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Sample records for molecular interaction networks

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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

  7. 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.

  8. Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer

    PubMed Central

    Xu, Jianting; E, Changyong; Yao, Yongfang; Ren, Shuangchun; Wang, Guoqing; Jin, Haofan

    2016-01-01

    Gastric cancer (GC) is one of the most common types of cancer of the digestive tract. Invasion of tumor cells into surrounding tissue and metastasis are among the most significant checkpoints in tumor progression. It is known that matrix metalloproteinases (MMPs) are involved in these processes; however, knowledge of their molecular interaction networks is still limited. Investigation of these networks could provide a more comprehensive picture of the function of MMPs in tumorigenesis. Furthermore, it could be used to develop new approaches to targeted anticancer therapy. In this study, we performed microarray analysis, and 1666 genes that were aberrantly expressed in GC tissues were identified (fold change >2, P<0.05). In addition, quantitative polymerase chain reaction analysis has confirmed that MMP1, MMP3, MMP7, MMP10, MMP11 and MMP12 expression is upregulated in GC. In addition, the MMP3 expression level was negatively correlated with GC differentiation (P<0.05). By integrating the microarray information and BioGRID and STRING databases, we constructed an MMP-related molecular interaction network and observed that 18 genes (including MMPs) were highly expressed in GC tissues. The most enriched of these 18 genes in the Gene Oncology (GO) and pathway analysis were in extracellular matrix disassembly (GO biological process) and extracellular matrix-receptor interaction (KEGG pathway), which are closely correlated with cancer invasion and metastasis. Collectively, our results suggest that the MMP-related interaction network has a role in GC progression, and therefore further studies are required in order to investigate these network interactions in tumorigenesis. PMID:27698806

  9. Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer

    PubMed Central

    Xu, Jianting; E, Changyong; Yao, Yongfang; Ren, Shuangchun; Wang, Guoqing; Jin, Haofan

    2016-01-01

    Gastric cancer (GC) is one of the most common types of cancer of the digestive tract. Invasion of tumor cells into surrounding tissue and metastasis are among the most significant checkpoints in tumor progression. It is known that matrix metalloproteinases (MMPs) are involved in these processes; however, knowledge of their molecular interaction networks is still limited. Investigation of these networks could provide a more comprehensive picture of the function of MMPs in tumorigenesis. Furthermore, it could be used to develop new approaches to targeted anticancer therapy. In this study, we performed microarray analysis, and 1666 genes that were aberrantly expressed in GC tissues were identified (fold change >2, P<0.05). In addition, quantitative polymerase chain reaction analysis has confirmed that MMP1, MMP3, MMP7, MMP10, MMP11 and MMP12 expression is upregulated in GC. In addition, the MMP3 expression level was negatively correlated with GC differentiation (P<0.05). By integrating the microarray information and BioGRID and STRING databases, we constructed an MMP-related molecular interaction network and observed that 18 genes (including MMPs) were highly expressed in GC tissues. The most enriched of these 18 genes in the Gene Oncology (GO) and pathway analysis were in extracellular matrix disassembly (GO biological process) and extracellular matrix-receptor interaction (KEGG pathway), which are closely correlated with cancer invasion and metastasis. Collectively, our results suggest that the MMP-related interaction network has a role in GC progression, and therefore further studies are required in order to investigate these network interactions in tumorigenesis.

  10. 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

  11. 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

  12. Structural properties and interaction energies affecting drug design. An approach combining molecular simulations, statistics, interaction energies and neural networks.

    PubMed

    Ioannidis, Dimitris; Papadopoulos, Georgios E; Anastassopoulos, Georgios; Kortsaris, Alexandros; Anagnostopoulos, Konstantinos

    2015-06-01

    In order to elucidate some basic principles for protein-ligand interactions, a subset of 87 structures of human proteins with their ligands was obtained from the PDB databank. After a short molecular dynamics simulation (to ensure structure stability), a variety of interaction energies and structural parameters were extracted. Linear regression was performed to determine which of these parameters have a potentially significant contribution to the protein-ligand interaction. The parameters exhibiting relatively high correlation coefficients were selected. Important factors seem to be the number of ligand atoms, the ratio of N, O and S atoms to total ligand atoms, the hydrophobic/polar aminoacid ratio and the ratio of cavity size to the sum of ligand plus water atoms in the cavity. An important factor also seems to be the immobile water molecules in the cavity. Nine of these parameters were used as known inputs to train a neural network in the prediction of seven other. Eight structures were left out of the training to test the quality of the predictions. After optimization of the neural network, the predictions were fairly accurate given the relatively small number of structures, especially in the prediction of the number of nitrogen and sulfur atoms of the ligand.

  13. Gene Expression Correlations in Human Cancer Cell Lines Define Molecular Interaction Networks for Epithelial Phenotype

    PubMed Central

    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

  14. 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

  15. Novel interactions of CLN5 support molecular networking between Neuronal Ceroid Lipofuscinosis proteins

    PubMed Central

    2009-01-01

    Background Neuronal ceroid lipofuscinoses (NCLs) comprise at least eight genetically characterized neurodegenerative disorders of childhood. Despite of genetic heterogeneity, the high similarity of clinical symptoms and pathology of different NCL disorders suggest cooperation between different NCL proteins and common mechanisms of pathogenesis. Here, we have studied molecular interactions between NCL proteins, concentrating specifically on the interactions of CLN5, the protein underlying the Finnish variant late infantile form of NCL (vLINCLFin). Results We found that CLN5 interacts with several other NCL proteins namely, CLN1/PPT1, CLN2/TPP1, CLN3, CLN6 and CLN8. Furthermore, analysis of the intracellular targeting of CLN5 together with the interacting NCL proteins revealed that over-expression of PPT1 can facilitate the lysosomal transport of mutated CLN5FinMajor, normally residing in the ER and in the Golgi complex. The significance of the novel interaction between CLN5 and PPT1 was further supported by the finding that CLN5 was also able to bind the F1-ATPase, earlier shown to interact with PPT1. Conclusion We have described novel interactions between CLN5 and several NCL proteins, suggesting a modifying role for these proteins in the pathogenesis of individual NCL disorders. Among these novel interactions, binding of CLN5 to CLN1/PPT1 is suggested to be the most significant one, since over-expression of PPT1 was shown to influence on the intracellular trafficking of mutated CLN5, and they were shown to share a binding partner outside the NCL protein spectrum. PMID:19941651

  16. Influence of Macromer Molecular Weight and Chemistry on Poly(β-amino ester) Network Properties and Initial Cell Interactions

    PubMed Central

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

    2009-01-01

    A library of photocrosslinkable poly(β-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 (≥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 due to 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

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs.

    PubMed

    Allain, Ariane; Chauvot de Beauchêne, Isaure; Langenfeld, Florent; Guarracino, Yann; Laine, Elodie; Tchertanov, Luba

    2014-01-01

    Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non

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

    PubMed

    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.

  3. 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

  4. 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.

  5. 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

  6. 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.

  7. Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

    PubMed

    Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

    2015-12-01

    Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.

  8. 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

  9. Electrostatic interactions in molecular materials

    NASA Astrophysics Data System (ADS)

    Painelli, Anna; Terenziani, Francesca

    2004-03-01

    Non-additive collective behavior appears in molecular materials as a result of intermolecular interactions. We present a model for interacting polar and polarizable molecules that applies to different supramolecular architectures of donor-π-acceptor molecules. We follow a bottom-up modeling strategy: the detailed analysis of spectroscopic data of solvated molecules leads to the definition of a simple two-state model for the molecular units. Classical electrostatic interactions are then introduced to model molecular clusters. The molecular properties are strickingly affected by supramolecular interactions, as demonstrated by spectroscopic studies. Brand new phenomena, like phase transitions and multielectron transfer, with no counterpart at the molecular level are observed as direct consequences of electrostatic intermolecular interactions.

  10. 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

  11. Synergistic interactions between the molecular and neuronal circadian networks drive robust behavioral circadian rhythms in Drosophila melanogaster.

    PubMed

    Weiss, Ron; Bartok, Osnat; Mezan, Shaul; Malka, Yuval; Kadener, Sebastian

    2014-04-01

    Most organisms use 24-hr circadian clocks to keep temporal order and anticipate daily environmental changes. In Drosophila melanogaster CLOCK (CLK) and CYCLE (CYC) initiates the circadian system by promoting rhythmic transcription of hundreds of genes. However, it is still not clear whether high amplitude transcriptional oscillations are essential for circadian timekeeping. In order to address this issue, we generated flies in which the amplitude of CLK-driven transcription can be reduced partially (approx. 60%) or strongly (90%) without affecting the average levels of CLK-target genes. The impaired transcriptional oscillations lead to low amplitude protein oscillations that were not sufficient to drive outputs of peripheral oscillators. However, circadian rhythms in locomotor activity were resistant to partial reduction in transcriptional and protein oscillations. We found that the resilience of the brain oscillator is depending on the neuronal communication among circadian neurons in the brain. Indeed, the capacity of the brain oscillator to overcome low amplitude transcriptional oscillations depends on the action of the neuropeptide PDF and on the pdf-expressing cells having equal or higher amplitude of molecular rhythms than the rest of the circadian neuronal groups in the fly brain. Therefore, our work reveals the importance of high amplitude transcriptional oscillations for cell-autonomous circadian timekeeping. Moreover, we demonstrate that the circadian neuronal network is an essential buffering system that protects against changes in circadian transcription in the brain. PMID:24698952

  12. Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions.

    PubMed

    Baltoumas, Fotis A; Theodoropoulou, Margarita C; Hamodrakas, Stavros J

    2016-06-01

    A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

  13. Protein-protein interaction and pathway analyses of top schizophrenia genes reveal schizophrenia susceptibility genes converge on common molecular networks and enrichment of nucleosome (chromatin) assembly genes in schizophrenia susceptibility loci.

    PubMed

    Luo, Xiongjian; Huang, Liang; Jia, Peilin; Li, Ming; Su, Bing; Zhao, Zhongming; Gan, Lin

    2014-01-01

    Recent genome-wide association studies have identified many promising schizophrenia candidate genes and demonstrated that common polygenic variation contributes to schizophrenia risk. However, whether these genes represent perturbations to a common but limited set of underlying molecular processes (pathways) that modulate risk to schizophrenia remains elusive, and it is not known whether these genes converge on common biological pathways (networks) or represent different pathways. In addition, the theoretical and genetic mechanisms underlying the strong genetic heterogeneity of schizophrenia remain largely unknown. Using 4 well-defined data sets that contain top schizophrenia susceptibility genes and applying protein-protein interaction (PPI) network analysis, we investigated the interactions among proteins encoded by top schizophrenia susceptibility genes. We found proteins encoded by top schizophrenia susceptibility genes formed a highly significant interconnected network, and, compared with random networks, these PPI networks are statistically highly significant for both direct connectivity and indirect connectivity. We further validated these results using empirical functional data (transcriptome data from a clinical sample). These highly significant findings indicate that top schizophrenia susceptibility genes encode proteins that significantly directly interacted and formed a densely interconnected network, suggesting perturbations of common underlying molecular processes or pathways that modulate risk to schizophrenia. Our findings that schizophrenia susceptibility genes encode a highly interconnected protein network may also provide a novel explanation for the observed genetic heterogeneity of schizophrenia, ie, mutation in any member of this molecular network will lead to same functional consequences that eventually contribute to risk of schizophrenia.

  14. 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.

  15. Embryonic stem cells: protein interaction networks*

    PubMed Central

    Ng, Patricia Miang-Lon; Lufkin, Thomas

    2012-01-01

    Embryonic stem cells have the ability to differentiate into nearly all cell types. However, the molecular mechanism of its pluripotency is still unclear. Oct3/4, Sox2 and Nanog are important factors of pluripotency. Oct3/4 (hereafter referred to as Oct4), in particular, has been an irreplaceable factor in the induction of pluripotency in adult cells. Proteins interacting with Oct4 and Nanog have been identified via affinity purification and mass spectrometry. These data, together with iterative purifications of interacting proteins allowed a protein interaction network to be constructed. The network currently includes 77 transcription factors, all of which are interconnected in one network. In-depth studies of some of these transcription factors show that they all recruit the NuRD complex. Hence, transcription factor clustering and chromosomal remodeling are key mechanism used by embryonic stem cells. Studies using RNA interference suggest that more pluripotency genes are yet to be discovered via protein-protein interactions. More work is required to complete and curate the embryonic stem cell protein interaction network. Analysis of a saturated protein interaction network by system biology tools can greatly aid in the understanding of the embryonic stem cell pluripotency network. PMID:22639699

  16. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.

    PubMed

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

    2013-11-21

    Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.

  17. 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

  18. Molecular Soybean-Pathogen Interactions.

    PubMed

    Whitham, Steven A; Qi, Mingsheng; Innes, Roger W; Ma, Wenbo; Lopes-Caitar, Valéria; Hewezi, Tarek

    2016-08-01

    Soybean hosts a wide variety of pathogens that cause significant yield losses. The importance of soybean as a major oilseed crop has led to research focused on its interactions with pathogens, such as Soybean mosaic virus, Pseudomonas syringae, Phytophthora sojae, Phakopsora pachyrhizi, and Heterodera glycines. Pioneering work on soybean's interactions with these organisms, which represent the five major pathogen groups (viruses, bacteria, oomycetes, fungi, and nematodes), has contributed to our understanding of the molecular mechanisms underlying virulence and immunity. These mechanisms involve conserved and unique features that validate the need for research in both soybean and homologous model systems. In this review, we discuss identification of effectors and their functions as well as resistance gene-mediated recognition and signaling. We also point out areas in which model systems and recent advances in resources and tools have provided opportunities to gain deeper insights into soybean-pathogen interactions. PMID:27359370

  19. 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.

  20. From networks of protein interactions to networks of functional dependencies

    PubMed Central

    2012-01-01

    Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation). However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations), based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud) or biological processes (e.g., cell budding) of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms. PMID:22607727

  1. Interaction intimacy organizes networks of antagonistic interactions in different ways

    PubMed Central

    Pires, Mathias M.; Guimarães, Paulo R.

    2013-01-01

    Interaction intimacy, the degree of biological integration between interacting individuals, shapes the ecology and evolution of species interactions. A major question in ecology is whether interaction intimacy also shapes the way interactions are organized within communities. We combined analyses of network structure and food web models to test the role of interaction intimacy in determining patterns of antagonistic interactions, such as host–parasite, predator–prey and plant–herbivore interactions. Networks describing interactions with low intimacy were more connected, more nested and less modular than high-intimacy networks. Moreover, the performance of the models differed across networks with different levels of intimacy. All models reproduced well low-intimacy networks, whereas the more elaborate models were also capable of reproducing networks depicting interactions with higher levels of intimacy. Our results indicate the key role of interaction intimacy in organizing antagonisms, suggesting that greater interaction intimacy might be associated with greater complexity in the assembly rules shaping ecological networks. PMID:23015523

  2. Interaction intimacy organizes networks of antagonistic interactions in different ways.

    PubMed

    Pires, Mathias M; Guimarães, Paulo R

    2013-01-01

    Interaction intimacy, the degree of biological integration between interacting individuals, shapes the ecology and evolution of species interactions. A major question in ecology is whether interaction intimacy also shapes the way interactions are organized within communities. We combined analyses of network structure and food web models to test the role of interaction intimacy in determining patterns of antagonistic interactions, such as host-parasite, predator-prey and plant-herbivore interactions. Networks describing interactions with low intimacy were more connected, more nested and less modular than high-intimacy networks. Moreover, the performance of the models differed across networks with different levels of intimacy. All models reproduced well low-intimacy networks, whereas the more elaborate models were also capable of reproducing networks depicting interactions with higher levels of intimacy. Our results indicate the key role of interaction intimacy in organizing antagonisms, suggesting that greater interaction intimacy might be associated with greater complexity in the assembly rules shaping ecological networks.

  3. 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

  4. 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

  5. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  6. 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

  7. 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.

  8. 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.

  9. Molecular geometry directed Kagomé and honeycomb networks: toward two-dimensional crystal engineering.

    PubMed

    Furukawa, Shuhei; Uji-i, Hiroshi; Tahara, Kazukuni; Ichikawa, Tomoyuki; Sonoda, Motohiro; De Schryver, Frans C; Tobe, Yoshito; De Feyter, Steven

    2006-03-22

    We present here the formation of a molecular Kagomé network within a two-dimensional (2D) crystal on a surface. This system provides a clear example of how, by design, molecular geometry can be expressed at the level of the 2D crystal lattice, leading to the formation of open networks. Key elements to control molecular network formation are core symmetry, location and orientation of interacting and connecting substituents, as well as symmetry matching between the networks and the surface.

  10. Molecular Networking as a Dereplication Strategy

    PubMed Central

    Yang, Jane Y.; Sanchez, Laura M.; Rath, Christopher M.; Liu, Xueting; Boudreau, Paul D.; Bruns, Nicole; Glukhov, Evgenia; Wodtke, Anne; de Felicio, Rafael; Fenner, Amanda; Ruh Wong, Weng; Linington, Roger G.; Zhang, Lixin; Debonsi, Hosana M.; Gerwick, William H.; Dorrestein, Pieter C.

    2014-01-01

    A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogs, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogs. PMID:24025162

  11. Molecular networking as a dereplication strategy.

    PubMed

    Yang, Jane Y; Sanchez, Laura M; Rath, Christopher M; Liu, Xueting; Boudreau, Paul D; Bruns, Nicole; Glukhov, Evgenia; Wodtke, Anne; de Felicio, Rafael; Fenner, Amanda; Wong, Weng Ruh; Linington, Roger G; Zhang, Lixin; Debonsi, Hosana M; Gerwick, William H; Dorrestein, Pieter C

    2013-09-27

    A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogues, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogues.

  12. Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

    PubMed Central

    2014-01-01

    Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

  13. 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.

  14. Phylogenetic analysis of modularity in protein interaction networks

    PubMed Central

    Erten, Sinan; Li, Xin; Bebek, Gurkan; Li, Jing; Koyutürk, Mehmet

    2009-01-01

    Background In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity. Results In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology. Conclusion These results establish

  15. Molecular communication and networking: opportunities and challenges.

    PubMed

    Nakano, Tadashi; Moore, Michael J; Wei, Fang; Vasilakos, Athanasios V; Shuai, Jianwei

    2012-06-01

    The ability of engineered biological nanomachines to communicate with biological systems at the molecular level is anticipated to enable future applications such as monitoring the condition of a human body, regenerating biological tissues and organs, and interfacing artificial devices with neural systems. From the viewpoint of communication theory and engineering, molecular communication is proposed as a new paradigm for engineered biological nanomachines to communicate with the natural biological nanomachines which form a biological system. Distinct from the current telecommunication paradigm, molecular communication uses molecules as the carriers of information; sender biological nanomachines encode information on molecules and release the molecules in the environment, the molecules then propagate in the environment to receiver biological nanomachines, and the receiver biological nanomachines biochemically react with the molecules to decode information. Current molecular communication research is limited to small-scale networks of several biological nanomachines. Key challenges to bridge the gap between current research and practical applications include developing robust and scalable techniques to create a functional network from a large number of biological nanomachines. Developing networking mechanisms and communication protocols is anticipated to introduce new avenues into integrating engineered and natural biological nanomachines into a single networked system. In this paper, we present the state-of-the-art in the area of molecular communication by discussing its architecture, features, applications, design, engineering, and physical modeling. We then discuss challenges and opportunities in developing networking mechanisms and communication protocols to create a network from a large number of bio-nanomachines for future applications.

  16. MINT: a Molecular INTeraction database.

    PubMed

    Zanzoni, Andreas; Montecchi-Palazzi, Luisa; Quondam, Michele; Ausiello, Gabriele; Helmer-Citterich, Manuela; Cesareni, Gianni

    2002-02-20

    Protein interaction databases represent unique tools to store, in a computer readable form, the protein interaction information disseminated in the scientific literature. Well organized and easily accessible databases permit the easy retrieval and analysis of large interaction data sets. Here we present MINT, a database (http://cbm.bio.uniroma2.it/mint/index.html) designed to store data on functional interactions between proteins. Beyond cataloguing binary complexes, MINT was conceived to store other types of functional interactions, including enzymatic modifications of one of the partners. Release 1.0 of MINT focuses on experimentally verified protein-protein interactions. Both direct and indirect relationships are considered. Furthermore, MINT aims at being exhaustive in the description of the interaction and, whenever available, information about kinetic and binding constants and about the domains participating in the interaction is included in the entry. MINT consists of entries extracted from the scientific literature by expert curators assisted by 'MINT Assistant', a software that targets abstracts containing interaction information and presents them to the curator in a user-friendly format. The interaction data can be easily extracted and viewed graphically through 'MINT Viewer'. Presently MINT contains 4568 interactions, 782 of which are indirect or genetic interactions.

  17. Molecular assembly of superquenchers in signaling molecular interactions.

    PubMed

    Yang, Chaoyong James; Lin, Hui; Tan, Weihong

    2005-09-21

    We have designed a novel molecular assembly of quencher molecules to form superquenchers with excellent quenching efficiency. The superquencher can be engineered as desired by assembling different types and different numbers of quencher molecules. By labeling a superquencher to a molecular beacon, a 320-fold enhancement of fluorescent signal was achieved, compared to about 14-fold from a molecular beacon prepared with the same monomer quencher. Our molecular assembly approach can effectively improve the sensitivity of a variety of fluorescent assays and can be widely useful for molecular interaction studies.

