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense. PMID:26901648

  2. Identification of Global Ferredoxin Interaction Networks in Chlamydomonas reinhardtii*

    PubMed Central

    Peden, Erin A.; Boehm, Marko; Mulder, David W.; Davis, ReAnna; Old, William M.; King, Paul W.; Ghirardi, Maria L.; Dubini, Alexandra

    2013-01-01

    Ferredoxins (FDXs) can distribute electrons originating from photosynthetic water oxidation, fermentation, and other reductant-generating pathways to specific redox enzymes in different organisms. The six FDXs identified in Chlamydomonas reinhardtii are not fully characterized in terms of their biological function. In this report, we present data from the following: (a) yeast two-hybrid screens, identifying interaction partners for each Chlamydomonas FDX; (b) pairwise yeast two-hybrid assays measuring FDX interactions with proteins from selected biochemical pathways; (c) affinity pulldown assays that, in some cases, confirm and even expand the interaction network for FDX1 and FDX2; and (d) in vitro NADP+ reduction and H2 photo-production assays mediated by each FDX that verify their role in these two pathways. Our results demonstrate new potential roles for FDX1 in redox metabolism and carbohydrate and fatty acid biosynthesis, for FDX2 in anaerobic metabolism, and possibly in state transition. Our data also suggest that FDX3 is involved in nitrogen assimilation, FDX4 in glycolysis and response to reactive oxygen species, and FDX5 in hydrogenase maturation. Finally, we provide experimental evidence that FDX1 serves as the primary electron donor to two important biological pathways, NADPH and H2 photo-production, whereas FDX2 is capable of driving these reactions at less than half the rate observed for FDX1. PMID:24100040

  3. Phage-bacteria interaction network in human oral microbiome.

    PubMed

    Wang, Jinfeng; Gao, Yuan; Zhao, Fangqing

    2016-07-01

    Although increasing knowledge suggests that bacteriophages play important roles in regulating microbial ecosystems, phage-bacteria interaction in human oral cavities remains less understood. Here we performed a metagenomic analysis to explore the composition and variation of oral dsDNA phage populations and potential phage-bacteria interaction. A total of 1,711 contigs assembled with more than 100 Gb shotgun sequencing data were annotated to 104 phages based on their best BLAST matches against the NR database. Bray-Curtis dissimilarities demonstrated that both phage and bacterial composition are highly diverse between periodontally healthy samples but show a trend towards homogenization in diseased gingivae samples. Significantly, according to the CRISPR arrays that record infection relationship between bacteria and phage, we found certain oral phages were able to invade other bacteria besides their putative bacterial hosts. These cross-infective phages were positively correlated with commensal bacteria while were negatively correlated with major periodontal pathogens, suggesting possible connection between these phages and microbial community structure in oral cavities. By characterizing phage-bacteria interaction as networks rather than exclusively pairwise predator-prey relationships, our study provides the first insight into the participation of cross-infective phages in forming human oral microbiota. PMID:26036920

  4. Modeling attacker-defender interactions in information networks.

    SciTech Connect

    Collins, Michael Joseph

    2010-09-01

    The simplest conceptual model of cybersecurity implicitly views attackers and defenders as acting in isolation from one another: an attacker seeks to penetrate or disrupt a system that has been protected to a given level, while a defender attempts to thwart particular attacks. Such a model also views all non-malicious parties as having the same goal of preventing all attacks. But in fact, attackers and defenders are interacting parts of the same system, and different defenders have their own individual interests: defenders may be willing to accept some risk of successful attack if the cost of defense is too high. We have used game theory to develop models of how non-cooperative but non-malicious players in a network interact when there is a substantial cost associated with effective defensive measures. Although game theory has been applied in this area before, we have introduced some novel aspects of player behavior in our work, including: (1) A model of how players attempt to avoid the costs of defense and force others to assume these costs; (2) A model of how players interact when the cost of defending one node can be shared by other nodes; and (3) A model of the incentives for a defender to choose less expensive, but less effective, defensive actions.

