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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

    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

  14. 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

  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

    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

  20. 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

  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. 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.

  15. 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.

  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. 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.

  2. 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).

  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. 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

  8. 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

  9. 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)

  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 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

  9. 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

  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. 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

  16. 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

  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. 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

  1. 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.

  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. 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

  13. 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

  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

    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.

  19. 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

  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. 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

  9. 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

  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. 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.

  15. 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

  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. 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

  8. 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

  9. 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,…

  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. 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.

  13. 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

  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. 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.

  18. 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

  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. 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

  1. 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

  2. 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

  3. 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.

  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. 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

  6. 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.

  7. 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),…

  8. 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)

  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. 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.

  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. 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

  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. 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.

  4. 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).

  5. 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

  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. 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.

  9. 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

  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. 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

  1. 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.

  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

    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

  5. 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

  6. 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

  7. 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.

  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.