  18. 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.

  19. 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…

  20. 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.

  1. Structure and Interactions in Neurofilament Networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna

    2005-03-01

    Neurofilaments (NFs) are a major constituent of nerve cell axons that assemble from three subunit proteins of low (NF-L), medium (NF-M), and high molecular weight (NF-H) to form a 10 nm diameter rod with radiating sidearms. The sidearm interactions result in an oriented network of NFs running parallel to the axon. Here, we reassemble NFs in vitro from varying weight ratios of two of the subunit proteins, NF-L and NF-M, purified from bovine spinal cord. We demonstrate the formation of the NF network 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 interfilament spacing varies depending on NF-M sidearm density and we relate this change to sidearm interactions. We show that at a low density of sidearms, repulsive forces dominate creating a lattice spacing that is regulated by the buffer volume. With an increasing sidearm density, the equilibrium interfilament spacing decreases as a result of competing repulsive and attractive forces. Supported by NIH GM-59288, NSF DMR- 0203755, & CTS-0404444.

  2. 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

  3. 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.

  4. 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

  5. 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

  6. Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation.

    PubMed

    Verkhivker, G M

    2016-10-20

    Protein kinases are central to proper functioning of cellular networks and are an integral part of many signal transduction pathways. The family of protein kinases represents by far the largest and most important class of therapeutic targets in oncology. Dimerization-induced activation has emerged as a common mechanism of allosteric regulation in BRAF kinases, which play an important role in growth factor signalling and human diseases. Recent studies have revealed that most of the BRAF inhibitors can induce dimerization and paradoxically stimulate enzyme transactivation by conferring an active conformation in the second monomer of the kinase dimer. The emerging connections between inhibitor binding and BRAF kinase domain dimerization have suggested a molecular basis of the activation mechanism in which BRAF inhibitors may allosterically modulate the stability of the dimerization interface and affect the organization of residue interaction networks in BRAF kinase dimers. In this work, we integrated structural bioinformatics analysis, molecular dynamics and binding free energy simulations with the protein structure network analysis of the BRAF crystal structures to determine dynamic signatures of BRAF conformations in complexes with different types of inhibitors and probe the mechanisms of the inhibitor-induced dimerization and paradoxical activation. The results of this study highlight previously unexplored relationships between types of BRAF inhibitors, inhibitor-induced changes in the residue interaction networks and allosteric modulation of the kinase activity. This study suggests a mechanism by which BRAF inhibitors could promote or interfere with the paradoxical activation of BRAF kinases, which may be useful in informing discovery efforts to minimize the unanticipated adverse biological consequences of these therapeutic agents.

  7. 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

  8. Molecular clock on a neutral network.

    PubMed

    Raval, Alpan

    2007-09-28

    The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating aspects of neutral network topology from empirical measurements of the substitution process.

  9. Alignment-free protein interaction network comparison

    PubMed Central

    Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

    2014-01-01

    Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

  10. Integrative network analysis reveals molecular mechanisms of blood pressure regulation.

    PubMed

    Huan, Tianxiao; Meng, Qingying; Saleh, Mohamed A; Norlander, Allison E; Joehanes, Roby; Zhu, Jun; Chen, Brian H; Zhang, Bin; Johnson, Andrew D; Ying, Saixia; Courchesne, Paul; Raghavachari, Nalini; Wang, Richard; Liu, Poching; O'Donnell, Christopher J; Vasan, Ramachandran; Munson, Peter J; Madhur, Meena S; Harrison, David G; Yang, Xia; Levy, Daniel

    2015-01-01

    Genome-wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP-associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single-gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression-related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP-related genes were uncovered. As proof-of-concept, we validated SH2B3, one of the top key drivers, using Sh2b3(-/-) mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3(-/-) mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

  11. Integrative network analysis reveals molecular mechanisms of blood pressure regulation.

    PubMed

    Huan, Tianxiao; Meng, Qingying; Saleh, Mohamed A; Norlander, Allison E; Joehanes, Roby; Zhu, Jun; Chen, Brian H; Zhang, Bin; Johnson, Andrew D; Ying, Saixia; Courchesne, Paul; Raghavachari, Nalini; Wang, Richard; Liu, Poching; O'Donnell, Christopher J; Vasan, Ramachandran; Munson, Peter J; Madhur, Meena S; Harrison, David G; Yang, Xia; Levy, Daniel

    2015-04-01

    Genome-wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP-associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single-gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression-related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP-related genes were uncovered. As proof-of-concept, we validated SH2B3, one of the top key drivers, using Sh2b3−/− mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3−/− mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

  12. Explorers of the Universe: Interactive Electronic Network

    NASA Technical Reports Server (NTRS)

    Alvarez, Marino C.; Burks, Geoffrey; Busby, Michael R.; Cannon, Tiffani; Sotoohi, Goli; Wade, Montanez

    2000-01-01

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

  13. Predicting genetic interactions with random walks on biological networks

    PubMed Central

    Chipman, Kyle C; Singh, Ambuj K

    2009-01-01

    Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse

  14. 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.

  15. Browsing Multidimensional Molecular Networks with the Generic Network Browser (N-Browse)

    PubMed Central

    Kao, Huey-Ling; Gunsalus, Kristin C.

    2009-01-01

    N-Browse is a graphical network browser for the visualization and navigation of heterogeneous molecular interaction data. N-Browse runs as a Java applet in a Web browser, providing highly dynamic and interactive on-demand access to network data available from a remote server. The N-Browse interface is easy to use and accommodates multiple types of functional linkages with associated information, allowing the exploration of many layers of functional information simultaneously. Although created for applications in biology, N-Browse uses a generic database schema that can be adapted to network representations in any knowledge domain. The N-Browse client-server package is freely available for distribution, providing a convenient way for data producers and providers to distribute and offer interactive visualization of network-based data. PMID:18819079

  16. CABIN: Collective Analysis of Biological Interaction Networks

    SciTech Connect

    Singhal, Mudita; Domico, Kelly O.

    2007-06-01

    The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. However not all prediction tools and databases predict interactions with one hundred percent accuracy. Generation of high confidence interaction networks formulates the first step towards deciphering unknown protein functions, determining protein complexes and inventing drugs. The CABIN: Collective Analysis of Biological Interaction Networks software is an exploratory data analysis tool that enables analysis and integration of interactions evidence obtained from multiple sources, thereby increasing the confidence of computational predictions as well as validating experimental observations. CABIN has been written in JavaTM and is available as a plugin for Cytoscape – an open source network visualization tool.

  17. Integration of molecular network data reconstructs Gene Ontology

    PubMed Central

    Gligorijević, Vladimir; Janjić, Vuk; Pržulj, Nataša

    2014-01-01

    Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. Availability and implementation: Supplementary Tables of new GO

  18. Interaction network among functional drug groups

    PubMed Central

    2013-01-01

    Background More attention has been being paid to combinatorial effects of drugs to treat complex diseases or to avoid adverse combinations of drug cocktail. Although drug interaction information has been increasingly accumulated, a novel approach like network-based method is needed to analyse that information systematically and intuitively Results Beyond focussing on drug-drug interactions, we examined interactions between functional drug groups. In this work, functional drug groups were defined based on the Anatomical Therapeutic Chemical (ATC) Classification System. We defined criteria whether two functional drug groups are related. Then we constructed the interaction network of drug groups. The resulting network provides intuitive interpretations. We further constructed another network based on interaction sharing ratio of the first network. Subsequent analysis of the networks showed that some features of drugs can be well described by this kind of interaction even for the case of structurally dissimilar drugs. Conclusion Our networks in this work provide intuitive insights into interactions among drug groups rather than those among single drugs. In addition, information on these interactions can be used as a useful source to describe mechanisms and features of drugs. PMID:24555875

  19. Functional features and protein network of human sperm-egg interaction.

    PubMed

    Sabetian, Soudabeh; Shamsir, Mohd Shahir; Abu Naser, Mohammed

    2014-12-01

    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new

  20. Functional features and protein network of human sperm-egg interaction.

    PubMed

    Sabetian, Soudabeh; Shamsir, Mohd Shahir; Abu Naser, Mohammed

    2014-12-01

    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new

  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.

  2. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  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. 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.

  6. 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.

  7. 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.

  8. 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

  9. Decoding molecular interactions in microbial communities.

    PubMed

    Abreu, Nicole A; Taga, Michiko E

    2016-09-01

    Microbial communities govern numerous fundamental processes on earth. Discovering and tracking molecular interactions among microbes is critical for understanding how single species and complex communities impact their associated host or natural environment. While recent technological developments in DNA sequencing and functional imaging have led to new and deeper levels of understanding, we are limited now by our inability to predict and interpret the intricate relationships and interspecies dependencies within these communities. In this review, we highlight the multifaceted approaches investigators have taken within their areas of research to decode interspecies molecular interactions that occur between microbes. Understanding these principles can give us greater insight into ecological interactions in natural environments and within synthetic consortia. PMID:27417261

  10. 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;…

  11. Molecular interactions of quinidine with phospholipid bilayers.

    PubMed

    Suwalsky, M; Villena, F; Bagnara, M; Sotomayor, C P

    1995-01-01

    Quinidine (QUIN) is one of the most important and efficient antiarrhythmic drugs (AAD). It belongs to class I, which are the drugs that exert their action at the level of the sodium channels in the membrane of the myocard. Several hypotheses support the idea that the molecular mechanism of action of the AAD is via nonspecific interactions with phospholipids sited in the neighborhood of the channels. In order to probe the validity of these hypotheses, QUIN was made to interact with the phospholipids dimyristoylphosphadidylcholine (DMPC) and dimyristoylphosphatidylethanolamine (DMPE). These interactions were performed in a hydrophobic and a hydrophilic medium under a wide range of molar ratios. The resulting products were analyzed by X-ray diffraction. QUIN solutions were also made to interact with DMPC liposomes, which were studied by fluorescent spectroscopy. Finally, human erythrocytes which were incubated with QUIN solutions were observed by scanning electron microscopy. The results of these experiments proved that QUIN indeed interacted with phospholipid bilayers. PMID:7546041

  12. New perspectives in tracing vector-borne interaction networks.

    PubMed

    Gómez-Díaz, Elena; Figuerola, Jordi

    2010-10-01

    Disentangling trophic interaction networks in vector-borne systems has important implications in epidemiological and evolutionary studies. Molecular methods based on bloodmeal typing in vectors have been increasingly used to identify hosts. Although most molecular approaches benefit from good specificity and sensitivity, their temporal resolution is limited by the often rapid digestion of blood, and mixed bloodmeals still remain a challenge for bloodmeal identification in multi-host vector systems. Stable isotope analyses represent a novel complementary tool that can overcome some of these problems. The utility of these methods using examples from different vector-borne systems are discussed and the extents to which they are complementary and versatile are highlighted. There are excellent opportunities for progress in the study of vector-borne transmission networks resulting from the integration of both molecular and stable isotope approaches.

  13. Ab Initio Study of Molecular Interactions in Cellulose Iα

    SciTech Connect

    Devarajan, Ajitha; Markutsya, Serjiy; Lamm, Monica H.; Cheng, Xiaolin; Smith, Jeremy C.; Baluyut, John Y.; Kholod, Yana; Gordon, Mark S.; Windus, Theresa L.

    2013-08-12

    Biomass recalcitrance, the resistance of cellulosic biomass to degradation, is due in part to the stability of the hydrogen bond network and stacking forces between the polysaccharide chains in cellulose microfibers. The fragment molecular orbital (FMO) method at the correlated Møller–Plesset second order perturbation level of theory was used on a model of the crystalline cellulose Iα core with a total of 144 glucose units. These computations show that the intersheet chain interactions are stronger than the intrasheet chain interactions for the crystalline structure, while they are more similar to each other for a relaxed structure. An FMO chain pair interaction energy decomposition analysis for both the crystal and relaxed structures reveals an intricate interplay between electrostatic, dispersion, charge transfer, and exchange repulsion effects. The role of the primary alcohol groups in stabilizing the interchain hydrogen bond network in the inner sheet of the crystal and relaxed structures of cellulose Iα, where edge effects are absent, was analyzed. The maximum attractive intrasheet interaction is observed for the GT-TG residue pair with one intrasheet hydrogen bond, suggesting that the relative orientation of the residues is as important as the hydrogen bond network in strengthening the interaction between the residues.

  14. Pairwise alignment of protein interaction networks.

    PubMed

    Koyutürk, Mehmet; Kim, Yohan; Topkara, Umut; Subramaniam, Shankar; Szpankowski, Wojciech; Grama, Ananth

    2006-03-01

    With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational

  15. Systematic mapping of genetic interaction networks.

    PubMed

    Dixon, Scott J; Costanzo, Michael; Baryshnikova, Anastasia; Andrews, Brenda; Boone, Charles

    2009-01-01

    Genetic interactions influencing a phenotype of interest can be identified systematically using libraries of genetic tools that perturb biological systems in a defined manner. Systematic screens conducted in the yeast Saccharomyces cerevisiae have identified thousands of genetic interactions and provided insight into the global structure of biological networks. Techniques enabling systematic genetic interaction mapping have been extended to other single-celled organisms, the bacteria Escherichia coli and the yeast Schizosaccharomyces pombe, opening the way to comparative investigations of interaction networks. Genetic interaction screens in Caenorhabditis elegans, Drosophila melanogaster, and mammalian models are helping to improve our understanding of metazoan-specific signaling pathways. Together, our emerging knowledge of the genetic wiring diagrams of eukaryotic and prokaryotic cells is providing a new understanding of the relationship between genotype and phenotype.

  16. Random interactions in higher order neural networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre; Venkatesh, Santosh S.

    1993-01-01

    Recurrent networks of polynomial threshold elements with random symmetric interactions are studied. Precise asymptotic estimates are derived for the expected number of fixed points as a function of the margin of stability. In particular, it is shown that there is a critical range of margins of stability (depending on the degree of polynomial interaction) such that the expected number of fixed points with margins below the critical range grows exponentially with the number of nodes in the network, while the expected number of fixed points with margins above the critical range decreases exponentially with the number of nodes in the network. The random energy model is also briefly examined and links with higher order neural networks and higher order spin glass models made explicit.

  17. 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).

  18. Detecting Allosteric Networks Using Molecular Dynamics Simulation.

    PubMed

    Bowerman, S; Wereszczynski, J

    2016-01-01

    Allosteric networks allow enzymes to transmit information and regulate their catalytic activities over vast distances. In principle, molecular dynamics (MD) simulations can be used to reveal the mechanisms that underlie this phenomenon; in practice, it can be difficult to discern allosteric signals from MD trajectories. Here, we describe how MD simulations can be analyzed to reveal correlated motions and allosteric networks, and provide an example of their use on the coagulation enzyme thrombin. Methods are discussed for calculating residue-pair correlations from atomic fluctuations and mutual information, which can be combined with contact information to identify allosteric networks and to dynamically cluster a system into highly correlated communities. In the case of thrombin, these methods show that binding of the antagonist hirugen significantly alters the enzyme's correlation landscape through a series of pathways between Exosite I and the catalytic core. Results suggest that hirugen binding curtails dynamic diversity and enforces stricter venues of influence, thus reducing the accessibility of thrombin to other molecules. PMID:27497176

  19. 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

  20. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

  1. 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

  2. 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

  3. Network Compression as a Quality Measure for Protein Interaction Networks

    PubMed Central

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  4. Network archaeology: uncovering ancient networks from present-day interactions.

    PubMed

    Navlakha, Saket; Kingsford, Carl

    2011-04-01

    What proteins interacted in a long-extinct ancestor of yeast? How have different members of a protein complex assembled together over time? Our ability to answer such questions has been limited by the unavailability of ancestral protein-protein interaction (PPI) networks. To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. Our likelihood-based method finds a probable previous state of the graph by applying an assumed growth model backwards in time. This approach retains node identities so that the history of individual nodes can be tracked. Using this methodology, we estimate protein ages in the yeast PPI network that are in good agreement with sequence-based estimates of age and with structural features of protein complexes. Further, by comparing the quality of the inferred histories for several different growth models (duplication-mutation with complementarity, forest fire, and preferential attachment), we provide additional evidence that a duplication-based model captures many features of PPI network growth better than models designed to mimic social network growth. From the reconstructed history, we model the arrival time of extant and ancestral interactions and predict that complexes have significantly re-wired over time and that new edges tend to form within existing complexes. We also hypothesize a distribution of per-protein duplication rates, track the change of the network's clustering coefficient, and predict paralogous relationships between extant proteins that are likely to be complementary to the relationships inferred using sequence alone. Finally, we infer plausible parameters for the model, thereby predicting the relative probability of various evolutionary events. The success of these algorithms indicates that parts of the history of the yeast PPI are encoded in its present-day form. PMID:21533211

  5. 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

  6. 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

  7. 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

  8. Interaction Energy Based Protein Structure Networks

    PubMed Central

    Vijayabaskar, M.S.; Vishveshwara, Saraswathi

    2010-01-01

    The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis. PMID:21112295

  9. 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.

  10. Modeling of light-matter interactions with neural networks

    SciTech Connect

    Selle, Reimer; Vogt, Gerhard; Brixner, Tobias; Gerber, Gustav; Metzler, Richard; Kinzel, Wolfgang

    2007-08-15

    We show that a neural network (NN) can be used for automated generation of computer models of light-matter interaction. Nonlinear input-output maps are created for phase-shaped femtosecond laser pulses in the exemplary cases of second-harmonic generation and molecular fluorescence yield. Simulations and experiments demonstrate that the NN has the capability of generalizing and extrapolating beyond initial training data, by predicting the response of the investigated systems for arbitrary laser pulse shapes. Applications are envisioned in the area of quantum control, specifically for the interpolation and extrapolation of control maps and possibly as a tool for investigating control mechanisms. In a wider scope, neural networks might generally provide effective computer models for light-matter interactions for cases where ab initio calculations are intractable.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. Thermodynamic molecular switch in macromolecular interactions.

    PubMed

    Chun, P W

    2000-01-01

    It is known that most living systems can live and operate optimally only at a sharply defined temperature, or over a limited temperature range, at best, which implies that many basic biochemical interactions exhibit a well-defined Gibbs free energy minimum as a function of temperature. The Gibbs free energy change, deltaG(o) (T), for biological systems shows a complicated behavior, in which deltaG(o)(T) changes from positive to negative, then reaches a negative value of maximum magnitude (favorable), and finally becomes positive as temperature increases. The critical factor in this complicated thermodynamic behavior is a temperature-dependent heat capacity change (deltaCp(o)(T) of reaction, which is positive at low temperature, but switches to a negative value at a temperature well below the ambient range. Thus, the thermodynamic molecular switch determines the behavior patterns of the Gibbs free energy change, and hence a change in the equilibrium constant, Keq, and/or spontaneity. The subsequent, mathematically predictable changes in deltaH(o)(T), deltaS(o)(T), deltaW(o)(T), and deltaG(o)(T) give rise to the classically observed behavior patterns in biological reactivity, as demonstrated in three interacting protein systems: the acid dimerization reaction of alpha-chymotrypsin at low pH, interaction of chromogranin A with the intraluminal loop peptide of the inositol 1,4,5-triphosphate receptor at pH 5.5, and the binding of L-arabinose and D-galactose to the L-arabinose binding protein of Escherichia coli. In cases of protein unfolding of four mutants of phage T4 lysozyme, no thermodynamic molecular switch is observed.

  16. Detecting phylogenetic signal in mutualistic interaction networks using a Markov process model

    PubMed Central

    Minoarivelo, H. O.; Hui, C.; Terblanche, J. S.; Pond, S. L. Kosakovsky; Scheffler, K.

    2014-01-01

    Ecological interaction networks, such as those describing the mutualistic interactions between plants and their pollinators or between plants and their frugivores, exhibit non-random structural properties that cannot be explained by simple models of network formation. One factor affecting the formation and eventual structure of such a network is its evolutionary history. We argue that this, in many cases, is closely linked to the evolutionary histories of the species involved in the interactions. Indeed, empirical studies of interaction networks along with the phylogenies of the interacting species have demonstrated significant associations between phylogeny and network structure. To date, however, no generative model explaining the way in which the evolution of individual species affects the evolution of interaction networks has been proposed. We present a model describing the evolution of pairwise interactions as a branching Markov process, drawing on phylogenetic models of molecular evolution. Using knowledge of the phylogenies of the interacting species, our model yielded a significantly better fit to 21% of a set of plant – pollinator and plant – frugivore mutualistic networks. This highlights the importance, in a substantial minority of cases, of inheritance of interaction patterns without excluding the potential role of ecological novelties in forming the current network architecture. We suggest that our model can be used as a null model for controlling evolutionary signals when evaluating the role of other factors in shaping the emergence of ecological networks. PMID:25294947

  17. Genetic interaction networks: better understand to better predict

    PubMed Central

    Boucher, Benjamin; Jenna, Sarah

    2013-01-01

    A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

  18. 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

  19. Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers.