  5. Relationship between molecular weight of poly(ethylene)glycol and intermolecular interaction of Taka-amylase A monomers

    NASA Astrophysics Data System (ADS)

    Onuma, Kazuo; Furubayashi, Naoki; Shibata, Fujiko; Kobayashi, Yoshiko; Kaito, Sachiko; Ohnishi, Yuki; Inaka, Koji

    2010-04-01

    Dynamic and static light scattering investigations of Taka-amylase A (TAA) protein monomers were done using solutions containing poly(ethylene)glycol (PEG) with molecular weights of 1500, 4000, 8000, and 20 000. The anomalies observed in a previous study using a weight of 8000, in which the hydrodynamic TAA monomer radius at a zero protein concentration and the molecular weight of the monomers decreased when the PEG concentration was increased, were observed for all four weights. These anomalies became more pronounced as the PEG molecular weight was increased. The overall interaction parameter did not move further in the direction of the attractive force despite an increase in the PEG concentration from 6% to 12.5% for the PEG 8000 and 20 000 solutions. This was due to the change in the relative contributions of the static structure factor (direct interaction) and the hydrodynamic interaction factor (indirect interaction) against the overall interaction parameter. For the PEG 1500 and 4000 solutions, the change in the overall interaction parameter with an increase in the PEG concentration was controlled by changing the static structure factor. For the PEG 8000 and 20 000 solutions, a change in the hydrodynamic interaction factor with an increase in the PEG concentration offset the change in the static structure factor, unexpectedly resulting in the overall interaction parameter being independent of the PEG concentration. This suggests that the scale and density of a PEG network structure, which are thought to be the origin of the observed anomalies, change nonlinearly with the PEG molecular weight.

  6. STRING v10: protein-protein interaction networks, integrated over the tree of life.

    PubMed

    Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan; Forslund, Kristoffer; Heller, Davide; Huerta-Cepas, Jaime; Simonovic, Milan; Roth, Alexander; Santos, Alberto; Tsafou, Kalliopi P; Kuhn, Michael; Bork, Peer; Jensen, Lars J; von Mering, Christian

    2015-01-01

    The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks. PMID:25352553

  7. Characterizing WW Domain Interactions of Tumor Suppressor WWOX Reveals Its Association with Multiprotein Networks*

    PubMed Central

    Abu-Odeh, Mohammad; Bar-Mag, Tomer; Huang, Haiming; Kim, TaeHyung; Salah, Zaidoun; Abdeen, Suhaib K.; Sudol, Marius; Reichmann, Dana; Sidhu, Sachdev; Kim, Philip M.; Aqeilan, Rami I.

    2014-01-01

    WW domains are small modules present in regulatory and signaling proteins that mediate specific protein-protein interactions. The WW domain-containing oxidoreductase (WWOX) encodes a 46-kDa tumor suppressor that contains two N-terminal WW domains and a central short-chain dehydrogenase/reductase domain. Based on its ligand recognition motifs, the WW domain family is classified into four groups. The largest one, to which WWOX belongs, recognizes ligands with a PPXY motif. To pursue the functional properties of the WW domains of WWOX, we employed mass spectrometry and phage display experiments to identify putative WWOX-interacting partners. Our analysis revealed that the first WW (WW1) domain of WWOX is the main functional interacting domain. Furthermore, our study uncovered well known and new PPXY-WW1-interacting partners and shed light on novel LPXY-WW1-interacting partners of WWOX. Many of these proteins are components of multiprotein complexes involved in molecular processes, including transcription, RNA processing, tight junction, and metabolism. By utilizing GST pull-down and immunoprecipitation assays, we validated that WWOX is a substrate of the E3 ubiquitin ligase ITCH, which contains two LPXY motifs. We found that ITCH mediates Lys-63-linked polyubiquitination of WWOX, leading to its nuclear localization and increased cell death. Our data suggest that the WW1 domain of WWOX provides a versatile platform that links WWOX with individual proteins associated with physiologically important networks. PMID:24550385

  8. System-level comparison of protein-protein interactions between viruses and the human type I interferon system network.