    PubMed

    Dix, A; Vlaic, S; Guthke, R; Linde, J

    2016-07-01

    In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

  20. 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

  1. 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

  2. Duplication and Divergence Effect on Network Motifs in Undirected Bio-Molecular Networks.

    PubMed

    Pei Wang; Jinhu Lu; Xinghuo Yu; Zengrong Liu

    2015-06-01

    Duplication and divergence are two basic evolutionary mechanisms of bio-molecular networks. Real-world bio-molecular networks and their statistical characteristics can be well mimicked by artificial algorithms based on the two mechanisms. Bio-molecular networks consist of network motifs, which act as building blocks of large-scale networks. A fundamental question is how network motifs are evolved from long time evolution and natural selection. By considering the effect of various duplication and divergence strategies, we find that the underlying duplication scheme of the real-world undirected bio-molecular networks would rather follow the anti-preference strategy than the random one. The anti-preference duplication mechanism and the dimerization processes can lead to the formation of various motifs, and robustly conserve proper quantities of motifs in the artificial networks as that in the real-world ones. Furthermore, the anti-preference mechanism and edge deletion divergence can robustly preserve the sparsity of the networks. The investigations reveal the possible evolutionary mechanisms of network motifs in real-world bio-molecular networks, and have potential implications in the design, synthesis and reengineering of biological networks for biomedical purpose. PMID:25203993

  3. 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…

  4. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    PubMed

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  5. Advanced fault diagnosis methods in molecular networks.

    PubMed

    Habibi, Iman; Emamian, Effat S; Abdi, Ali

    2014-01-01

    Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.

  6. Optically driven quantum networks: Applications in molecular electronics

    NASA Astrophysics Data System (ADS)

    Körner, H.; Mahler, G.

    1993-07-01

    Progress in nanostructuring tends to provide us with synthetic structures for which, for example, energy or time scales can be adjusted in such a way that quantum systems with unusual physical properties emerge. The challenge of molecular electronics is to make these properties represent computer functions. We investigate a quantum network model consisting of a modular array of localized few-level subsystems. When driven optically, a diagonal (energy renormalizing) interaction among these subsystems is shown to lead to a complex stochastic dynamics, which may be interpreted as a highly parallel Monte-Carlo-type simulation ``programmed'' by the external light field. A first application is demonstrated in terms of a two-dimensional kinetic Ising model with J(Rn-Rm)~||Rn-Rm||-3. In another application the nonlocal nonlinear optical properties are exploited in specific pump and probe scenarios: Under certain conditions simple image processing tasks are performed. A possible realization of such quantum network models by an array of charge-transfer quantum dots is discussed.

  7. 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.

  8. 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.

  9. Transferring network topological knowledge for predicting protein-protein interactions.

    PubMed

    Xu, Qian; Xiang, Evan Wei; Yang, Qiang

    2011-10-01

    Protein-protein interactions (PPIs) play an important role in cellular processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, the existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, we introduce the well-known collective matrix factorization technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish a correspondence between a source network and a target network via network-wide similarities. We test this method on two real PPI networks, Helicobacter pylori (as a target network) and human (as a source network). Our experimental results show that our method can achieve higher performance as compared with some baseline methods. PMID:21770035

  10. KEGG for representation and analysis of molecular networks involving diseases and drugs.

    PubMed

    Kanehisa, Minoru; Goto, Susumu; Furumichi, Miho; Tanabe, Mao; Hirakawa, Mika

    2010-01-01

    Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.

  11. Formation mechanism for a hybrid supramolecular network involving cooperative interactions.

    PubMed

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

    2012-04-27

    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. PMID:22680886

  12. Identification and evolution of structurally dominant nodes in protein-protein interaction networks.

    PubMed

    Wang, Pei; Yu, Xinghuo; Lü, Jinhu

    2014-02-01

    It is well known that protein-protein interaction (PPI) networks are typical evolving complex networks. Identification of important nodes has been an emerging popular topic in complex networks. Many indexes have been proposed to measure the importance of nodes in complex networks, such as degree, closeness, betweenness, k-shell, clustering coefficient, semi-local centrality, eigenvector centrality. Based on multivariate statistical analysis, through integrating the above indexes and further considering the appearances of nodes in network motifs, this paper aims at developing a new measure to characterize the structurally dominant proteins (SDP) in PPI networks. Moreover, we will further investigate the evolution of the defined dominant nodes in temporal evolving real-world and artificial PPI networks. Our results indicate that the constructed artificial networks have some similar statistical properties as those of the real-world evolving networks. In this case, the artificial PPI networks can be used to further investigate the above evolution characteristics of the real-world evolving networks. Simulation results reveal that SDP in the yeast PPI networks are evolutionary conserved, however, the undominant nodes evolve rapidly. Furthermore, PPI networks are very robust against random mutations, while fragile yet with certain robustness to targeted mutations on SDP. Our investigations shed some light on the future applications of the evolving characteristics of bio-molecular networks, such as reengineering of particular networks for technological, synthetic or pharmacological purposes. PMID:24681922

  13. Entropy Bounds for Hierarchical Molecular Networks

    PubMed Central

    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

  14. Discrete network models of interacting nephrons

    NASA Astrophysics Data System (ADS)

    Moss, Rob; Kazmierczak, Ed; Kirley, Michael; Harris, Peter

    2009-11-01

    The kidney is one of the major organs involved in whole-body homeostasis, and exhibits many of the properties of a complex system. The functional unit of the kidney is the nephron, a complex, segmented tube into which blood plasma is filtered and its composition adjusted. Although the behaviour of individual nephrons can fluctuate widely and even chaotically, the behaviour of the kidney remains stable. In this paper, we investigate how the filtration rate of a multi-nephron system is affected by interactions between nephrons. We introduce a discrete-time multi-nephron network model. The tubular mechanisms that have the greatest effect on filtration rate are the transport of sodium and water, consequently our model attempts to capture these mechanisms. Multi-nephron systems also incorporate two competing coupling mechanisms-vascular and hemodynamic-that enforce in-phase and anti-phase synchronisations respectively. Using a two-nephron model, we demonstrate how changing the strength of the hemodynamic coupling mechanism and changing the arterial blood pressure have equivalent effects on the system. The same two-nephron system is then used to demonstrate the interactions that arise between the two coupling mechanisms. We conclude by arguing that our approach is scalable to large numbers of nephrons, based on the performance characteristics of the model.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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.

  3. Theoretical Analysis of Dynamic Processes for Interacting Molecular Motors

    PubMed Central

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

    2015-01-01

    Biological transport is supported by 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 analyzing a new class of totally asymmetric exclusion processes where interactions are accounted for in a thermodynamically consistent fashion. It allows us to connect explicitly microscopic features of motor proteins with their collective dynamic properties. Theoretical analysis that combines various mean-field calculations and computer simulations suggests that dynamic properties of molecular motors strongly depend on interactions, and 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 motors transport is more sensitive to attractive interactions. Applications of these results for kinesin motor proteins are discussed. PMID:25688287

  4. 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.

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

    PubMed

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

    2016-07-22

    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.

  6. 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

  7. 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

  8. Extended master equation models for molecular communication networks.

    PubMed

    Chou, Chun Tung

    2013-06-01

    We consider molecular communication networks consisting of transmitters and receivers distributed in a fluidic medium. In such networks, a transmitter sends one or more signaling molecules, which are diffused over the medium, to the receiver to realize the communication. In order to be able to engineer synthetic molecular communication networks, mathematical models for these networks are required. This paper proposes a new stochastic model for molecular communication networks called reaction-diffusion master equation with exogenous input (RDMEX). The key idea behind RDMEX is to model the transmitters as time series of signaling molecule counts, while diffusion in the medium and chemical reactions at the receivers are modeled as Markov processes using master equation. An advantage of RDMEX is that it can readily be used to model molecular communication networks with multiple transmitters and receivers. For the case where the reaction kinetics at the receivers is linear, we show how RDMEX can be used to determine the mean and covariance of the receiver output signals, and derive closed-form expressions for the mean receiver output signal of the RDMEX model. These closed-form expressions reveal that the output signal of a receiver can be affected by the presence of other receivers. Numerical examples are provided to demonstrate the properties of the model.

  9. A systematic molecular circuit design method for gene networks under biochemical time delays and molecular noises

    PubMed Central

    Chen, Bor-Sen; Chang, Yu-Te

    2008-01-01

    Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S) fuzzy time-delay model and linear matrix inequalities (LMIs) technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises. PMID:19038029

  10. 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

  11. 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…

  12. 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.

  13. 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

  14. 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

  15. Understanding polycaroboxylate interactions with counterions: A molecular modeling approach

    SciTech Connect

    Fitzwater, S.; Freeman, M.B.

    1993-12-31

    Low molecular weight polycarboyxlates, such as poly(acrylic acid), have utility as dispersants in a variety of commercial applications including home laundry detergents, mineral processing and water treatment. In general, counterions (Ca, Mg, Fe, etc.) are unavoidable in these applications and often dictate the polymer composition and molecular weight necessary for successful performance. The authors have been investigating the interaction of polycarboxylates with counterions in order to better understand how that interaction impacts on the dispersant properties of a polymer. Using computer modeling, it can be seen how molecular geometry, molecular dynamics, and the shape/polarity of the molecular surface are affected by counterion binding and polymer composition. The authors can then combine information from the modeling with experimental information and literature concepts to provide a direction toward the synthesis of improved low molecular weight polycarboxylate dispersants.

  16. 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.

  17. 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.

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

    PubMed

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

    2016-08-04

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

  19. The architecture of functional interaction networks in the retina.

    PubMed

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2011-02-23

    Sensory information is represented in the brain by the joint activity of large groups of neurons. Recent studies have shown that, although the number of possible activity patterns and underlying interactions is exponentially large, pairwise-based models give a surprisingly accurate description of neural population activity patterns. We explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli. We found that we can further simplify these pairwise models by neglecting weak interaction terms or by relying on a small set of interaction strengths. Comparing network interactions under different visual stimuli, we show the existence of local network motifs in the interaction map of the retina. Our results demonstrate that the underlying interaction map of the retina is sparse and dominated by local overlapping interaction modules.

  20. 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

  1. Characteristics of Quasi-Molecular State Interaction

    SciTech Connect

    Devdariani, A.; Dalimier, E.; Kereselidze, T.; Noselidze, I.; Rebentrost, F.; Sauvan, P.

    2008-10-22

    The quasi-molecular dipole transition moments have been considered analytically within the framework of the two-state approximation with particular emphasis on their roots (zeros) on spectral manifestations of the roots in the adiabatic diabatic limits. The interrelation between the spectral features the non-adiabatic transitions found in [1] has been demonstrated for excited state charge exchange Al{sup +12}(n = 4)+C{sup +6}{yields}Al{sup +13}+C{sup +5}(n = 2)

  2. 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.

  3. 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

  4. Tcof1-Related Molecular Networks in Treacher Collins Syndrome.

    PubMed

    Dai, Jiewen; Si, Jiawen; Wang, Minjiao; Huang, Li; Fang, Bing; Shi, Jun; Wang, Xudong; Shen, Guofang

    2016-09-01

    Treacher Collins syndrome (TCS) is a rare, autosomal-dominant disorder characterized by craniofacial deformities, and is primarily caused by mutations in the Tcof1 gene. This article was aimed to perform a comprehensive literature review and systematic bioinformatic analysis of Tcof1-related molecular networks in TCS. First, the up- and down-regulated genes in Tcof1 heterozygous haploinsufficient mutant mice embryos and Tcof1 knockdown and Tcof1 over-expressed neuroblastoma N1E-115 cells were obtained from the Gene Expression Omnibus database. The GeneDecks database was used to calculate the 500 genes most closely related to Tcof1. Then, the relationships between 4 gene sets (a predicted set and sets comparing the wildtype with the 3 Gene Expression Omnibus datasets) were analyzed using the DAVID, GeneMANIA and STRING databases. The analysis results showed that the Tcof1-related genes were enriched in various biological processes, including cell proliferation, apoptosis, cell cycle, differentiation, and migration. They were also enriched in several signaling pathways, such as the ribosome, p53, cell cycle, and WNT signaling pathways. Additionally, these genes clearly had direct or indirect interactions with Tcof1 and between each other. Literature review and bioinformatic analysis finds imply that special attention should be given to these pathways, as they may offer target points for TCS therapies.

  5. The mechanism of selective molecular capture in carbon nanotube networks.

    PubMed

    Wan, Yu; Guan, Jun; Yang, Xudong; Zheng, Quanshui; Xu, Zhiping

    2014-07-28

    Recently, air pollution issues have drawn significant attention to the development of efficient air filters, and one of the most promising materials for this purpose is nanofibers. We explore here the mechanism of selective molecular capture of volatile organic compounds in carbon nanotube networks by performing atomistic simulations. The results are discussed with respect to the two key parameters that define the performance of nanofiltration, i.e. the capture efficiency and flow resistance, which demonstrate the advantages of carbon nanotube networks with high surface-to-volume ratio and atomistically smooth surfaces. We also reveal the important roles of interfacial adhesion and diffusion that govern selective gas transport through the network.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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,…

  11. Genetic and Molecular Network Analysis of Behavior

    PubMed Central

    Williams, Robert W.; Mulligan, Megan K.

    2014-01-01

    This chapter provides an introduction into the genetic control and analysis of behavioral variation using powerful online resources. We introduce you to the new field of systems genetics using "case studies" drawn from the world of behavioral genetics that exploit populations of genetically diverse lines of mice. These lines differ very widely in patterns of gene and protein expression in the brain and in patterns of behavior. In this chapter we address the following set of related questions: (1) Can we combine massive genomic data sets with large aggregates of precise quantitative data on behavior? (2) Can we map causal relations between gene variants and behavioral differences? (3) Can we simultaneously use these highly coherent data sets to understand more about the underlying molecular and cellular basis of behavior? PMID:23195314

  12. 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

  13. Plasmonic interactions: from molecular plasmonics and Fano resonances to ferroplasmons.

    PubMed

    Passarelli, Nicolás; Pérez, Luis A; Coronado, Eduardo A

    2014-10-28

    Plasmon interactions are a subject of great interest from both the technological as well as the fundamental points of view. In this Perspective, we outline the great variety of physical phenomena that are produced by the interactions of localized surface plasmon resonance with molecular excitons; with other plasmonic nanostructures, particularly the Fano effect; and with nonplasmonic nanoparticles, such as the just-reported interaction with ferromagnetic nanoparticles. The theoretical as well as experimental challenges remaining to be elucidated are discussed.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. BIMOLECULAR FLUORESCENCE COMPLEMENTATION: VISUALIZATION OF MOLECULAR INTERACTIONS IN LIVING CELLS

    PubMed Central

    Kerppola, Tom K.

    2009-01-01

    A variety of experimental methods have been developed for the analysis of protein interactions. The majority of these methods either require disruption of the cells to detect molecular interactions or rely on indirect detection of the protein interaction. The bimolecular fluorescence complementation (BiFC) assay provides a direct approach for the visualization of molecular interactions in living cells and organisms. The BiFC approach is based on the facilitated association between two fragments of a fluorescent protein when the fragments are brought together by an interaction between proteins fused to the fragments. The BiFC approach has been used for visualization of interactions among a variety of structurally divers interaction partners in many different cell types and. It enables detection of transient complexes as well as complexes formed by a subpopulation of the interaction partners. It is essential to include negative controls in each experiment in which the interface between the interaction partners has been mutated or deleted. The BiFC assay has been adapted for simultaneous visualization of multiple protein complexes in the same cell and the competition for shared interaction partners. A ubiquitin-mediated fluorescence complementation (UbFC) assay has also been developed for visualization of the covalent modification of proteins by ubiquitin family peptides. These fluorescence complementation assays have a great potential to illuminate a variety of biological interactions in the future. PMID:18155474

  2. 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

  3. Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

    PubMed Central

    Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

    2015-01-01

    It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

  4. Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis.

    PubMed

    Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

    2015-07-28

    It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant-wild-type and 16 matched SNP--wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation.

  5. 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.…

  6. Speeding up biomolecular interactions by molecular sledding

    DOE PAGES

    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

  7. 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.

  8. Mathematically Designing a Local Interaction Algorithm for Decentralized Network Systems

    NASA Astrophysics Data System (ADS)

    Kubo, Takeshi; Hasegawa, Teruyuki; Hasegawa, Toru

    In the near future, decentralized network systems consisting of a huge number of sensor nodes are expected to play an important role. In such a network, each node should control itself by means of a local interaction algorithm. Although such local interaction algorithms improve system reliability, how to design a local interaction algorithm has become an issue. In this paper, we describe a local interaction algorithm in a partial differential equation (or PDE) and propose a new design method whereby a PDE is derived from the solution we desire. The solution is considered as a pattern of nodes' control values over the network each of which is used to control the node's behavior. As a result, nodes collectively provide network functions such as clustering, collision and congestion avoidance. In this paper, we focus on a periodic pattern comprising sinusoidal waves and derive the PDE whose solution exhibits such a pattern by exploiting the Fourier method.

  9. 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

  10. An Interaction Library for the FcεRI Signaling Network.

    PubMed

    Chylek, Lily A; Holowka, David A; Baird, Barbara A; Hlavacek, William S

    2014-01-01

    Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein-protein and protein-lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP3 at the plasma membrane and the soluble second messenger IP3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

  11. Ontology integration to identify protein complex in protein interaction networks

    PubMed Central

    2011-01-01

    Background Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. PMID:22165991

  12. Molecular interactions and crystal packing in nematogen: Computational thermodynamic approach

    NASA Astrophysics Data System (ADS)

    Lakshmi Praveen, P.; Ojha, Durga P.

    2011-10-01

    A computational thermodynamic approach of molecular interactions in a nematogen p-n-alkyl benzoic acid ( nBAC) molecule with an alkyl group butyl (4BAC) has been carried out with respect to translational and orientational motion. The atomic net charge and dipole moment at each atomic center were evaluated using the complete neglect differential overlap (CNDO/2) method. The modified Rayleigh-Schrödinger perturbation theory along with multicentered-multipole expansion method were employed to evaluate long-range intermolecular interactions, while a 6-exp potential function was assumed for short-range interactions. Various possible geometrical arrangements of molecular pairs with regard to different energy components were considered, and the energetically favorable configuration was found to understand the crystal packing picture. Furthermore, these interaction energy values are taken as input to calculate the configurational entropy at room temperature (300 K), nematic-isotropic transition temperature (386 K) and above transition temperature (450 K) during different modes of interactions. An attempt has been made to describe interactions in a nematogen at molecular level, through which one can simplify the system to make the model computationally feasible in understanding the delicate interplay between energy and entropy, that accounts for mesomorphism and there by to analyze the molecular structure of a nematogen.

  13. 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.

  14. Linking Classrooms of the Future through Interactive Telecommunications Network.

    ERIC Educational Resources Information Center

    Cisco, Ponney G.

    This document describes an interactive television (ITV) distance education network designed to service rural schools. Phase one of the network involved the installation of over 14 miles of fiber optic cable linking two high schools, a career center, and the University of Rio Grande; phase two will bring seven high schools in economically depressed…

  15. 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),…

  16. 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.

  17. 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)

  18. Interacting Social Processes on Interconnected Networks

    PubMed Central

    Alvarez-Zuzek, Lucila G.; La Rocca, Cristian E.; Vazquez, Federico; Braunstein, Lidia A.

    2016-01-01

    We propose and study a model for the interplay between two different dynamical processes –one for opinion formation and the other for decision making– on two interconnected networks A and B. The opinion dynamics on network A corresponds to that of the M-model, where the state of each agent can take one of four possible values (S = −2,−1, 1, 2), describing its level of agreement on a given issue. The likelihood to become an extremist (S = ±2) or a moderate (S = ±1) is controlled by a reinforcement parameter r ≥ 0. The decision making dynamics on network B is akin to that of the Abrams-Strogatz model, where agents can be either in favor (S = +1) or against (S = −1) the issue. The probability that an agent changes its state is proportional to the fraction of neighbors that hold the opposite state raised to a power β. Starting from a polarized case scenario in which all agents of network A hold positive orientations while all agents of network B have a negative orientation, we explore the conditions under which one of the dynamics prevails over the other, imposing its initial orientation. We find that, for a given value of β, the two-network system reaches a consensus in the positive state (initial state of network A) when the reinforcement overcomes a crossover value r*(β), while a negative consensus happens for r < r*(β). In the r − β phase space, the system displays a transition at a critical threshold βc, from a coexistence of both orientations for β < βc to a dominance of one orientation for β > βc. We develop an analytical mean-field approach that gives an insight into these regimes and shows that both dynamics are equivalent along the crossover line (r*, β*). PMID:27689698

  19. 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

  20. 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

  1. 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

    2015-12-21

    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.

  2. 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.

  3. Specialization for resistance in wild host-pathogen interaction networks

    PubMed Central

    Barrett, Luke G.; Encinas-Viso, Francisco; Burdon, Jeremy J.; Thrall, Peter H.

    2015-01-01

    Properties encompassed by host-pathogen interaction networks have potential to give valuable insight into the evolution of specialization and coevolutionary dynamics in host-pathogen interactions. However, network approaches have been rarely utilized in previous studies of host and pathogen phenotypic variation. Here we applied quantitative analyses to eight networks derived from spatially and temporally segregated host (Linum marginale) and pathogen (Melampsora lini) populations. First, we found that resistance strategies are highly variable within and among networks, corresponding to a spectrum of specialist and generalist resistance types being maintained within all networks. At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance. Second, we found that all networks were significantly nested. There was little support for the hypothesis that temporal evolutionary dynamics may lead to the development of nestedness in host-pathogen infection networks. Rather, the common patterns observed in terms of nestedness suggests a universal driver (or multiple drivers) that may be independent of spatial and temporal structure. Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks. We conclude that (1) overall patterns of specialization in the networks we studied mirror evolutionary trade-offs with the strength of resistance; (2) that specific network architecture can emerge under different evolutionary scenarios; and (3) network approaches offer great utility as a tool for probing the evolutionary and ecological genetics of host-pathogen interactions. PMID:26442074

  4. Conservation of molecular interactions stabilizing bovine and mouse rhodopsin †

    PubMed Central

    Kawamura, Shiho; Colozo, Alejandro T.; Müller, Daniel J.; Park, Paul S.-H.

    2010-01-01

    Rhodopsin is the light receptor that initiates phototransduction in rod photoreceptor cells. The structure and function of rhodopsin is tightly linked to molecular interactions that stabilize and determine the receptor's functional state. Single-molecule force spectroscopy (SMFS) was used to localize and quantify molecular interactions that structurally stabilize bovine and mouse rhodopsin from native disc membranes of rod photoreceptor cells. The mechanical unfolding of bovine and mouse rhodopsin revealed nine major unfolding intermediates, each intermediate defining a structurally stable segment in the receptor. These stable structural segments had similar localization and occurrence in both bovine and mouse samples. For each structural segment, parameters describing their unfolding energy barrier were determined by dynamic SMFS. No major differences were observed between bovine and mouse rhodopsin thereby implying that the structures of both rhodopsins are largely stabilized by similar molecular interactions. PMID:21038881

  5. Molecular beacons for protein-DNA interaction studies.

    PubMed

    Li, Jun; Cao, Zehui Charles; Tang, Zhiwen; Wang, Kemin; Tan, Weihong

    2008-01-01

    Real-time monitoring of DNA-protein interactions involving molecular beacon (MB) and molecular beacon aptamer (MBA) was discussed in this chapter. MBs are single-stranded oligonucleotide probes with a hairpin structure. MBs have been designed for oligonucleotide recognition and protein-DNA interaction studies. Real-time monitoring of enzymatic reactions, such as cleavage, ligation, and phosphorylation of single-stranded DNA by specific enzyme, has been studied using MBs. Meanwhile, a new generation of molecular probes, MBA, was designed by combining the excellent signal transduction properties of MBs with the specificity of aptamers for protein recognition. Two different aptamers, the one for thrombin and that for platelet-derived growth factor, have been successfully used to construct MBA probes. The interaction between the proteins and the MBA probes was investigated by fluorescence resonance energy transfer, fluorescence anisotropy, and time-resolved fluorescence. This chapter has reviewed our recent progress in this area.

  6. Molecular modeling of protein-glycosaminoglycan interactions.

    PubMed

    Cardin, A D; Weintraub, H J

    1989-01-01

    Forty-nine regions in 21 proteins were identified as potential heparin-binding sites based on the sequence organizations of their basic and nonbasic residues. Twelve known heparin-binding sequences in vitronectin, apolipoproteins E and B-100, and platelet factor 4 were used to formulate two search strings for identifying potential heparin-binding regions in other proteins. Consensus sequences for glycosaminoglycan recognition were determined as [-X-B-B-X-B-X-] and [-X-B-B-B-X-X-B-X-] where B is the probability of a basic residue and X is a hydropathic residue. Predictions were then made as to the heparin-binding domains in endothelial cell growth factor, purpurin, and antithrombin-III. Many of the natural sequences conforming to these consensus motifs show prominent amphipathic periodicities having both alpha-helical and beta-strand conformations as determined by predictive algorithms and circular dichroism studies. The heparin-binding domain of vitronectin was modeled and formed a hydrophilic pocket that wrapped around and folded over a heparin octasaccharide, yielding a complementary structure. We suggest that these consensus sequence elements form potential nucleation sites for the recognition of polyanions in proteins and may provide a useful guide in identifying heparin-binding regions in other proteins. The possible relevance of protein-glycosaminoglycans interactions in atherosclerosis is discussed. PMID:2463827

  7. 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

  8. 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.

  9. 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.

  10. 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

  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. Adhesion and friction of PDMS networks: molecular weight effects.

    PubMed

    Galliano, A; Bistac, S; Schultz, J

    2003-09-15

    The objective of this work is to find relations between adherence and friction behaviors of elastomer networks. The chosen approach is based on the parallel study of the initial molecular weight (i.e., the degree of cross-linking) dependence of both adherence and friction. The polymers used are cross-linked polydimethylsiloxane (PDMS) and the substrate is a smooth glass plate. The experimental procedure uses both friction (pin on disk tribometer) and adhesion (tack test) measurements, associated with surface analysis and mechanical and rheological characterizations. Tack results show that high molecular weight PDMS exhibits the greater adherence energy. This can be explained by the role of both chain length and free and pendant chains: more numerous and longer free chains favor the substrate wetting (at a molecular scale) and increases the energy dissipation during separation (extraction and reptation mechanisms). However, friction results indicate a higher friction resistance for low molecular weight PDMS. This result could be quite surprising. An explanation based on interfacial sliding properties of free and pendant chains can be proposed. Elsewhere, for the lower molecular weight polymer, elastic contact present during friction is able to act as a forced wetting, constraining the network and consequently leading to a greater energy dissipation.

  13. 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

  14. 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.

  15. 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.

  16. Origin of molecular conformational stability: perspectives from molecular orbital interactions and density functional reactivity theory.

    PubMed

    Liu, Shubin; Schauer, Cynthia K

    2015-02-01

    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.

  17. 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.

  18. 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

  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. Steps towards a repertoire of comprehensive maps of human protein interaction networks: the Human Proteotheque Initiative (HuPI).

    PubMed

    Coulombe, Benoit; Blanchette, Mathieu; Jeronimo, Célia

    2008-04-01

    Defining human protein interaction networks has become essential to develop an overall, systems-based understanding of the molecular events that sustain cell growth in normal and disease conditions. To characterize protein interaction networks from human cells, we have undertaken the development of a systematic, unbiased technology pipeline that couples experimental and computational approaches. This discovery engine is central to the Human Proteotheque Initiative (HuPI), a multidisciplinary project aimed at building a repertoire of comprehensive maps of human protein interaction networks, the Human Proteotheque. The information contained in the Proteotheque is made publicly available through an interactive web site that can be consulted to visualize some of the fundamental molecular connections formed in human cells and to determine putative functions of previously uncharacterized proteins based on guilt by association. The process governing the evolution of HuPI towards becoming a repository of accurate and complete protein interaction maps is described.

  1. 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.

  2. 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).

  3. Use of an Interactive Telecommunications Network to Deliver Inservice Education.

    ERIC Educational Resources Information Center

    Slaton, Deborah Bott; Lacefield, Warren E.

    1991-01-01

    A total of 155 educators in rural Kentucky participated in 4 inservice education sessions on learning disabilities and adaptive teaching, delivered via an interactive telecommunications network. Participants' reactions indicated that interactive television was an acceptable format for delivery of inservice education. (Author/JDD)

  4. 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…

  5. 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.

  6. 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

  7. Quantifying the impact of molecular defects on polymer network elasticity.

    PubMed

    Zhong, Mingjiang; Wang, Rui; Kawamoto, Ken; Olsen, Bradley D; Johnson, Jeremiah A

    2016-09-16

    Elasticity, one of the most important properties of a soft material, is difficult to quantify in polymer networks because of the presence of topological molecular defects in these materials. Furthermore, the impact of these defects on bulk elasticity is unknown. We used rheology, disassembly spectrometry, and simulations to measure the shear elastic modulus and count the numbers of topological "loop" defects of various order in a series of polymer hydrogels, and then used these data to evaluate the classical phantom and affine network theories of elasticity. The results led to a real elastic network theory (RENT) that describes how loop defects affect bulk elasticity. Given knowledge of the loop fractions, RENT provides predictions of the shear elastic modulus that are consistent with experimental observations. PMID:27634530

  8. Molecular codes in biological and chemical reaction networks.

    PubMed

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

    Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

  9. 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.

  10. 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

  11. Spectral tuning of organic nanocolloids by controlled molecular interactions.

    PubMed

    Spillmann, Christopher M; Naciri, Jawad; Anderson, George P; Chen, Mu-San; Ratna, Banahalli R

    2009-10-27

    The controlled self-assembly of molecules and interactions between them remain a challenge in creating tunable and functional organic nanostructures. One class of molecular systems that has proven useful for incorporating tunable functionality at different length scales is liquid crystals (LCs) due to its ability to inherently self-organize. Here we present a novel approach to utilize the self-assembly of polymerizable liquid crystals to control the molecular aggregation of stable fluorescent chromophores and create a unique class of organic fluorescent nanocolloids. By adjusting the ratio between the dye and LC molecules inside the nanocolloids, we demonstrate the ability to control the molecular interactions and tune the fluorescent emission spectra of nanocolloid populations under single wavelength excitation. The single absorption spectrum and multiple emission spectra are highly desirable and reminiscent of the spectroscopic signature of quantum dots. These novel fluorescent nanocolloids have broad potential applications in fluorescent imaging and biological labeling.

  12. 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

  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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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…

  19. Strategy selection in evolutionary game dynamics on group interaction networks.

    PubMed

    Tan, Shaolin; Feng, Shasha; Wang, Pei; Chen, Yao

    2014-11-01

    Evolutionary game theory provides an appropriate tool for investigating the competition and diffusion of behavioral traits in biological or social populations. A core challenge in evolutionary game theory is the strategy selection problem: Given two strategies, which one is favored by the population? Recent studies suggest that the answer depends not only on the payoff functions of strategies but also on the interaction structure of the population. Group interactions are one of the fundamental interactive modes within populations. This work aims to investigate the strategy selection problem in evolutionary game dynamics on group interaction networks. In detail, the strategy selection conditions are obtained for some typical networks with group interactions. Furthermore, the obtained conditions are applied to investigate selection between cooperation and defection in populations. The conditions for evolution of cooperation are derived for both the public goods game and volunteer's dilemma game. Numerical experiments validate the above analytical results.

  20. Molecular interactions of flavonoids to pepsin: Insights from spectroscopic and molecular docking studies.

    PubMed

    Zeng, Hua-Jin; Yang, Ran; Liang, Huili; Qu, Ling-Bo

    2015-01-01

    In the work described on this paper, the inhibitory effect of 10 flavonoids on pepsin and the interactions between them were investigated by a combination of spectroscopic and molecular docking methods. The results indicated that all flavonoids could bind with pepsin to form flavonoid-pepsin complexes. The binding parameters obtained from the data at different temperatures revealed that flavonoids could spontaneously interact with pepsin mainly through electrostatic forces and hydrophobic interactions with one binding site. According to synchronous and three-dimensional fluorescence spectra and molecular docking results, all flavonoids bound directly into the enzyme cavity site and the binding influenced the microenvironment and conformation of the pepsin activity site which resulted in the reduced enzyme activity. The present study provides direct evidence at a molecular level to understand the mechanism of digestion caused by flavonoids.

  1. 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.

  2. Generating interactive molecular documentaries using a library of graphical actions.

    PubMed

    Pulavarthi, P; Chiang, R; Altman, R B

    2000-01-01

    Paper-based publishing of scientific articles limits the types of presentations that can be used. The emergence of electronic publishing has created opportunities to increase the range of formats available for conveying scientific content. We introduce the Graphical Explanation Markup Language, GEML, implemented as an XML format for defining molecular documentaries which exploit the interactive capabilities of electronic publishing. GEML builds upon existing molecular structure definitions such as the Protein Data Bank (PDB) standard file format. GEML provides a library of gestures (or actions) commonly used for structural explanations, and is extensible. XML allows us to separate explicit statements about how to highlight a molecular structure from the implementation of these instructions. We also present GEIS (Generator of Explanatory Interactive Systems), a program that takes as input a GEML documentary definition file and produces all the files necessary for an interactive, web-based molecular documentary. To demonstrate GEML and GEIS, we constructed a documentary capturing the difficult 3D notions expressed in two selected published reports about human topoisomerase I. We have created a prototype Java application, GEMLBuilder, as an editor of GEML files. PMID:10902175

  3. 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.

  4. 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

  5. Using Molecular Networking for Microbial Secondary Metabolite Bioprospecting.

    PubMed

    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

    2016-01-08

    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.

  6. 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

  7. 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.

  8. Molecular Codes in Biological and Chemical Reaction Networks

    PubMed Central

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

    Shannon’s theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process “meaningful” information from those that do not. Here, we present a formal method to assess a system’s semantic capacity by analyzing a reaction network’s capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems posses different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life. PMID:23372756

  9. The evolution of generalized reciprocity on social interaction networks.

    PubMed

    van Doorn, Gerrit Sander; Taborsky, Michael

    2012-03-01

    Generalized reciprocity (help anyone, if helped by someone) is a minimal strategy capable of supporting cooperation between unrelated individuals. Its simplicity makes it an attractive model to explain the evolution of reciprocal altruism in animals that lack the information or cognitive skills needed for other types of reciprocity. Yet, generalized reciprocity is anonymous and thus defenseless against exploitation by defectors. Recognizing that animals hardly ever interact randomly, we investigate whether social network structure can mitigate this vulnerability. Our results show that heterogeneous interaction patterns strongly support the evolution of generalized reciprocity. The future probability of being rewarded for an altruistic act is inversely proportional to the average connectivity of the social network when cooperators are rare. Accordingly, sparse networks are conducive to the invasion of reciprocal altruism. Moreover, the evolutionary stability of cooperation is enhanced by a modular network structure. Communities of reciprocal altruists are protected against exploitation, because modularity increases the mean access time, that is, the average number of steps that it takes for a random walk on the network to reach a defector. Sparseness and community structure are characteristic properties of vertebrate social interaction patterns, as illustrated by network data from natural populations ranging from fish to primates.

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  12. Graph spectral analysis of protein interaction network evolution.

    PubMed

    Thorne, Thomas; Stumpf, Michael P H

    2012-10-01

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

  13. Drug interaction networks: an introduction to translational and clinical applications.

    PubMed

    Azuaje, Francisco

    2013-03-15

    This article introduces fundamental concepts to guide the analysis and interpretation of drug-target interaction networks. An overview of the generation and integration of interaction networks is followed by key strategies for extracting biologically meaningful information. The article highlights how this information can enable novel translational and clinically motivated applications. Important advances for the discovery of new treatments and for the detection of adverse drug effects are discussed. Examples of applications and findings originating from cardiovascular research are presented. The review ends with a discussion of crucial challenges and opportunities.

  14. Drug interaction networks: an introduction to translational and clinical applications.

    PubMed

    Azuaje, Francisco

    2013-03-15

    This article introduces fundamental concepts to guide the analysis and interpretation of drug-target interaction networks. An overview of the generation and integration of interaction networks is followed by key strategies for extracting biologically meaningful information. The article highlights how this information can enable novel translational and clinically motivated applications. Important advances for the discovery of new treatments and for the detection of adverse drug effects are discussed. Examples of applications and findings originating from cardiovascular research are presented. The review ends with a discussion of crucial challenges and opportunities. PMID:22977007

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2016-01-01

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

  17. Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

    NASA Astrophysics Data System (ADS)

    Gill, Joel C.; Malamud, Bruce D.

    2016-08-01

    This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability

  18. Probing Interactions in Complex Molecular Systems through Ordered Assembly

    SciTech Connect

    De Yoreo, J J; Bartelt, M C; Orme, C A; Villacampa, A; Weeks, B L; Miller, A E

    2002-01-31

    Emerging from the machinery of epitaxial science and chemical synthesis, is a growing emphasis on development of self-organized systems of complex molecular species. The nature of self-organization in these systems spans the continuum from simple crystallization of large molecules such as dendrimers and proteins, to assembly into large organized networks of nanometer-scale structures such as quantum dots or nanoparticles. In truth, self-organization in complex molecular systems has always been a central feature of many scientific disciplines including fields as diverse as structural biology, polymer science and geochemistry. But over the past decade, changes in those fields have often been marked by the degree to which researchers are using molecular-scale approaches to understand the hierarchy of structures and processes driven by this ordered assembly. At the same time, physical scientists have begun to use their knowledge of simple atomic and molecular systems to fabricate synthetic self-organized systems. This increasing activity in the field of self-organization is testament to the success of the physical and chemical sciences in building a detailed understanding of crystallization and epitaxy in simple atomic and molecular systems, one that is soundly rooted in thermodynamics and chemical kinetics. One of the fundamental challenges of chemistry and materials science in the coming decades is to develop a similarly well-founded physical understanding of assembly processes in complex molecular systems. Over the past five years, we have successfully used in situ atomic force microscopy (AFM) to investigate the physical controls on single crystal epitaxy from solutions for a wide range of molecular species. More recently, we have combined this method with grazing incidence X-ray diffraction and kinetic Monte Carlo modeling in order to relate morphology to surface atomic structure and processes. The purpose of this proposal was to extend this approach to assemblies

  19. Community structure description in amino acid interaction networks.

    PubMed

    Gaci, Omar

    2011-03-01

    In this paper, we represent proteins by amino acid interaction networks. This is a graph whose vertices are the protein's amino acids and whose edges are the interactions between them. We begin by identifying the main topological properties of these interaction networks using graph theory measures. We observe that the amino acids interact specifically, according to their structural role, and depending on whether they participate or not in the secondary structure. Thus, certain amino acids tend to group together to form local clouds. Then, we study the formation of node aggregations through community structure detections. We observe that the composition of organizations confirms a specific aggregation between loops around a core composed of secondary.

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  2. Evolutionary interaction networks of insect pathogenic fungi.

    PubMed

    Boomsma, Jacobus J; Jensen, Annette B; Meyling, Nicolai V; Eilenberg, Jørgen

    2014-01-01

    Lineages of insect pathogenic fungi are concentrated in three major clades: Hypocreales (several genera), Entomophthoromycota (orders Entomophthorales and Neozygitales), and Onygenales (genus Ascosphaera). Our review focuses on aspects of the evolutionary biology of these fungi that have remained underemphasized in previous reviews. To ensure integration with the better-known domains of insect pathology research, we followed a conceptual framework formulated by Tinbergen, asking complementary questions on mechanism, ontogeny, phylogeny, and adaptation. We aim to provide an introduction to the merits of evolutionary approaches for readers with a background in invertebrate pathology research and to make the insect pathogenic fungi more accessible as model systems for evolutionary biologists. We identify a number of questions in which fundamental research can offer novel insights into the evolutionary forces that have shaped host specialization and life-history traits such as spore number and size, somatic growth rate, toxin production, and interactions with host immune systems.

  3. Multiplicative interaction in network meta-analysis.

    PubMed

    Piepho, Hans-Peter; Madden, Laurence V; Williams, Emlyn R

    2015-02-20

    Meta-analysis of a set of clinical trials is usually conducted using a linear predictor with additive effects representing treatments and trials. Additivity is a strong assumption. In this paper, we consider models for two or more treatments that involve multiplicative terms for interaction between treatment and trial. Multiplicative models provide information on the sensitivity of each treatment effect relative to the trial effect. In developing these models, we make use of a two-way analysis-of-variance approach to meta-analysis and consider fixed or random trial effects. It is shown using two examples that models with multiplicative terms may fit better than purely additive models and provide insight into the nature of the trial effect. We also show how to model inconsistency using multiplicative terms.

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

    NASA Astrophysics Data System (ADS)

    Bhattacharyya, Samit; Sinha, Somdatta

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

  5. The Kinetochore Interaction Network (KIN) of ascomycetes

    PubMed Central

    Freitag, Michael

    2016-01-01

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

  6. 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.

  7. Antituberculosis activity of the molecular libraries screening center network library.

    PubMed

    Maddry, Joseph A; Ananthan, Subramaniam; Goldman, Robert C; Hobrath, Judith V; Kwong, Cecil D; Maddox, Clinton; Rasmussen, Lynn; Reynolds, Robert C; Secrist, John A; Sosa, Melinda I; White, E Lucile; Zhang, Wei

    2009-09-01

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

  8. Controlling single-molecule junction conductance by molecular interactions

    PubMed Central

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

    2015-01-01

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

  9. Controlling single-molecule junction conductance by molecular interactions

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  10. Nanoscale swimmers: hydrodynamic interactions and propulsion of molecular machines

    NASA Astrophysics Data System (ADS)

    Sakaue, T.; Kapral, R.; Mikhailov, A. S.