    PubMed

    Navratil, V; de Chassey, B; Meyniel, L; Pradezynski, F; André, P; Rabourdin-Combe, C; Lotteau, V

    2010-07-01

    Innate immunity has evolved complex molecular pathways to protect organisms from viral infections. One pivotal line of cellular defense is the induction of the antiviral effect of interferon. To circumvent this primary response and achieve their own replication, viruses have developed complex molecular strategies. Here, we provide a systems-level study of the human type I interferon system subversion by the viral proteome, by reconstructing the underlying protein-protein interaction network. At this network level, viruses establish a massive and a gradual attack, from receptors to transcription factors, by interacting preferentially with highly connected and central proteins as well as interferon-induced proteins. We also demonstrate that viruses significantly target 22% of the proteins directly interacting with the type I interferon system network, suggesting the relevance of our network-based method to identify new candidates involved in the regulation of the antiviral response. Finally, based on the comparative analysis of interactome profiles across four viral families, we provide evidence of common and differential targeting strategies. PMID:20459142

  9. Nanovoid formation and mechanics: a comparison of poly(dicyclopentadiene) and epoxy networks from molecular dynamics simulations.

    PubMed

    Elder, Robert M; Knorr, Daniel B; Andzelm, Jan W; Lenhart, Joseph L; Sirk, Timothy W

    2016-05-11

    Protective equipment in civilian and military applications requires the use of polymer materials that are both stiff and tough over a wide range of strain rates. However, typical structural materials, like tightly cross-linked epoxies, are very brittle. Recent experiments demonstrated that cross-linked poly(dicyclopentadiene) (pDCPD) networks can circumvent this trade-off by providing structural properties such as a high glass transition temperature and glassy modulus, while simultaneously exhibiting excellent toughness and high-rate impact resistance. The greater performance of pDCPD was attributed to more facile plastic deformation and nano-scale void formation, but the chemical and structural mechanisms underlying this response were not clear. Here, we use atomistic molecular dynamics to compare the molecular- and chain-level properties of pDCPD and epoxy networks undergoing high strain rate deformation. We quantify the tensile modulus and yield strength of the networks as well as the prevalence and characteristics of nanovoids that form during deformation. Networks of similar molecular weight between cross-links are compared. Two key molecular-level properties are identified - monomer flexibility and polar chemistry - that influence the behavior of the networks. Increasing monomer flexibility reduces the modulus and yield strength, while strong non-covalent interactions (e.g., hydrogen bonds) that accompany polar moieties provide higher modulus and yield strength. The lack of strong non-covalent interactions in pDCPD was found to account for its lower modulus and yield strength compared to the epoxies. We examine the molecular-level properties of nanovoids, such as shape, alignment, and local stress distribution, as well as the local chemical environment, finding that nanovoid formation and growth are increased by the monomer rigidity but decreased by polar chemistry. As a result, the pDCPD network, which has a stiff chain backbone with nonpolar alkane

  10. Crop epigenetics and the molecular hardware of genotype × environment interactions

    PubMed Central

    King, Graham J.

    2015-01-01

    Crop plants encounter thermal environments which fluctuate on a diurnal and seasonal basis. Future climate resilient cultivars will need to respond to thermal profiles reflecting more variable conditions, and harness plasticity that involves regulation of epigenetic processes and complex genomic regulatory networks. Compartmentalization within plant cells insulates the genomic central processing unit within the interphase nucleus. This review addresses the properties of the chromatin hardware in which the genome is embedded, focusing on the biophysical and thermodynamic properties of DNA, histones and nucleosomes. It explores the consequences of thermal and ionic variation on the biophysical behavior of epigenetic marks such as DNA cytosine methylation (5mC), and histone variants such as H2A.Z, and how these contribute to maintenance of chromatin integrity in the nucleus, while enabling specific subsets of genes to be regulated. Information is drawn from theoretical molecular in vitro studies as well as model and crop plants and incorporates recent insights into the role epigenetic processes play in mediating between environmental signals and genomic regulation. A preliminary speculative framework is outlined, based on the evidence of what appears to be a cohesive set of interactions at molecular, biophysical and electrostatic level between the various components contributing to chromatin conformation and dynamics. It proposes that within plant nuclei, general and localized ionic homeostasis plays an important role in maintaining chromatin conformation, whilst maintaining complex genomic regulation that involves specific patterns of epigenetic marks. More generally, reversible changes in DNA methylation appear to be consistent with the ability of nuclear chromatin to manage variation in external ionic and temperature environment. Whilst tentative, this framework provides scope to develop experimental approaches to understand in greater detail the internal

  11. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  12. Ochratoxin A: Molecular Interactions, Mechanisms of Toxicity and Prevention at the Molecular Level.