    2010-06-01

    Molecular machines execute nearly regular cyclic conformational changes as a result of ligand binding and product release. This cyclic conformational dynamics is generally non-reciprocal so that under time reversal a different sequence of machine conformations is visited. Since such changes occur in a solvent, coupling to solvent hydrodynamic modes will generally result in self-propulsion of the molecular machine. These effects are investigated for a class of coarse grained models of protein machines consisting of a set of beads interacting through pair-wise additive potentials. Hydrodynamic effects are incorporated through a configuration-dependent mobility tensor, and expressions for the propulsion linear and angular velocities, as well as the stall force, are obtained. In the limit where conformational changes are small so that linear response theory is applicable, it is shown that propulsion is exponentially small; thus, propulsion is nonlinear phenomenon. The results are illustrated by computations on a simple model molecular machine.

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

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

    PubMed Central

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

    2006-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Gill, Joel; Malamud, Bruce D.

    2016-04-01

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

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

    PubMed

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

    2011-09-01

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

  15. Mining the Modular Structure of Protein Interaction Networks

    PubMed Central

    Furlong, Laura Inés; Chernomoretz, Ariel

    2015-01-01

    Background Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera’s cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. PMID:25856434

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  17. Optimizing a global alignment of protein interaction networks

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  20. Two-dimensional short-range disordered crystalline networks from flexible molecular modules.

    PubMed

    Ecija, David; Vijayaraghavan, Saranyan; Auwärter, Willi; Joshi, Sushobhan; Seufert, Knud; Aurisicchio, Claudia; Bonifazi, Davide; Barth, Johannes V

    2012-05-22

    Studies of complex condensed matter systems have led to the discovery of materials of unexpected spatial organization as glasses, glassy crystals, quasicrystals, and protein and virus crystals. Here, we present two-dimensional (2D) short-range disordered molecular crystalline networks, which, regarding spatial organization, can be considered as surface analogues of 3D glassy crystals. In particular, the deposition of a flexible molecular module on Cu(111) gives rise to distinct phases whose characteristics have been examined in real space by scanning tunneling microscopy: a 2D short-range distortional disordered crystalline network and a 2D short-range orientational disordered crystalline network, respectively. Both phases exhibit a random arrangement of nanopores that are stabilized by the simultaneous presence of metal-organic and pyridyl-pyridyl interactions. The 2D short-range distortional disordered crystalline network displayed intriguing flexibility, as probed by the STM tip that modifies the pore shape, a prerequisite for adaptive behavior in host-guest processes.

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

    PubMed

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

    2016-01-26

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

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

    PubMed

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

    2016-08-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

    Janssen, Marco A; Jager, Wander

    2003-01-01

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

  5. Topological Signatures of Species Interactions in Metabolic Networks

    PubMed Central

    Feldman, Marcus W.

    2009-01-01

    Abstract The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting “reverse ecology” framework for studying the complex web of interactions characterizing various ecosystems. PMID:19178139

  6. Characterizing interactions in online social networks during exceptional events

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    ERIC Educational Resources Information Center

    Horwitz, Barry; Braun, Allen R.

    2004-01-01

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

  8. Analysing Interactions in a Teacher Network Forum: A Sociometric Approach

    ERIC Educational Resources Information Center

    Lisboa, Eliana Santana; Coutinho, Clara Pereira

    2013-01-01

    This article presents the sociometric analysis of the interactions in a forum of a social network created for the professional development of Portuguese-speaking teachers. The main goal of the forum, which was titled Stricto Sensu, was to discuss the educational value of programmes that joined the distance learning model in Brazil. The empirical…

  9. Topological signatures of species interactions in metabolic networks.

    PubMed

    Borenstein, Elhanan; Feldman, Marcus W

    2009-02-01

    The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting "reverse ecology" framework for studying the complex web of interactions characterizing various ecosystems.

  10. The Three Attentional Networks: On Their Independence and Interactions

    ERIC Educational Resources Information Center

    Callejas, Alicia; Lupianez, Juan; Tudela, Pio

    2004-01-01

    The present investigation was aimed to the study of the three attentional networks (Alerting, Orienting, and Executive Function) and their interactions. A modification of the task developed by Fan, McCandliss, Sommer, Raz, and Posner (2002) was used, in which a cost and benefit paradigm was combined with a flanker task and an alerting signal. We…

  11. Molecular electrostatics for probing lone pair-π interactions.

    PubMed

    Mohan, Neetha; Suresh, Cherumuttathu H; Kumar, Anmol; Gadre, Shridhar R

    2013-11-14

    An electrostatics-based approach has been proposed for probing the weak interactions between lone pair containing molecules and π deficient molecular systems. For electron-rich molecules, the negative minima in molecular electrostatic potential (MESP) topography give the location of electron localization and the MESP value at the minimum (Vmin) quantifies the electron-rich character of that region. Interactive behavior of a lone pair bearing molecule with electron deficient π-systems, such as hexafluorobenzene, 1,3,5-trinitrobenzene, 2,4,6-trifluoro-1,3,5-triazine and 1,2,4,5-tetracyanobenzene explored within DFT brings out good correlation of the lone pair-π interaction energy (E(int)) with the Vmin value of the electron-rich system. Such interaction is found to be portrayed well with the Electrostatic Potential for Intermolecular Complexation (EPIC) model. On the basis of the precise location of MESP minimum, a prediction for the orientation of a lone pair bearing molecule with an electron deficient π-system is possible in the majority of the cases studied.

  12. Molecular systems governing leaf growth: from genes to networks.

    PubMed

    González, Nathalie; Inzé, Dirk

    2015-02-01

    Arabidopsis leaf growth consists of a complex sequence of interconnected events involving cell division and cell expansion, and requiring multiple levels of genetic regulation. With classical genetics, numerous leaf growth regulators have been identified, but the picture is far from complete. With the recent advances made in quantitative phenotyping, the study of the quantitative, dynamic, and multifactorial features of leaf growth is now facilitated. The use of high-throughput phenotyping technologies to study large numbers of natural accessions or mutants, or to screen for the effects of large sets of chemicals will allow for further identification of the additional players that constitute the leaf growth regulatory networks. Only a tight co-ordination between these numerous molecular players can support the formation of a functional organ. The connections between the components of the network and their dynamics can be further disentangled through gene-stacking approaches and ultimately through mathematical modelling. In this review, we describe these different approaches that should help to obtain a holistic image of the molecular regulation of organ growth which is of high interest in view of the increasing needs for plant-derived products.

  13. Innovative interactive flexible docking method for multi-scale reconstruction elucidates dystrophin molecular assembly.

    PubMed

    Molza, A-E; Férey, N; Czjzek, M; Le Rumeur, E; Hubert, J-F; Tek, A; Laurent, B; Baaden, M; Delalande, O

    2014-01-01

    At present, our molecular knowledge of dystrophin, the protein encoded by the DMD gene and mutated in myopathy patients, remains limited. To get around the absence of its atomic structure, we have developed an innovative interactive docking method based on the BioSpring software in combination with Small-angle X-ray Scattering (SAXS) data. BioSpring allows interactive handling of biological macromolecules thanks to an augmented Elastic Network Model (aENM) that combines the spring network with non-bonded terms between atoms or pseudo-atoms. This approach can be used for building molecular assemblies even on a desktop or a laptop computer thanks to code optimizations including parallel computing and GPU programming. By combining atomistic and coarse-grained models, the approach significantly simplifies the set-up of multi-scale scenarios. BioSpring is remarkably efficient for the preparation of numeric simulations or for the design of biomolecular models integrating qualitative experimental data restraints. The combination of this program and SAXS allowed us to propose the first high-resolution models of the filamentous central domain of dystrophin, covering repeats 11 to 17. Low-resolution interactive docking experiments driven by a potential grid enabled us to propose how dystrophin may associate with F-actin and nNOS. This information provides an insight into medically relevant discoveries to come. PMID:25340652

  14. Innovative interactive flexible docking method for multi-scale reconstruction elucidates dystrophin molecular assembly.

    PubMed

    Molza, A-E; Férey, N; Czjzek, M; Le Rumeur, E; Hubert, J-F; Tek, A; Laurent, B; Baaden, M; Delalande, O

    2014-01-01

    At present, our molecular knowledge of dystrophin, the protein encoded by the DMD gene and mutated in myopathy patients, remains limited. To get around the absence of its atomic structure, we have developed an innovative interactive docking method based on the BioSpring software in combination with Small-angle X-ray Scattering (SAXS) data. BioSpring allows interactive handling of biological macromolecules thanks to an augmented Elastic Network Model (aENM) that combines the spring network with non-bonded terms between atoms or pseudo-atoms. This approach can be used for building molecular assemblies even on a desktop or a laptop computer thanks to code optimizations including parallel computing and GPU programming. By combining atomistic and coarse-grained models, the approach significantly simplifies the set-up of multi-scale scenarios. BioSpring is remarkably efficient for the preparation of numeric simulations or for the design of biomolecular models integrating qualitative experimental data restraints. The combination of this program and SAXS allowed us to propose the first high-resolution models of the filamentous central domain of dystrophin, covering repeats 11 to 17. Low-resolution interactive docking experiments driven by a potential grid enabled us to propose how dystrophin may associate with F-actin and nNOS. This information provides an insight into medically relevant discoveries to come.

  15. Effective interactions in molecular dynamics simulations of lysozyme solutions

    NASA Astrophysics Data System (ADS)

    Pellicane, Giuseppe; Sarkisov, Lev

    2014-09-01

    In this article we explore a problem of effective interactions between two rotationally restrained lysozyme molecules forming a crystal contact in aqueous solution. We perform non-equilibrium molecular dynamics simulations in order to estimate the interaction energy as a function of the distance between the two proteins obtained from direct application of the Jarzynski equality (JE), and compare it with that calculated by means of another non-equilibrium approach (Forward-Reverse method) and constrained force methods. The performance of the JE equality when applied to solvated protein interactions is discussed. All of the equilibrium and non-equilibrium methods show clear evidence that the potentials of mean force (PMF) are short-ranged, do not exceed few kTs, and that there is an accumulation of anions in the presence of hydrophobic surfaces.

  16. Monitoring molecular beacon/DNA interactions using atomic force microscopy.

    PubMed

    Jin, Yan; Wang, Kemin; Tan, Weihong; Wu, Ping; Wang, Qing; Huang, Hongmei; Huang, Shasheng; Tang, Zhiwen; Guo, Qiuping

    2004-10-01

    The molecular beacon (MB) is a new fluorescence probe containing a single-stranded oligonucleotide with a probe sequence embedded in complementary sequences that form a hairpin stem. Due to the inherent fluorescent signal transduction mechanism, an MB functions as a sensitive probe with a high signal-to-background ratio for real-time monitoring and provides a variety of exciting opportunities in DNA, RNA, and protein studies. To better understand the properties of MBs, the specific interactions between MB and target DNA (complementary and one-base mismatch) have been directly investigated by atomic force microscopy. The interaction force between a linear DNA probe and the target DNA was also detected and compared to that between MB and target DNA. The results demonstrate the high specificity of the MB/target DNA compared to the linear DNA/target DNA interaction.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  18. ProtNet: a tool for stochastic simulations of protein interaction networks dynamics

    PubMed Central

    Bernaschi, Massimo; Castiglione, Filippo; Ferranti, Alessandra; Gavrila, Caius; Tinti, Michele; Cesareni, Gianni

    2007-01-01

    Background Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell. Results To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell. We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and

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

    PubMed Central

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

    2013-01-01

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

  20. Molecular Basis of Chemokine CXCL5-Glycosaminoglycan Interactions.

    PubMed

    Sepuru, Krishna Mohan; Nagarajan, Balaji; Desai, Umesh R; Rajarathnam, Krishna

    2016-09-23

    Chemokines, a large family of highly versatile small soluble proteins, play crucial roles in defining innate and adaptive immune responses by regulating the trafficking of leukocytes, and also play a key role in various aspects of human physiology. Chemokines share the characteristic feature of reversibly existing as monomers and dimers, and their functional response is intimately coupled to interaction with glycosaminoglycans (GAGs). Currently, nothing is known regarding the structural basis or molecular mechanisms underlying CXCL5-GAG interactions. To address this missing knowledge, we characterized the interaction of a panel of heparin oligosaccharides to CXCL5 using solution NMR, isothermal titration calorimetry, and molecular dynamics simulations. NMR studies indicated that the dimer is the high-affinity GAG binding ligand and that lysine residues from the N-loop, 40s turn, β3 strand, and C-terminal helix mediate binding. Isothermal titration calorimetry indicated a stoichiometry of two oligosaccharides per CXCL5 dimer. NMR-based structural models reveal that these residues form a contiguous surface within a monomer and, interestingly, that the GAG-binding domain overlaps with the receptor-binding domain, indicating that a GAG-bound chemokine cannot activate the receptor. Molecular dynamics simulations indicate that the roles of the individual lysines are not equivalent and that helical lysines play a more prominent role in determining binding geometry and affinity. Further, binding interactions and GAG geometry in CXCL5 are novel and distinctly different compared with the related chemokines CXCL1 and CXCL8. We conclude that a finely tuned balance between the GAG-bound dimer and free soluble monomer regulates CXCL5-mediated receptor signaling and function.

  1. Molecular Basis of Chemokine CXCL5-Glycosaminoglycan Interactions.

    PubMed

    Sepuru, Krishna Mohan; Nagarajan, Balaji; Desai, Umesh R; Rajarathnam, Krishna

    2016-09-23

    Chemokines, a large family of highly versatile small soluble proteins, play crucial roles in defining innate and adaptive immune responses by regulating the trafficking of leukocytes, and also play a key role in various aspects of human physiology. Chemokines share the characteristic feature of reversibly existing as monomers and dimers, and their functional response is intimately coupled to interaction with glycosaminoglycans (GAGs). Currently, nothing is known regarding the structural basis or molecular mechanisms underlying CXCL5-GAG interactions. To address this missing knowledge, we characterized the interaction of a panel of heparin oligosaccharides to CXCL5 using solution NMR, isothermal titration calorimetry, and molecular dynamics simulations. NMR studies indicated that the dimer is the high-affinity GAG binding ligand and that lysine residues from the N-loop, 40s turn, β3 strand, and C-terminal helix mediate binding. Isothermal titration calorimetry indicated a stoichiometry of two oligosaccharides per CXCL5 dimer. NMR-based structural models reveal that these residues form a contiguous surface within a monomer and, interestingly, that the GAG-binding domain overlaps with the receptor-binding domain, indicating that a GAG-bound chemokine cannot activate the receptor. Molecular dynamics simulations indicate that the roles of the individual lysines are not equivalent and that helical lysines play a more prominent role in determining binding geometry and affinity. Further, binding interactions and GAG geometry in CXCL5 are novel and distinctly different compared with the related chemokines CXCL1 and CXCL8. We conclude that a finely tuned balance between the GAG-bound dimer and free soluble monomer regulates CXCL5-mediated receptor signaling and function. PMID:27471273

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  3. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

    PubMed Central

    2013-01-01

    Background Molecular biology knowledge can be formalized and systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist an increasing number of maps of molecular interactions containing detailed and step-wise description of various cell mechanisms. It is difficult to explore these large maps, to organize discussion of their content and to maintain them. Several efforts were recently made to combine these capabilities together in one environment, and NaviCell is one of them. Results NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. It is characterized by a combination of three essential features: (1) efficient map browsing based on Google Maps; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of interest in the context of signaling pathways and crosstalk between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell greatly facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive and user-friendly fashion due to an imbedded blogging system. Conclusions NaviCell provides user-friendly exploration of large-scale maps of molecular interactions, thanks to Google Maps and WordPress interfaces, with which many users are already familiar. Semantic zooming which is used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization readable. In addition, NaviCell provides a framework for community-based curation of maps

  4. A Scalable Method for Molecular Network Reconstruction Identifies Properties of Targets and Mutations in Acute Myeloid Leukemia

    PubMed Central

    Ong, Edison; Szedlak, Anthony; Kang, Yunyi; Smith, Peyton; Smith, Nicholas; McBride, Madison; Finlay, Darren; Vuori, Kristiina; Mason, James; Ball, Edward D.

    2015-01-01

    Abstract A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein–protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML. PMID:25844667

  5. Molecular dynamics simulations on the interactions of low molecular weight natural organic acids with C60.

    PubMed

    Sun, Qian; Xie, Hong-Bin; Chen, Jingwen; Li, Xuehua; Wang, Zhuang; Sheng, Lianxi

    2013-07-01

    As an important part of dissolved organic matter (DOM), low molecular weight organic acids (LOAs) may play a key role in the process for DOM stabilizing carbon nanomaterials (e.g. C60) suspensions in aquatic environment. In addition, both LOAs and C60 have been detected in the troposphere and therefore have a chance to interact with each other in the gaseous phase. However, the mechanism for LOAs-C60 interactions and their environmental implications need further investigations. In this study, molecular dynamics (MD) simulation was employed to investigate the interactions between both neutral and ionic LOAs with C60 in vacuum and water. The results showed that the adsorptions of all LOAs on C60 in energy are favorable, and the aromatic acids have stronger interactions with C60 than the aliphatic acids in vacuum and water. The interaction energies (Eint) of the LOA anions with C60 were weaker than those of their corresponding neutral LOA molecules. The models were also developed to predict and interpret Eint based on the results from MD simulations. Dispersion, induction and hydrophobic interactions were found to be the dominating factor in Eint. These findings indicate that cost-efficient MD simulation can be employed as an important tool to predict the adsorption behavior of LOAs on carbon nanomaterials.

  6. Hepatitis A virus: host interactions, molecular epidemiology and evolution.

    PubMed

    Vaughan, Gilberto; Goncalves Rossi, Livia Maria; Forbi, Joseph C; de Paula, Vanessa S; Purdy, Michael A; Xia, Guoliang; Khudyakov, Yury E

    2014-01-01

    Infection with hepatitis A virus (HAV) is the commonest viral cause of liver disease and presents an important public health problem worldwide. Several unique HAV properties and molecular mechanisms of its interaction with host were recently discovered and should aid in clarifying the pathogenesis of hepatitis A. Genetic characterization of HAV strains have resulted in the identification of different genotypes and subtypes, which exhibit a characteristic worldwide distribution. Shifts in HAV endemicity occurring in different parts of the world, introduction of genetically diverse strains from geographically distant regions, genotype displacement observed in some countries and population expansion detected in the last decades of the 20th century using phylogenetic analysis are important factors contributing to the complex dynamics of HAV infections worldwide. Strong selection pressures, some of which, like usage of deoptimized codons, are unique to HAV, limit genetic variability of the virus. Analysis of subgenomic regions has been proven useful for outbreak investigations. However, sharing short sequences among epidemiologically unrelated strains indicates that specific identification of HAV strains for molecular surveillance can be achieved only using whole-genome sequences. Here, we present up-to-date information on the HAV molecular epidemiology and evolution, and highlight the most relevant features of the HAV-host interactions.

  7. Interaction between Cassiopeia A and Nearby Molecular Clouds

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  8. Interaction between Cassiopeia A and nearby molecular clouds

    SciTech Connect

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

    2014-12-01

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

  9. Digital Ecology: Coexistence and Domination among Interacting Networks

    NASA Astrophysics Data System (ADS)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Shankar, Chandrashekar

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

  11. Liquid Crystalline Polymers and Networks -- orientation, molecular shape change, mechanics

    NASA Astrophysics Data System (ADS)

    Warner, Mark

    2008-03-01

    In a prescient paper of 1969, Pierre-Gilles de Gennes envisaged both liquid crystal polymers and elastomers. 10 years later, these systems were realised. After 25 years, monodomain elastomers were prepared and displayed phenomena he had predicted: rods incorporated into polymers induce liquid crystallinity in polymer melts and elastomers; orientational order causes shape changes in the back bones of such polymers; mechanical ramifications follow in networks, e.g. spontaneous elongations and contractions on changing order. The latter are proposed as the basis of micro-actuation and artificial muscles, both heat and light-driven. In 1969, de Gennes already described ideal networks heated through the nematic-isotropic transition losing all their order by mechanical relaxation. It is not obvious, but is true in theory and largely in experiment, even in highly non-ideal networks. He also envisaged that a cholesteric network, where there is a topological memory of chirality imprinted by crosslinking chains in a twisted state. Chirality cannot relax away on entering the isotropic phase, even in systems without molecular chirality (for instance those crosslinked in the presence of chiral solvent that is subsequently exchanged away). His chiral elastomers have found application as mechanically-tuneable, rubber lasers. De Gennes also constructed the first continuum elastic theories of nematic elastomers (1982), though distortions are generally very large. His elasticity has informed non-linear elasticity that works even at large amplitudes. I shall describe de Gennes' many contributions, and the current state of a field that has since yielded still more remarkable phenomena.

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

    PubMed Central

    2009-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  16. Electron-phonon interaction within classical molecular dynamics

    DOE PAGES

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

    2016-07-14

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

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

    PubMed

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

    2009-01-01

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

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

    SciTech Connect

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

    2003-03-26

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

  19. Electron-phonon interaction within classical molecular dynamics

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Sun, Huandong; Li, Ming; Xu, Ying

    2003-05-01

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

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

    PubMed

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-20

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

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

    NASA Astrophysics Data System (ADS)

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-01

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

  7. Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

    PubMed Central

    2014-01-01

    Background Prostate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis – the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development. Results Using the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer. Conclusion We conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies. PMID:25151146

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

    PubMed

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

    2014-11-01

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

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

    PubMed

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

    2016-07-01

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

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

    PubMed Central

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

    2009-01-01

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

  11. Auditing medical records accesses via healthcare interaction networks.

    PubMed

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

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

  12. Auditing Medical Records Accesses via Healthcare Interaction Networks

    PubMed Central

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

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

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

    PubMed

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

    2015-06-01

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

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

    PubMed

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

    2015-06-01

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

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

    PubMed Central

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

    2015-01-01

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

  16. Agreement dynamics on interaction networks with diverse topologies.

    PubMed

    Barrat, Alain; Baronchelli, Andrea; Dall'Asta, Luca; Loreto, Vittorio

    2007-06-01

    We review the behavior of a recently introduced model of agreement dynamics, called the "Naming Game." This model describes the self-organized emergence of linguistic conventions and the establishment of simple communication systems in a population of agents with pairwise local interactions. The mechanisms of convergence towards agreement strongly depend on the network of possible interactions between the agents. In particular, the mean-field case in which all agents communicate with all the others is not efficient, since a large temporary memory is requested for the agents. On the other hand, regular lattice topologies lead to a fast local convergence but to a slow global dynamics similar to coarsening phenomena. The embedding of the agents in a small-world network represents an interesting tradeoff: a local consensus is easily reached, while the long-range links allow to bypass coarsening-like convergence. We also consider alternative adaptive strategies which can lead to faster global convergence.

  17. Reconstruction and Application of Protein–Protein Interaction Network

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-04-01

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

  20. Molecular Statics Simulation of Hydrogen Defect Interaction in Tungsten

    NASA Astrophysics Data System (ADS)

    Li, Xiaochun; Liu, Yinan; Yu, Yi; Niu, Guojian; Luo, Guangnan; Shu, Xiaolin

    2015-06-01

    Hydrogen (H) defect interactions have been investigated by molecular statics simulations in tungsten (W), including H-H interactions and interactions between H and W self-interstitial atoms. The interactions between H and small H-vacancy clusters are also demonstrated; the binding energies of an H, a vacancy and a self-interstitial W to an H-vacancy cluster depend on the H-to-vacancy ratio. We conclude that H bubble formation needs a high concentration of H in W for the H bubble nucleation and growth, which are also governed by the H-to-vacancy ratio of the cluster. The vacancy first combines with H atoms and a cluster forms, then the H-vacancy cluster goes through the whole process of vacancy capture, H capture, and vacancy capture again, and as a result the H-vacancy cluster grows larger and larger. Finally, the H bubble forms. supported by National Natural Science Foundation of China (Nos. 51171008 and 11405201) and the National Magnetic Confinement Fusion Science Program of China (No. 2013GB1090)

  1. Molecular networks involved in the immune control of BK polyomavirus.

    PubMed

    Girmanova, Eva; Brabcova, Irena; Klema, Jiri; Hribova, Petra; Wohlfartova, Mariana; Skibova, Jelena; Viklicky, Ondrej

    2012-01-01

    BK polyomavirus infection is the important cause of virus-related nephropathy following kidney transplantation. BK virus reactivates in 30%-80% of kidney transplant recipients resulting in BK virus-related nephropathy in 1%-10% of cases. Currently, the molecular processes associated with asymptomatic infections in transplant patients infected with BK virus remain unclear. In this study we evaluate intrarenal molecular processes during different stages of BKV infection. The gene expression profiles of 90 target genes known to be associated with immune response were evaluated in kidney graft biopsy material using TaqMan low density array. Three patient groups were examined: control patients with no evidence of BK virus reactivation (n = 11), infected asymptomatic patients (n = 9), and patients with BK virus nephropathy (n = 10). Analysis of biopsies from asymptomatic viruria patients resulted in the identification of 5 differentially expressed genes (CD3E, CD68, CCR2, ICAM-1, and SKI) (P < 0.05), and functional analysis showed a significantly heightened presence of costimulatory signals (e.g., CD40/CD40L; P < 0.05). Gene ontology analysis revealed several biological networks associated with BKV immune control in comparison to the control group. This study demonstrated that asymptomatic BK viruria is associated with a different intrarenal regulation of several genes implicating in antiviral immune response.

  2. Targeting molecular interactions essential for Plasmodium sexual reproduction

    PubMed Central

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

    2015-01-01

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

  3. Using molecular beacons to probe molecular interactions between lactate dehydrogenase and single-stranded DNA.

    PubMed

    Fang, X; Li, J J; Tan, W

    2000-07-15

    The interactions between two key macromolecular species, nucleic acids and proteins, control many important biological processes. There have been limited effective methodologies to study these interactions in real time. In this work, we have applied a newly developed molecular beacon (MB) DNA probe for the analysis of an enzyme, lactate dehydrogenase (LDH), and for the investigation of its properties of binding with single-stranded DNA. Molecular beacons are single-stranded oligonucleotide probes designed to report the presence of specific complementary nucleic acids by fluorescence detection. The interaction between LDH and MB has resulted in a significant fluorescence signal enhancement, which is used for the elucidation of MB/LDH binding properties. The processes of binding between MB and different isoenzymes of LDH have been studied. The results show that the stoichiometry of LDH-5/MB binding is 1:1, and the binding constant is 1.9 x 10(-7) M(-1). We have also studied salt effects, binding sites, temperature effects, pH effects, and the binding specificities for different isoenzymes. Our results demonstrate that MB can be effectively used for sensitive protein quantitation and for efficient protein-DNA interaction studies. MB has a signal transduction mechanism built within the molecule and can thus be used for the development of rapid protein assays and for real-time measurements.

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

    PubMed Central

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

    2016-01-01

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

  5. Calsyntenin-3 molecular architecture and interaction with neurexin 1α.

    PubMed

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

    2014-12-12

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

  6. Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments

    ERIC Educational Resources Information Center

    Eagle, Michael; Hicks, Drew; Barnes, Tiffany

    2015-01-01

    Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…

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

    PubMed

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

    2016-02-01

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

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

    PubMed Central

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

    2016-01-01

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

  9. Deconvolving molecular signatures of interactions between microbial colonies

    PubMed Central

    Harn, Y.-C.; Powers, M. J.; Shank, E. A.; Jojic, V.

    2015-01-01

    Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret. Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies. Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project. Contact: vjojic@cs.unc.edu Supplementary

  10. Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

    PubMed Central

    Azpeitia, Eugenio; Weinstein, Nathan; Benítez, Mariana; Mendoza, Luis; Alvarez-Buylla, Elena R.

    2013-01-01

    Over the last few decades, the Arabidopsis thaliana root stem cell niche (RSCN) has become a model system for the study of plant development and stem cell niche dynamics. Currently, many of the molecular mechanisms involved in RSCN maintenance and development have been described. A few years ago, we published a gene regulatory network (GRN) model integrating this information. This model suggested that there were missing components or interactions. Upon updating the model, the observed stable gene configurations of the RSCN could not be recovered, indicating that there are additional missing components or interactions in the model. In fact, due to the lack of experimental data, GRNs inferred from published data are usually incomplete. However, predicting the location and nature of the missing data is a not trivial task. Here, we propose a set of procedures for detecting and predicting missing interactions in Boolean networks. We used these procedures to predict putative missing interactions in the A. thaliana RSCN network model. Using our approach, we identified three necessary interactions to recover the reported gene activation configurations that have been experimentally uncovered for the different cell types within the RSCN: (1) a regulation of PHABULOSA to restrict its expression domain to the vascular cells, (2) a self-regulation of WOX5, possibly by an indirect mechanism through the auxin signaling pathway, and (3) a positive regulation of JACKDAW by MAGPIE. The procedures proposed here greatly reduce the number of possible Boolean functions that are biologically meaningful and experimentally testable and that do not contradict previous data. We believe that these procedures can be used on any Boolean network. However, because the procedures were designed for the specific case of the RSCN, formal demonstrations of the procedures should be shown in future efforts. PMID:23658556

  11. Protein function prediction using guilty by association from interaction networks.

    PubMed

    Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C E

    2015-12-01

    Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.

  12. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions.

    PubMed

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P; Yao, Shuqiao

    2016-01-01

    Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network-mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  13. Spectroscopic investigations, molecular interactions, and molecular docking studies on the potential inhibitor "thiophene-2-carboxylicacid".

    PubMed

    Karthick, T; Balachandran, V; Perumal, S

    2015-04-15

    Thiophene derivatives have been focused in the past decades due to their remarkable biological and pharmacological activities. In connection with that the conformational stability, spectroscopic characterization, molecular (inter- and intra-) interactions, and molecular docking studies on thiophene-2-carboxylicacid have been performed in this work by experimental FT-IR and theoretical quantum chemical computations. Experimentally recorded FT-IR spectrum in the region 4000-400 cm(-1) has been compared with the scaled theoretical spectrum and the spectral peaks have been assigned on the basis of potential energy distribution results obtained from MOLVIB program package. The conformational stability of monomer and dimer conformers has been examined. The presence of inter- and intramolecular interactions in the monomer and dimer conformers have been explained by natural bond orbital analysis. The UV-Vis spectra of the sample in different solvents have been simulated and solvent effects were predicted by polarisable continuum model with TD-DFT/B3LYP/6-31+G(d,p) method. To test the biological activity of the sample, molecular docking (ligand-protein) simulations have been performed using SWISSDOCK web server. The full fitness (FF) score and binding affinity values revealed that thiophene-2-carboxylicacid can act as potential inhibitor against inflammation.

  14. Spectroscopic investigations, molecular interactions, and molecular docking studies on the potential inhibitor "thiophene-2-carboxylicacid"

    NASA Astrophysics Data System (ADS)

    Karthick, T.; Balachandran, V.; Perumal, S.

    2015-04-01

    Thiophene derivatives have been focused in the past decades due to their remarkable biological and pharmacological activities. In connection with that the conformational stability, spectroscopic characterization, molecular (inter- and intra-) interactions, and molecular docking studies on thiophene-2-carboxylicacid have been performed in this work by experimental FT-IR and theoretical quantum chemical computations. Experimentally recorded FT-IR spectrum in the region 4000-400 cm-1 has been compared with the scaled theoretical spectrum and the spectral peaks have been assigned on the basis of potential energy distribution results obtained from MOLVIB program package. The conformational stability of monomer and dimer conformers has been examined. The presence of inter- and intramolecular interactions in the monomer and dimer conformers have been explained by natural bond orbital analysis. The UV-Vis spectra of the sample in different solvents have been simulated and solvent effects were predicted by polarisable continuum model with TD-DFT/B3LYP/6-31+G(d,p) method. To test the biological activity of the sample, molecular docking (ligand-protein) simulations have been performed using SWISSDOCK web server. The full fitness (FF) score and binding affinity values revealed that thiophene-2-carboxylicacid can act as potential inhibitor against inflammation.

  15. At the Intersection of Networks and Highly Interactive Online Games

    NASA Astrophysics Data System (ADS)

    Armitage, Grenville

    The game industry continues to evolves its techniques for extracting the most realistic 'immersion' experience for players given the vagaries on best-effort Internet service. A key challenge for service providers is understanding the characteristics of traffic imposed on networks by games, and their service quality requirements. Interactive online games are particularly susceptible to the side effects of other non-interactive (or delay- and loss-tolerant) traffic sharing next- generation access links. This creates challenges out toward the edges, where high-speed home LANs squeeze through broadband consumer access links to reach the Internet. In this chapter we identify a range of research work exploring many issues associated with the intersection of highly interactive games and the Internet, and hopefully stimulate some further thinking along these lines.

  16. Molecular Dynamics of Shock Wave Interaction with Nanoscale Structured Materials

    NASA Astrophysics Data System (ADS)

    Al-Qananwah, Ahmad K.

    Typical theoretical treatments of shock wave interactions are based on a continuum approach, which cannot resolve the spatial variations in solids with nano-scale porous structure. Nano-structured materials have the potential to attenuate the strength of traveling shock waves because of their high surface-to-volume ratio. To investigate such interactions we have developed a molecular dynamics simulation model, based on Short Range Attractive interactions. A piston, modeled as a uni-directional repulsive force field translating at a prescribed velocity, impinges on a region of gas which is compressed to form a shock, which in turn is driven against an atomistic solid wall. Periodic boundary conditions are used in the directions orthogonal to the piston motion, and we have considered solids based on either embedded atom potentials (target structure) or tethered potential (rigid piston, holding wall). Velocity, temperature and stress fields are computed locally in both gas and solid regions, and displacements within the solid are interpreted in terms of its elastic constants. In this work we present results of the elastic behavior of solid structures subjected to shock wave impact and analysis of energy transport and absorption in porous materials. The results indicated that the presence of nano-porous material layers in front of a target wall reduced the stress magnitude detected inside and the energy deposited there by about 30 percent while, at the same time, its loading rate was decreased substantially.

  17. Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell

    PubMed Central

    Lim, Wendell A.; Lee, Connie M.; Tang, Chao

    2013-01-01

    A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241

  18. Measuring Asymmetric Interactions in Resting State Brain Networks*

    PubMed Central

    Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

    2015-01-01

    Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  19. Measuring Asymmetric Interactions in Resting State Brain Networks.

    PubMed

    Joshi, Anand A; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M

    2015-01-01

    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  20. Plasticity of genetic interactions in metabolic networks of yeast.

    PubMed

    Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

    2007-02-13

    Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness. PMID:17284612

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

    SciTech Connect

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

    2011-04-07

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

  2. Attractive interactions among intermediate filaments determine network mechanics in vitro.

    PubMed

    Pawelzyk, Paul; Mücke, Norbert; Herrmann, Harald; Willenbacher, Norbert

    2014-01-01

    Mechanical and structural properties of K8/K18 and vimentin intermediate filament (IF) networks have been investigated using bulk mechanical rheometry and optical microrheology including diffusing wave spectroscopy and multiple particle tracking. A high elastic modulus G0 at low protein concentration c, a weak concentration dependency of G0 (G0 ∼ c(0.5 ± 0.1)) and pronounced strain stiffening are found for these systems even without external crossbridgers. Strong attractive interactions among filaments are required to maintain these characteristic mechanical features, which have also been reported for various other IF networks. Filament assembly, the persistence length of the filaments and the network mesh size remain essentially unaffected when a nonionic surfactant is added, but strain stiffening is completely suppressed, G0 drops by orders of magnitude and exhibits a scaling G0 ∼ c(1.9 ± 0.2) in agreement with microrheological measurements and as expected for entangled networks of semi-flexible polymers. Tailless K8Δ/K18ΔT and various other tailless filament networks do not exhibit strain stiffening, but still show high G0 values. Therefore, two binding sites are proposed to exist in IF networks. A weaker one mediated by hydrophobic amino acid clusters in the central rod prevents stretched filaments between adjacent cross-links from thermal equilibration and thus provides the high G0 values. Another strong one facilitating strain stiffening is located in the tail domain with its high fraction of hydrophobic amino acid sequences. Strain stiffening is less pronounced for vimentin than for K8/K18 due to electrostatic repulsion forces partly compensating the strong attraction at filament contact points.

  3. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions

    PubMed Central

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of “commonly accepted beliefs” seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node’s age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  4. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.

    PubMed

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  5. Building KCNQ1/KCNE1 channel models and probing their interactions by molecular-dynamics simulations.

    PubMed

    Xu, Yu; Wang, Yuhong; Meng, Xuan-Yu; Zhang, Mei; Jiang, Min; Cui, Meng; Tseng, Gea-Ny

    2013-12-01

    The slow delayed rectifier (I(KS)) channel is composed of KCNQ1 (pore-forming) and KCNE1 (auxiliary) subunits, and functions as a repolarization reserve in the human heart. Design of I(KS)-targeting anti-arrhythmic drugs requires detailed three-dimensional structures of the KCNQ1/KCNE1 complex, a task made possible by Kv channel crystal structures (templates for KCNQ1 homology-modeling) and KCNE1 NMR structures. Our goal was to build KCNQ1/KCNE1 models and extract mechanistic information about their interactions by molecular-dynamics simulations in an explicit lipid/solvent environment. We validated our models by confirming two sets of model-generated predictions that were independent from the spatial restraints used in model-building. Detailed analysis of the molecular-dynamics trajectories revealed previously unrecognized KCNQ1/KCNE1 interactions, whose relevance in I(KS) channel function was confirmed by voltage-clamp experiments. Our models and analyses suggest three mechanisms by which KCNE1 slows KCNQ1 activation: by promoting S6 bending at the Pro hinge that closes the activation gate; by promoting a downward movement of gating charge on S4; and by establishing a network of electrostatic interactions with KCNQ1 on the extracellular surface that stabilizes the channel in a pre-open activated state. Our data also suggest how KCNE1 may affect the KCNQ1 pore conductance.

  6. Network of Interactions Between Ciliates and Phytoplankton During Spring

    PubMed Central

    Posch, Thomas; Eugster, Bettina; Pomati, Francesco; Pernthaler, Jakob; Pitsch, Gianna; Eckert, Ester M.

    2015-01-01

    The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile) as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic/omnivorous species, and highlighted the role of Halteria/Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA) proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species. PMID:26635757

  7. MiRTargetLink--miRNAs, Genes and Interaction Networks.

    PubMed

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-04-14

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink.

  8. miRTargetLink—miRNAs, Genes and Interaction Networks

    PubMed Central

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-01-01

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink. PMID:27089332

  9. Real-time hierarchically distributed processing network interaction simulation

    NASA Technical Reports Server (NTRS)

    Zimmerman, W. F.; Wu, C.

    1987-01-01

    The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

  10. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

    PubMed

    Basha, Omer; Flom, Dvir; Barshir, Ruth; Smoly, Ilan; Tirman, Shoval; Yeger-Lotem, Esti

    2015-07-01

    The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

  11. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks

    PubMed Central

    Chambers, Brendan; MacLean, Jason N.

    2016-01-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  12. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

    PubMed

    Chambers, Brendan; MacLean, Jason N

    2016-08-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  13. Interactive display of molecular models using a microcomputer system

    NASA Technical Reports Server (NTRS)

    Egan, J. T.; Macelroy, R. D.

    1980-01-01

    A simple, microcomputer-based, interactive graphics display system has been developed for the presentation of perspective views of wire frame molecular models. The display system is based on a TERAK 8510a graphics computer system with a display unit consisting of microprocessor, television display and keyboard subsystems. The operating system includes a screen editor, file manager, PASCAL and BASIC compilers and command options for linking and executing programs. The graphics program, written in USCD PASCAL, involves the centering of the coordinate system, the transformation of centered model coordinates into homogeneous coordinates, the construction of a viewing transformation matrix to operate on the coordinates, clipping invisible points, perspective transformation and scaling to screen coordinates; commands available include ZOOM, ROTATE, RESET, and CHANGEVIEW. Data file structure was chosen to minimize the amount of disk storage space. Despite the inherent slowness of the system, its low cost and flexibility suggests general applicability.

  14. Molecular and chemical dialogues in bacteria-protozoa interactions

    PubMed Central

    Song, Chunxu; Mazzola, Mark; Cheng, Xu; Oetjen, Janina; Alexandrov, Theodore; Dorrestein, Pieter; Watrous, Jeramie; van der Voort, Menno; Raaijmakers, Jos M.

    2015-01-01

    Protozoan predation of bacteria can significantly affect soil microbial community composition and ecosystem functioning. Bacteria possess diverse defense strategies to resist or evade protozoan predation. For soil-dwelling Pseudomonas species, several secondary metabolites were proposed to provide protection against different protozoan genera. By combining whole-genome transcriptome analyses with (live) imaging mass spectrometry (IMS), we observed multiple changes in the molecular and chemical dialogues between Pseudomonas fluorescens and the protist Naegleria americana. Lipopeptide (LP) biosynthesis was induced in Pseudomonas upon protozoan grazing and LP accumulation transitioned from homogeneous distributions across bacterial colonies to site-specific accumulation at the bacteria-protist interface. Also putrescine biosynthesis was upregulated in P. fluorescens upon predation. We demonstrated that putrescine induces protozoan trophozoite encystment and adversely affects cyst viability. This multifaceted study provides new insights in common and strain-specific responses in bacteria-protozoa interactions, including responses that contribute to bacterial survival in highly competitive soil and rhizosphere environments. PMID:26246193

  15. Integrating pathways of Parkinson's disease in a molecular interaction map.

    PubMed

    Fujita, Kazuhiro A; Ostaszewski, Marek; Matsuoka, Yukiko; Ghosh, Samik; Glaab, Enrico; Trefois, Christophe; Crespo, Isaac; Perumal, Thanneer M; Jurkowski, Wiktor; Antony, Paul M A; Diederich, Nico; Buttini, Manuel; Kodama, Akihiko; Satagopam, Venkata P; Eifes, Serge; Del Sol, Antonio; Schneider, Reinhard; Kitano, Hiroaki; Balling, Rudi

    2014-02-01

    Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .

  16. Revealing the potential pathogenesis of glioma by utilizing a glioma associated protein-protein interaction network.

    PubMed

    Pan, Weiran; Li, Gang; Yang, Xiaoxiao; Miao, Jinming

    2015-04-01

    This study aims to explore the potential mechanism of glioma through bioinformatic approaches. The gene expression profile (GSE4290) of glioma tumor and non-tumor samples was downloaded from Gene Expression Omnibus database. A total of 180 samples were available, including 23 non-tumor and 157 tumor samples. Then the raw data were preprocessed using robust multiarray analysis, and 8,890 differentially expressed genes (DEGs) were identified by using t-test (false discovery rate < 0.0005). Furthermore, 16 known glioma related genes were abstracted from Genetic Association Database. After mapping 8,890 DEGs and 16 known glioma related genes to Human Protein Reference Database, a glioma associated protein-protein interaction network (GAPN) was constructed. In addition, 51 sub-networks in GAPN were screened out through Molecular Complex Detection (score ≥ 1), and sub-network 1 was found to have the closest interaction (score = 3). What' more, for the top 10 sub-networks, Gene Ontology (GO) enrichment analysis (p value < 0.05) was performed, and DEGs involved in sub-network 1 and 2, such as BRMS1L and CCNA1, were predicted to regulate cell growth, cell cycle, and DNA replication via interacting with known glioma related genes. Finally, the overlaps of DEGs and human essential, housekeeping, tissue-specific genes were calculated (p value = 1.0, 1.0, and 0.00014, respectively) and visualized by Venn Diagram package in R. About 61% of human tissue-specific genes were DEGs as well. This research shed new light on the pathogenesis of glioma based on DEGs and GAPN, and our findings might provide potential targets for clinical glioma treatment.

  17. Cryo-bioorganic chemistry: molecular interactions at low temperature.

    PubMed

    Vajda, T

    1999-10-30

    Freezing of aqueous or organic solutions plays a pivotal role in enhancement of rate and/or yield of biomolecular reactions. The smooth conditions of the frozen state at low temperature can also suppress racemization and side-product formation of the reactions. Molecular interactions in liquid undercooled solutions, on the other hand, offer the possibility to study enzyme activity mechanisms in vitro and a chance for survival of organisms in vivo. This review illustrates the differences between frozen and liquid conditions on several small and large biomolecules, together with the synthetic use of freezing. In relation to the freezing effect on enzyme activity, a peculiar phenomenon is discussed: 'cryo-oscillations' are temporal motions of trypsin activity in frozen solution in the presence of Mn2+ ion. The molecular basis of cold adaptation is also discussed, which points to mechanisms evolved by organisms living at subzero temperatures. The factors involved in the freezing effect are shown; i.e. the role of freeze-concentration and frozen solvent surface is demonstrated and elucidated using several examples. PMID:11212294

  18. A physical interaction network of dengue virus and human proteins.

    PubMed

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  19. Network-level molecular evolutionary analysis of the insulin/TOR signal transduction pathway across 12 Drosophila genomes

    PubMed Central

    Alvarez-Ponce, David; Aguadé, Montserrat; Rozas, Julio

    2009-01-01

    Biological function is based on complex networks consisting of large numbers of interacting molecules. The evolutionary properties of molecular networks and, in particular, the impact of network architecture on the sequence evolution of its individual components are, nonetheless, still poorly understood. Here, we conducted a fine-scale network-level molecular evolutionary analysis of the insulin/TOR pathway across 12 species of Drosophila. We found that the insulin/TOR pathway components are completely conserved across these species and that two genes located at major network branch points show evidence for positive selection. Remarkably, we detected a gradient in the strength of purifying selection along the pathway, increasing from the upstream to the downstream genes. We also found that physically interacting proteins tend to have more similar levels of selective constraint, even though this feature might represent a byproduct of the correlation between selective constraint and the pathway position. Our results clearly indicate that the levels of functional constraint do depend on the position of the proteins in the pathway and, consequently, the architecture of the pathway constrains gene sequence evolution. PMID:19141596

  20. Postiive tone resists based on network deploymerization of molecular resists

    NASA Astrophysics Data System (ADS)

    Lawson, Richard A.; Cheng, Jing; Cheshmehkani, Ameneh; Tolbert, Laren M.; Henderson, Clifford L.

    2013-03-01

    Conventional chemically amplified resists have several issues that can potentially limit their capability for sub-40 nm imaging. One of the major issues at this size scale is that the mechanical strength of positive tone CARs limits the amount of stress they can withstand during development, rinse, and drying, thus leading to problems with pattern collapse due to the high capillary forces generated during drying. This problem is exasperated by the fact that linear polymers show dramatically reduced modulus at sub-50 nm features sizes. To improve on this problem, we have made a positive tone resist based on network depolymerization of molecular resists. The resist thermally cross-links after being spin cast into thin film form through reactions between vinyl ether groups and carboxylic acid groups. By cross-linking the resist to form a dense three dimensional polymer network, the mechanical strength of the resist is greatly improved compared to linear polymers. The network is depolymerized using an acid catalyzed reaction to create development contrast that allows for patterning of the resist via development in either aqueous base or organic solvent. One drawback of the current resist design is that the free carboxylic acids on the resist molecule appear to react in solution at room temperature with both the vinyl ether groups on adjacent molecules and with any added base quencher. These reactions cause reduced effectiveness of the base quencher and produce a noticeable resist shelf life problem. Despite these limitations, the material was used to compare the effect of development in aqueous base versus organic solvent. The resist formulated in this work showed a DUV sensitivity of 7 mJ/cm2 and a contrast of 5.2 for development in either solvent or aqueous base. Under 100 keV e-beam imaging, the material showed 40 nm resolution for both development types. In standard 0.26 N TMAH, the dose-to-size was 84 μC/cm2 with 3σ LER of 14.2 nm. Using methyl isobutyl ketone

  1. Structural Refinement of Proteins by Restrained Molecular Dynamics Simulations with Non-interacting Molecular Fragments

    PubMed Central

    Shen, Rong; Han, Wei; Fiorin, Giacomo; Islam, Shahidul M.; Schulten, Klaus; Roux, Benoît

    2015-01-01

    The knowledge of multiple conformational states is a prerequisite to understand the function of membrane transport proteins. Unfortunately, the determination of detailed atomic structures for all these functionally important conformational states with conventional high-resolution approaches is often difficult and unsuccessful. In some cases, biophysical and biochemical approaches can provide important complementary structural information that can be exploited with the help of advanced computational methods to derive structural models of specific conformational states. In particular, functional and spectroscopic measurements in combination with site-directed mutations constitute one important source of information to obtain these mixed-resolution structural models. A very common problem with this strategy, however, is the difficulty to simultaneously integrate all the information from multiple independent experiments involving different mutations or chemical labels to derive a unique structural model consistent with the data. To resolve this issue, a novel restrained molecular dynamics structural refinement method is developed to simultaneously incorporate multiple experimentally determined constraints (e.g., engineered metal bridges or spin-labels), each treated as an individual molecular fragment with all atomic details. The internal structure of each of the molecular fragments is treated realistically, while there is no interaction between different molecular fragments to avoid unphysical steric clashes. The information from all the molecular fragments is exploited simultaneously to constrain the backbone to refine a three-dimensional model of the conformational state of the protein. The method is illustrated by refining the structure of the voltage-sensing domain (VSD) of the Kv1.2 potassium channel in the resting state and by exploring the distance histograms between spin-labels attached to T4 lysozyme. The resulting VSD structures are in good agreement with

  2. Structural Refinement of Proteins by Restrained Molecular Dynamics Simulations with Non-interacting Molecular Fragments.

    PubMed

    Shen, Rong; Han, Wei; Fiorin, Giacomo; Islam, Shahidul M; Schulten, Klaus; Roux, Benoît

    2015-10-01

    The knowledge of multiple conformational states is a prerequisite to understand the function of membrane transport proteins. Unfortunately, the determination of detailed atomic structures for all these functionally important conformational states with conventional high-resolution approaches is often difficult and unsuccessful. In some cases, biophysical and biochemical approaches can provide important complementary structural information that can be exploited with the help of advanced computational methods to derive structural models of specific conformational states. In particular, functional and spectroscopic measurements in combination with site-directed mutations constitute one important source of information to obtain these mixed-resolution structural models. A very common problem with this strategy, however, is the difficulty to simultaneously integrate all the information from multiple independent experiments involving different mutations or chemical labels to derive a unique structural model consistent with the data. To resolve this issue, a novel restrained molecular dynamics structural refinement method is developed to simultaneously incorporate multiple experimentally determined constraints (e.g., engineered metal bridges or spin-labels), each treated as an individual molecular fragment with all atomic details. The internal structure of each of the molecular fragments is treated realistically, while there is no interaction between different molecular fragments to avoid unphysical steric clashes. The information from all the molecular fragments is exploited simultaneously to constrain the backbone to refine a three-dimensional model of the conformational state of the protein. The method is illustrated by refining the structure of the voltage-sensing domain (VSD) of the Kv1.2 potassium channel in the resting state and by exploring the distance histograms between spin-labels attached to T4 lysozyme. The resulting VSD structures are in good agreement with

  3. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

    PubMed Central

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

    2016-01-01

    Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  4. The Bilingual Language Interaction Network for Comprehension of Speech*

    PubMed Central

    Marian, Viorica

    2013-01-01

    During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

  5. The Bilingual Language Interaction Network for Comprehension of Speech.

    PubMed

    Shook, Anthony; Marian, Viorica

    2013-04-01

    During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

  6. Visualization of protein interaction networks: problems and solutions

    PubMed Central

    2013-01-01

    Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to

  7. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    PubMed Central

    Chu, Liang-Hui; Chen, Bor-Sen

    2008-01-01

    Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs. PMID:18590547

  8. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  9. Molecular interactions in the priming complex of bacteriophage T7

    PubMed Central

    Kulczyk, Arkadiusz W.; Richardson, Charles C.

    2012-01-01

    The lagging-strand DNA polymerase requires an oligoribonucleotide, synthesized by DNA primase, to initiate the synthesis of an Okazaki fragment. In the replication system of bacteriophage T7 both DNA primase and DNA helicase activities are contained within a single protein, the bifunctional gene 4 protein (gp4). Intermolecular interactions between gp4 and T7 DNA polymerase are crucial for the stabilization of the oligoribonucleotide, its transfer to the polymerase, and its extension by DNA polymerase. We have identified conditions necessary to assemble the T7 priming complex and characterized its biophysical properties using fluorescence anisotropy. In order to reveal molecular interactions that occur during delivery of the oligoribonucleotide to DNA polymerase, we have used four genetically altered gp4 to demonstrate that both the RNA polymerase and the zinc-finger domains of DNA primase are involved in the stabilization of the priming complex and in sequence recognition in the DNA template. We find that the helicase domain of gp4 contributes to the stability of the complex by binding to the ssDNA template. The C-terminal tail of gp4 is not required for complex formation. PMID:22645372

  10. Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions

    PubMed Central

    Scholes, Natalie S.; Weinzierl, Robert O. J.

    2016-01-01

    Transcriptional activation domains (ADs) are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD) simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bound to the GAL11 coactivator surface. We show that the AD-coactivator interactions are highly dynamic while obeying distinct rules. The data provide insights into the constant and variable aspects of orientation of ADs relative to the coactivator, changes in secondary structure and energetic contributions stabilizing the various conformers at different time points. We also demonstrate that a prediction of α-helical propensity correlates directly with the experimentally measured transactivation potential of a large set of mutagenized ADs. The link between α-helical propensity and the stimulatory activity of ADs has fundamental practical and theoretical implications concerning the recruitment of ADs to coactivators. PMID:27175900

  11. Molecular interactions between (-)-epigallocatechin gallate analogs and pancreatic lipase.

    PubMed

    Wang, Shihui; Sun, Zeya; Dong, Shengzhao; Liu, Yang; Liu, Yun

    2014-01-01

    The molecular interactions between pancreatic lipase (PL) and four tea polyphenols (EGCG analogs), like (-)-epigallocatechin gallate (EGCG), (-)-gallocatechin gallate (GCG), (-)-epicatechin gallate (ECG), and (-)-epigallocatechin (EC), were studied from PL activity, conformation, kinetics and thermodynamics. It was observed that EGCG analogs inhibited PL activity, and their inhibitory rates decreased by the order of EGCG>GCG>ECG>EC. PL activity at first decreased rapidly and then slowly with the increase of EGCG analogs concentrations. α-Helix content of PL secondary structure decreased dependent on EGCG analogs concentration by the order of EGCG>GCG>ECG>EC. EGCG, ECG, and EC could quench PL fluorescence both dynamically and statically, while GCG only quenched statically. EGCG analogs would induce PL self-assembly into complexes and the hydrodynamic radii of the complexes possessed a close relationship with the inhibitory rates. Kinetics analysis showed that EGCG analogs non-competitively inhibited PL activity and did not bind to PL catalytic site. DSC measurement revealed that EGCG analogs decreased the transition midpoint temperature of PL enzyme, suggesting that these compounds reduced PL enzyme thermostability. In vitro renaturation through urea solution indicated that interactions between PL and EGCG analogs were weak and non-covalent. PMID:25365042

  12. Developing a Molecular Roadmap of Drug-Food Interactions

    PubMed Central

    Jensen, Kasper; Ni, Yueqiong; Panagiotou, Gianni; Kouskoumvekaki, Irene

    2015-01-01

    Recent research has demonstrated that consumption of food -especially fruits and vegetables- can alter the effects of drugs by interfering either with their pharmacokinetic or pharmacodynamic processes. Despite the recognition of such drug-food associations as an important element for successful therapeutic interventions, a systematic approach for identifying, predicting and preventing potential interactions between food and marketed or novel drugs is not yet available. The overall objective of this work was to sketch a comprehensive picture of the interference of ∼ 4,000 dietary components present in ∼1800 plant-based foods with the pharmacokinetics and pharmacodynamics processes of medicine, with the purpose of elucidating the molecular mechanisms involved. By employing a systems chemical biology approach that integrates data from the scientific literature and online databases, we gained a global view of the associations between diet and dietary molecules with drug targets, metabolic enzymes, drug transporters and carriers currently deposited in DrugBank. Moreover, we identified disease areas and drug targets that are most prone to the negative effects of drug-food interactions, showcasing a platform for making recommendations in relation to foods that should be avoided under certain medications. Lastly, by investigating the correlation of gene expression signatures of foods and drugs we were able to generate a completely novel drug-diet interactome map. PMID:25668218

  13. Interactions of arabinoxylan and (1,3)(1,4)-β-glucan with cellulose networks.

    PubMed

    Mikkelsen, Deirdre; Flanagan, Bernadine M; Wilson, Sarah M; Bacic, Antony; Gidley, Michael J

    2015-04-13

    To identify interactions of relevance to the structure and properties of the primary cell walls of cereals and grasses, we used arabinoxylan and (1,3)(1,4)-β-glucan, major polymers in cereal/grass primary cell walls, to construct composites with cellulose produced by Gluconacetobacter xylinus. Both polymers associated prolifically with cellulose without becoming rigid or altering the nature or extent of cellulose crystallinity. Mechanical properties were modestly affected compared with xyloglucan or pectin (characteristic components of nongrass primary cell walls) composites with cellulose. In situ depletion of arabinoxylan arabinose side chains within preformed cellulose composites resulted in phase separation, with only limited enhancement of xylan-cellulose interactions. These results suggest that arabinoxylan and (1 → 3)(1 → 4)-β-d-glucan are not functional homologues for either xyloglucan or pectin in the way they interact with cellulose networks. Association of cell-wall polymers with cellulose driven by entropic amelioration of high energy cellulose/water interfaces should be considered as a third type of interaction within cellulose-based cell walls, in addition to molecular binding (enthalpic driving force) exhibited by, for example, xyloglucans or mannans, and interpenetrating networks based on, for example, pectins. PMID:25756836

  14. Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure.

    PubMed

    Ansari-Pour, Naser; Razaghi-Moghadam, Zahra; Barneh, Farnaz; Jafari, Mohieddin

    2016-03-01

    Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.

  15. Multitargeting by curcumin as revealed by molecular interaction studies

    PubMed Central

    Gupta, Subash C.; Prasad, Sahdeo; Kim, Ji Hye; Patchva, Sridevi; Webb, Lauren J.; Priyadarsini, Indira K.

    2012-01-01

    Curcumin (diferuloylmethane), the active ingredient in turmeric (Curcuma longa), is a highly pleiotropic molecule with anti-inflammatory, anti-oxidant, chemopreventive, chemosensitization, and radiosensitization activities. The pleiotropic activities attributed to curcumin come from its complex molecular structure and chemistry, as well as its ability to influence multiple signaling molecules. Curcumin has been shown to bind by multiple forces directly to numerous signaling molecules, such as inflammatory molecules, cell survival proteins, protein kinases, protein reductases, histone acetyltransferase, histone deacetylase, glyoxalase I, xanthine oxidase, proteasome, HIV1 integrase, HIV1 protease, sarco (endo) plasmic reticulum Ca2+ ATPase, DNA methyltransferases 1, FtsZ protofilaments, carrier proteins, and metal ions. Curcumin can also bind directly to DNA and RNA. Owing to its β-diketone moiety, curcumin undergoes keto–enol tautomerism that has been reported as a favorable state for direct binding. The functional groups on curcumin found suitable for interaction with other macromolecules include the α, β-unsaturated β-diketone moiety, carbonyl and enolic groups of the β-diketone moiety, methoxy and phenolic hydroxyl groups, and the phenyl rings. Various biophysical tools have been used to monitor direct interaction of curcumin with other proteins, including absorption, fluorescence, Fourier transform infrared (FTIR) and circular dichroism (CD) spectroscopy, surface plasmon resonance, competitive ligand binding, Forster type fluorescence resonance energy transfer (FRET), radiolabeling, site-directed mutagenesis, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), immunoprecipitation, phage display biopanning, electron microscopy, 1-anilino-8-naphthalene-sulfonate (ANS) displacement, and co-localization. Molecular docking, the most commonly employed computational tool for calculating binding affinities and predicting

  16. Insights into the molecular interactions between aminopeptidase and amyloid beta peptide using molecular modeling techniques.

    PubMed

    Dhanavade, Maruti J; Sonawane, Kailas D

    2014-08-01

    Amyloid beta (Aβ) peptides play a central role in the pathogenesis of Alzheimer's disease. The accumulation of Aβ peptides in AD brain was caused due to overproduction or insufficient clearance and defects in the proteolytic degradation of Aβ peptides. Hence, Aβ peptide degradation could be a promising therapeutic approach in AD treatment. Recent experimental report suggests that aminopeptidase from Streptomyces griseus KK565 (SGAK) can degrade Aβ peptides but the interactive residues are yet to be known in detail at the atomic level. Hence, we developed the three-dimensional model of aminopeptidase (SGAK) using SWISS-MODEL, Geno3D and MODELLER. Model built by MODELLER was used for further studies. Molecular docking was performed between aminopeptidase (SGAK) with wild-type and mutated Aβ peptides. The docked complex of aminopeptidase (SGAK) and wild-type Aβ peptide (1IYT.pdb) shows more stability than the other complexes. Molecular docking and MD simulation results revealed that the residues His93, Asp105, Glu139, Glu140, Asp168 and His255 are involved in the hydrogen bonding with Aβ peptide and zinc ions. The interactions between carboxyl oxygen atoms of Glu139 of aminopeptidase (SGAK) with water molecule suggest that the Glu139 may be involved in the nucleophilic attack on Ala2-Glu3 peptide bond of Aβ peptide. Hence, amino acid Glu139 of aminopeptidase (SGAK) might play an important role to degrade Aβ peptides, a causative agent of Alzheimer's disease.

  17. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    NASA Astrophysics Data System (ADS)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  18. Molecular structure of monomorphic peptide fibrils within a kinetically trapped hydrogel network

    PubMed Central

    Nagy-Smith, Katelyn; Moore, Eric; Schneider, Joel; Tycko, Robert

    2015-01-01

    Most, if not all, peptide- and protein-based hydrogels formed by self-assembly can be characterized as kinetically trapped 3D networks of fibrils. The propensity of disease-associated amyloid-forming peptides and proteins to assemble into polymorphic fibrils suggests that cross-β fibrils comprising hydrogels may also be polymorphic. We use solid-state NMR to determine the molecular and supramolecular structure of MAX1, a de novo designed gel-forming peptide, in its fibrillar state. We find that MAX1 adopts a β-hairpin conformation and self-assembles with high fidelity into a double-layered cross-β structure. Hairpins assemble with an in-register Syn orientation within each β-sheet layer and with an Anti orientation between layers. Surprisingly, although the MAX1 fibril network is kinetically trapped, solid-state NMR data show that fibrils within this network are monomorphic and most likely represent the thermodynamic ground state. Intermolecular interactions not available in alternative structural arrangements apparently dictate this monomorphic behavior. PMID:26216960

  19. Phospho-tyrosine dependent protein–protein interaction network

    PubMed Central

    Grossmann, Arndt; Benlasfer, Nouhad; Birth, Petra; Hegele, Anna; Wachsmuth, Franziska; Apelt, Luise; Stelzl, Ulrich

    2015-01-01

    Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes. PMID:25814554

  20. Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

    PubMed

    Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

    2016-01-01

    This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information. PMID:27440295

  1. Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics.