    PubMed

    Kőszegi, Tamás; Poór, Miklós

    2016-04-01

    Ochratoxin A (OTA) is a widely-spread mycotoxin all over the world causing major health risks. The focus of the present review is on the molecular and cellular interactions of OTA. In order to get better insight into the mechanism of its toxicity and on the several attempts made for prevention or attenuation of its toxic action, a detailed description is given on chemistry and toxicokinetics of this mycotoxin. The mode of action of OTA is not clearly understood yet, and seems to be very complex. Inhibition of protein synthesis and energy production, induction of oxidative stress, DNA adduct formation, as well as apoptosis/necrosis and cell cycle arrest are possibly involved in its toxic action. Since OTA binds very strongly to human and animal albumin, a major emphasis is done regarding OTA-albumin interaction. Displacement of OTA from albumin by drugs and by natural flavonoids are discussed in detail, hypothesizing their potentially beneficial effect in order to prevent or attenuate the OTA-induced toxic consequences. PMID:27092524

  13. Ochratoxin A: Molecular Interactions, Mechanisms of Toxicity and Prevention at the Molecular Level

    PubMed Central

    Kőszegi, Tamás; Poór, Miklós

    2016-01-01

    Ochratoxin A (OTA) is a widely-spread mycotoxin all over the world causing major health risks. The focus of the present review is on the molecular and cellular interactions of OTA. In order to get better insight into the mechanism of its toxicity and on the several attempts made for prevention or attenuation of its toxic action, a detailed description is given on chemistry and toxicokinetics of this mycotoxin. The mode of action of OTA is not clearly understood yet, and seems to be very complex. Inhibition of protein synthesis and energy production, induction of oxidative stress, DNA adduct formation, as well as apoptosis/necrosis and cell cycle arrest are possibly involved in its toxic action. Since OTA binds very strongly to human and animal albumin, a major emphasis is done regarding OTA-albumin interaction. Displacement of OTA from albumin by drugs and by natural flavonoids are discussed in detail, hypothesizing their potentially beneficial effect in order to prevent or attenuate the OTA-induced toxic consequences. PMID:27092524

  14. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    PubMed Central

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  15. Interacting Bose gas, the logistic law, and complex networks

    NASA Astrophysics Data System (ADS)

    Sowa, A.

    2015-01-01

    We discuss a mathematical link between the Quantum Statistical Mechanics and the logistic growth and decay processes. It is based on an observation that a certain nonlinear operator evolution equation, which we refer to as the Logistic Operator Equation (LOE), provides an extension of the standard model of noninteracting bosons. We discuss formal solutions (asymptotic formulas) for a special calibration of the LOE, which sets it in the number-theoretic framework. This trick, in the tradition of Julia and Bost-Connes, makes it possible for us to tap into the vast resources of classical mathematics and, in particular, to construct explicit solutions of the LOE via the Dirichlet series. The LOE is applicable to a range of modeling and simulation tasks, from characterization of interacting boson systems to simulation of some complex man-made networks. The theoretical results enable numerical simulations, which, in turn, shed light at the unique complexities of the rich and multifaceted models resulting from the LOE.

  16. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  17. Commentary: Biochemistry and Molecular Biology Educators Launch National Network

    ERIC Educational Resources Information Center

    Bailey, Cheryl; Bell, Ellis; Johnson, Margaret; Mattos, Carla; Sears, Duane; White, Harold B.

    2010-01-01

    The American Society of Biochemistry and Molecular Biology (ASBMB) has launched an National Science Foundation (NSF)-funded 5 year project to support biochemistry and molecular biology educators learning what and how students learn. As a part of this initiative, hundreds of life scientists will plan and develop a rich central resource for…

  18. Module organization and variance in protein-protein interaction networks

    PubMed Central

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-01-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions. PMID:25797237

  19. Ensemble transcript interaction networks: a case study on Alzheimer's disease.

    PubMed

    Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

    2012-10-01

    Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classi