    PubMed

    Karthikeyan, Bagavathy Shanmugam; Akbarsha, Mohammad Abdulkader; Parthasarathy, Subbiah

    2016-06-21

    Cytochrome P450 (CYP) enzymes that degrade xenobiotics play a critical role in the metabolism and biotransformation of drugs and xenobiotics in humans as well as experimental animal models such as mouse and rat. These proteins function as a network collectively as well as independently. Though there are several reports on the organization, regulation and functionality of various CYP enzymes at the molecular level, the understanding of organization and functionality of these proteins at the holistic level remain unclear. The objective of this study is to understand the organization and functionality of xenobiotic degrading CYP enzymes of human, mouse and rat using network theory approaches and to study species differences that exist among them at the holistic level. For our analysis, a protein-protein interaction (PPI) network for CYP enzymes of human, mouse and rat was constructed using the STRING database. Topology, centrality, modularity and robustness analyses were performed for our predicted CYP PPI networks that were then validated by comparison with randomly generated network models. Network centrality analyses of CYP PPI networks reveal the central/hub proteins in the network. Modular analysis of the CYP PPI networks of human, mouse and rat resulted in functional clusters. These clusters were subjected to ontology and pathway enrichment analysis. The analyses show that the cluster of the human CYP PPI network is enriched with pathways principally related to xenobiotic/drug metabolism. Endo-xenobiotic crosstalk dominated in mouse and rat CYP PPI networks, and they were highly enriched with endogenous metabolic and signaling pathways. Thus, cross-species comparisons and analyses of human, mouse and rat CYP PPI networks gave insights about species differences that existed at the holistic level. More investigations from both reductionist and holistic perspectives can help understand CYP metabolism and species extrapolation in a much better way. PMID:27194593

  2. A DNA Methylation Network Interaction Measure, and Detection of Network Oncomarkers

    PubMed Central

    Bartlett, Thomas E.; Olhede, Sofia C.; Zaikin, Alexey

    2014-01-01

    Epigenetic processes–including DNA methylation–are increasingly seen as having a fundamental role in chronic diseases like cancer. DNA methylation patterns offer a route to develop prognostic measures based directly on DNA measurements, rather than less-stable RNA measurements. A novel DNA methylation-based measure of the co-ordinated interactive behaviour of genes is developed, in a network context. It is shown that this measure reflects well the co-regulatory behaviour linked to gene expression (at the mRNA level) over the same network interactions. This measure, defined for pairs of genes in a single patient/sample, associates with overall survival outcome independent of known prognostic clinical features, in several independent data sets relating to different cancer types. In total, more than half a billion CpGs in over 1600 samples, taken from nine different cancer entities, are analysed. It is found that groups of gene-pair interactions which associate significantly with survival identify statistically significant subnetwork modules. Many of these subnetwork modules are shown to be biologically relevant by strong correlation with pre-defined gene sets, such as immune function, wound healing, mitochondrial function and MAP-kinase signalling. In particular, the wound healing module corresponds to an increase in co-ordinated interactive behaviour between genes for worse prognosis, and the immune module corresponds to a decrease in co-ordinated interactive behaviour between genes for worse prognosis. This measure has great potential for defining DNA-based cancer biomarkers. Such biomarkers could naturally be developed further, by drawing on the rapidly expanding knowledge base of network science. PMID:24400102

  3. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    ERIC Educational Resources Information Center

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  4. Topology of Protein Interaction Network Shapes Protein Abundances and Strengths of Their Functional and Nonspecific Interactions

    SciTech Connect

    Maslov, S.; Heo, M.; Shakhnovich, E.

    2011-03-08

    How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a 'frustration' effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.

  5. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.

    PubMed

    Yan, Xiao-Ying; Zhang, Shao-Wu; Zhang, Song-Yao

    2016-02-01

    The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions. PMID

  6. Ranking of Molecular Biomarker Interaction with Targeted DNA Nucleobases via Full Atomistic Molecular Dynamics

    PubMed Central

    Zhang, Wenjun; Wang, Ming L.; Cranford, Steven W.

    2016-01-01

    DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring. PMID:26750747

  7. Interpretation of Association Behavior and Molecular Interactions in Binary Mixtures from Thermoacoustics and Molecular Compression Data

    NASA Astrophysics Data System (ADS)

    Shukla, Rajeev K.; Kumar, Atul; Srivastava, Urvashi; Srivastava, Kirti; Pandey, Vivek K.

    2016-09-01

    Density and acoustic velocity were measured for binary liquid mixtures of formamide, N-methylacetamide (NMA), dimethylformamide (DMF), and dimethylacetamide (DMA) with acetonitrile at atmospheric pressure and 293.15 K, 298.15 K, 303.15 K, 308.15 K, or 313.15 K over the concentration range 0.12 to 0.97. Models assuming association and nonassociation of the components of the mixtures were used to predict the behavior of the studied liquids, which would typically show weak interactions. The measured properties were fitted to the Redlich-Kister polynomial to estimate the binary coefficients and standard errors. The data were used to study the molecular interactions in the binary mixtures. Furthermore, the McAllister multibody interaction model was used to correlate the properties of the binary liquid mixtures. Testing of the nonassociation and association models for the different systems showed that, compared with the nonassociation model theoretical results, the association model theoretical results were more consistent with the experimental results.

  8. Ranking of Molecular Biomarker Interaction with Targeted DNA Nucleobases via Full Atomistic Molecular Dynamics

    NASA Astrophysics Data System (ADS)

    Zhang, Wenjun; Wang, Ming L.; Cranford, Steven W.

    2016-01-01

    DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring.

  9. Genotoxicants Target Distinct Molecular Networks in Neonatal Neurons

    PubMed Central

    Kisby, Glen E.; Olivas, Antoinette; Standley, Melissa; Lu, Xinfang; Pattee, Patrick; O’Malley, Jean; Li, Xiaorong; Muniz, Juan; Nagalla, Srinavasa R.

    2006-01-01

    Background Exposure of the brain to environmental agents during critical periods of neuronal development is considered a key factor underlying many neurologic disorders. Objectives In this study we examined the influence of genotoxicants on cerebellar function during early development by measuring global gene expression changes. Methods We measured global gene expression in immature cerebellar neurons (i.e., granule cells) after treatment with two distinct alkylating agents, methylazoxymethanol (MAM) and nitrogen mustard (HN2). Granule cell cultures were treated for 24 hr with MAM (10–1,000 μM) or HN2 (0.1–20 μM) and examined for cell viability, DNA damage, and markers of apoptosis. Results Neuronal viability was significantly reduced (p < 0.01) at concentrations > 500 μM for MAM and > 1.0 μM for HN2; this correlated with an increase in both DNA damage and markers of apoptosis. Neuronal cultures treated with sublethal concentrations of MAM (100 μM) or HN2 (1.0 μM) were then examined for gene expression using large-scale mouse cDNA microarrays (27,648). Gene expression results revealed that a) global gene expression was predominantly up-regulated by both genotoxicants; b) the number of down-regulated genes was approximately 3-fold greater for HN2 than for MAM; and c) distinct classes of molecules were influenced by MAM (i.e, neuronal differentiation, the stress and immune response, and signal transduction) and HN2 (i.e, protein synthesis and apoptosis). Conclusions These studies demonstrate that individual genotoxicants induce distinct gene expression signatures. Further study of these molecular networks may explain the variable response of the developing brain to different types of environmental genotoxicants. PMID:17107856

  10. Molecular Ecological Insights into Neotropical Bird-Tick Interactions.

    PubMed

    Miller, Matthew J; Esser, Helen J; Loaiza, Jose R; Herre, Edward Allen; Aguilar, Celestino; Quintero, Diomedes; Alvarez, Eric; Bermingham, Eldredge

    2016-01-01

    In the tropics, ticks parasitize many classes of vertebrate hosts. However, because many tropical tick species are only identifiable in the adult stage, and these adults usually parasitize mammals, most attention on the ecology of tick-host interactions has focused on mammalian hosts. In contrast, immature Neotropical ticks are often found on wild birds, yet difficulties in identifying immatures hinder studies of birds' role in tropical tick ecology and tick-borne disease transmission. In Panama, we found immature ticks on 227 out of 3,498 individually-sampled birds representing 93 host species (24% of the bird species sampled, and 13% of the Panamanian land bird fauna). Tick parasitism rates did not vary with rainfall or temperature, but did vary significantly with several host ecological traits. Likewise, Neotropical-Nearctic migratory birds were significantly less likely to be infested than resident species. Using a molecular library developed from morphologically-identified adult ticks specifically for this study, we identified eleven tick species parasitizing birds, indicating that a substantial portion of the Panamanian avian species pool is parasitized by a diversity of tick species. Tick species that most commonly parasitized birds had the widest diversity of avian hosts, suggesting that immature tick species are opportunistic bird parasites. Although certain avian ecological traits are positively associated with parasitism, we found no evidence that individual tick species show specificity to particular avian host ecological traits. Finally, our data suggest that the four principal vectors of Rocky Mountain Spotted Fever in the Neotropics rarely, if ever, parasitize Panamanian birds. However, other tick species that harbor newly-discovered rickettsial parasites of unknown pathogenicity are frequently found on these birds. Given our discovery of broad interaction between Panamanian tick and avian biodiversity, future work on tick ecology and the dynamics of

  11. Molecular Ecological Insights into Neotropical Bird–Tick Interactions

    PubMed Central

    Esser, Helen J.; Loaiza, Jose R.; Herre, Edward Allen; Aguilar, Celestino; Quintero, Diomedes; Alvarez, Eric; Bermingham, Eldredge

    2016-01-01

    In the tropics, ticks parasitize many classes of vertebrate hosts. However, because many tropical tick species are only identifiable in the adult stage, and these adults usually parasitize mammals, most attention on the ecology of tick-host interactions has focused on mammalian hosts. In contrast, immature Neotropical ticks are often found on wild birds, yet difficulties in identifying immatures hinder studies of birds’ role in tropical tick ecology and tick-borne disease transmission. In Panama, we found immature ticks on 227 out of 3,498 individually–sampled birds representing 93 host species (24% of the bird species sampled, and 13% of the Panamanian land bird fauna). Tick parasitism rates did not vary with rainfall or temperature, but did vary significantly with several host ecological traits. Likewise, Neotropical–Nearctic migratory birds were significantly less likely to be infested than resident species. Using a molecular library developed from morphologically–identified adult ticks specifically for this study, we identified eleven tick species parasitizing birds, indicating that a substantial portion of the Panamanian avian species pool is parasitized by a diversity of tick species. Tick species that most commonly parasitized birds had the widest diversity of avian hosts, suggesting that immature tick species are opportunistic bird parasites. Although certain avian ecological traits are positively associated with parasitism, we found no evidence that individual tick species show specificity to particular avian host ecological traits. Finally, our data suggest that the four principal vectors of Rocky Mountain Spotted Fever in the Neotropics rarely, if ever, parasitize Panamanian birds. However, other tick species that harbor newly–discovered rickettsial parasites of unknown pathogenicity are frequently found on these birds. Given our discovery of broad interaction between Panamanian tick and avian biodiversity, future work on tick ecology and the

  12. Interaction of proteases with legume seed inhibitors. Molecular features.

    PubMed

    de Seidl, D S

    1996-12-01

    After having found that raw black beans (Phaseolus vulgaris) were toxic, while the cooked ones constitute the basic diet of the underdeveloped peoples of the world, in the sixties, our research directed by Dr. Jaffé, concentrated mainly around the detection and identification of the heat labile toxic factors in legume seeds. A micromethod for the detection of protease inhibitors (PI) in individual seeds was developed, for the purpose of establishing that the multiple trypsin inhibitors (TI) found in the Cubagua variety were expressions of single seeds and not a mixture of a non homogenous bean lot. Six isoinhibitors were isolated and purified, all of which were "double-headed" and interacted with trypsin (T) and chymotrypsin (CHT) independently and simultaneously, as shown by electrophoresis of their binary and ternary complexes with each and both enzymes. However, their affinity for the enzymes, including elastases, was rather variable, as well as their amino acid composition which consisted of 51 units for inhibitor V, the smallest, and 83 amino acids for inhibitor I, the largest. A low molecular weight protein fraction that inhibited subtilisin (S), but recognized neither T, CHT nor pancreatic elastase was detected in 63 varieties of Phaseolus vulgaris as well as in broad beans (Vicia faba), chick peas (Cicer arietinum), jack beans (Canavalia ensiformis), kidney beans (Vigna aureus), etc., It was absent though, in soybeans (Glycine max), lentils (Lens culinaris), green peas (Pisum sativum), cowpea (Vigna sinensis) and lupine seeds (Lupinus sp). Subtilisin inhibitors (SI) were isolated from black beans, broad beans, chick peas and jack beans. Their Mr is between 8-9KD and they show a rather high stability in the presence of denaturing agents. They are specific toward microbial proteases, in addition to subtilisins, Carlsberg and BPN', they inhibit the alkaline protease from Tritirachium album (Protease K), from Aspergillus oryzae and one isolated from

  13. Molecular Ecological Insights into Neotropical Bird-Tick Interactions.

    PubMed

    Miller, Matthew J; Esser, Helen J; Loaiza, Jose R; Herre, Edward Allen; Aguilar, Celestino; Quintero, Diomedes; Alvarez, Eric; Bermingham, Eldredge

    2016-01-01

    In the tropics, ticks parasitize many classes of vertebrate hosts. However, because many tropical tick species are only identifiable in the adult stage, and these adults usually parasitize mammals, most attention on the ecology of tick-host interactions has focused on mammalian hosts. In contrast, immature Neotropical ticks are often found on wild birds, yet difficulties in identifying immatures hinder studies of birds' role in tropical tick ecology and tick-borne disease transmission. In Panama, we found immature ticks on 227 out of 3,498 individually-sampled birds representing 93 host species (24% of the bird species sampled, and 13% of the Panamanian land bird fauna). Tick parasitism rates did not vary with rainfall or temperature, but did vary significantly with several host ecological traits. Likewise, Neotropical-Nearctic migratory birds were significantly less likely to be infested than resident species. Using a molecular library developed from morphologically-identified adult ticks specifically for this study, we identified eleven tick species parasitizing birds, indicating that a substantial portion of the Panamanian avian species pool is parasitized by a diversity of tick species. Tick species that most commonly parasitized birds had the widest diversity of avian hosts, suggesting that immature tick species are opportunistic bird parasites. Although certain avian ecological traits are positively associated with parasitism, we found no evidence that individual tick species show specificity to particular avian host ecological traits. Finally, our data suggest that the four principal vectors of Rocky Mountain Spotted Fever in the Neotropics rarely, if ever, parasitize Panamanian birds. However, other tick species that harbor newly-discovered rickettsial parasites of unknown pathogenicity are frequently found on these birds. Given our discovery of broad interaction between Panamanian tick and avian biodiversity, future work on tick ecology and the dynamics of

  14. Molecular interactions of the neuronal GPI-anchored lipocalin Lazarillo.

    PubMed

    Sanchez, Diego; Ortega-Cubero, Sara; Akerström, Bo; Herrera, Macarena; Bastiani, Michael J; Ganfornina, Maria D

    2008-01-01

    Lazarillo, a glycoprotein involved in axon growth and guidance in the grasshopper embryo, is the only member of the lipocalin family that is attached to the cell surface by a GPI anchor. Recently, the study of Lazarillo homologous genes in Drosophila and mouse has revealed new functions in the regulation of lifespan, stress resistance and neurodegeneration. Here we report an analysis of biochemical properties of Lazarillo to gain insight into the molecular basis of its physiological function. Recombinant forms of the grasshopper protein were expressed in two different systems to test: (1) potential binding of several hydrophobic ligands; (2) protein-protein homophilic interactions; and (3) whether interaction with the function-blocking mAb 10E6 interferes with ligand binding. We tested 10 candidate ligands (retinoic acid, heme, bilirubin, biliverdin, ecdysterone, juvenile hormone, farnesol, arachidonic acid, linoleic acid and palmitic acid), and monitored binding using electrophoretic mobility shift, absorbance spectrum, and fluorimetry assays. Our work indicates binding to heme and retinoic acid, resulting in increased electrophoretic mobility, as well as to fatty acids, resulting in multimerization. Retinoic acid and fatty acids binding were confirmed by fluorescence titration, and heme binding was confirmed with absorbance spectrum assays. We demonstrate that Lazarillo oligomerizes in solution and can form clusters in the plasma membrane when expressed and GPI-anchored to the cell surface, however it is unable to mediate cell-cell adhesion. Finally, by ligand-mAb competition experiments we show that ligand-binding alone cannot be the key factor for Lazarillo to perform its function during axonal growth in the grasshopper embryo.

  15. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning.

  16. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning. PMID:27400769

  17. Networks of Host Factors that Interact with NS1 Protein of Influenza A Virus

    PubMed Central

    Thulasi Raman, Sathya N.; Zhou, Yan

    2016-01-01

    Pigs are an important host of influenza A viruses due to their ability to generate reassortant viruses with pandemic potential. NS1 protein of influenza A viruses is a key virulence factor and a major antagonist of innate immune responses. It is also involved in enhancing viral mRNA translation and regulation of virus replication. Being a protein with pleiotropic functions, NS1 has a variety of cellular interaction partners. Hence, studies on swine influenza viruses (SIV) and identification of swine influenza NS1-interacting host proteins is of great interest. Here, we constructed a recombinant SIV carrying a Strep-tag in the NS1 protein and infected primary swine respiratory epithelial cells (SRECs) with this virus. The Strep-tag sequence in the NS1 protein enabled us to purify intact, the NS1 protein and its interacting protein complex specifically. We identified cellular proteins present in the purified complex by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and generated a dataset of these proteins. 445 proteins were identified by LC-MS/MS and among them 192 proteins were selected by setting up a threshold based on MS parameters. The selected proteins were analyzed by bioinformatics and were categorized as belonging to different functional groups including translation, RNA processing, cytoskeleton, innate immunity, and apoptosis. Protein interaction networks were derived using these data and the NS1 interactions with some of the specific host factors were verified by immunoprecipitation. The novel proteins and the networks revealed in our study will be the potential candidates for targeted study of the molecular interaction of NS1 with host proteins, which will provide insights into the identification of new therapeutic targets to control influenza infection and disease pathogenesis. PMID:27199973

  18. MOLECULAR INTERACTION POTENTIALS FOR THE DEVELOPMENT OF STRUCTURE-ACTIVITY RELATIONSHIPS

    EPA Science Inventory

    Abstract
    One reasonable approach to the analysis of the relationships between molecular structure and toxic activity is through the investigation of the forces and intermolecular interactions responsible for chemical toxicity. The interaction between the xenobiotic and the bio...

  19. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    PubMed

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well. PMID:26243825

  20. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    PubMed

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

  1. Modeling attacker-defender interactions in information networks.

    SciTech Connect

    Collins, Michael Joseph

    2010-09-01

    The simplest conceptual model of cybersecurity implicitly views attackers and defenders as acting in isolation from one another: an attacker seeks to penetrate or disrupt a system that has been protected to a given level, while a defender attempts to thwart particular attacks. Such a model also views all non-malicious parties as having the same goal of preventing all attacks. But in fact, attackers and defenders are interacting parts of the same system, and different defenders have their own individual interests: defenders may be willing to accept some risk of successful attack if the cost of defense is too high. We have used game theory to develop models of how non-cooperative but non-malicious players in a network interact when there is a substantial cost associated with effective defensive measures. Although game theory has been applied in this area before, we have introduced some novel aspects of player behavior in our work, including: (1) A model of how players attempt to avoid the costs of defense and force others to assume these costs; (2) A model of how players interact when the cost of defending one node can be shared by other nodes; and (3) A model of the incentives for a defender to choose less expensive, but less effective, defensive actions.

  2. Phage-bacteria interaction network in human oral microbiome.

    PubMed

    Wang, Jinfeng; Gao, Yuan; Zhao, Fangqing

    2016-07-01

    Although increasing knowledge suggests that bacteriophages play important roles in regulating microbial ecosystems, phage-bacteria interaction in human oral cavities remains less understood. Here we performed a metagenomic analysis to explore the composition and variation of oral dsDNA phage populations and potential phage-bacteria interaction. A total of 1,711 contigs assembled with more than 100 Gb shotgun sequencing data were annotated to 104 phages based on their best BLAST matches against the NR database. Bray-Curtis dissimilarities demonstrated that both phage and bacterial composition are highly diverse between periodontally healthy samples but show a trend towards homogenization in diseased gingivae samples. Significantly, according to the CRISPR arrays that record infection relationship between bacteria and phage, we found certain oral phages were able to invade other bacteria besides their putative bacterial hosts. These cross-infective phages were positively correlated with commensal bacteria while were negatively correlated with major periodontal pathogens, suggesting possible connection between these phages and microbial community structure in oral cavities. By characterizing phage-bacteria interaction as networks rather than exclusively pairwise predator-prey relationships, our study provides the first insight into the participation of cross-infective phages in forming human oral microbiota.

  3. Relationship between molecular weight of poly(ethylene)glycol and intermolecular interaction of Taka-amylase A monomers

    NASA Astrophysics Data System (ADS)

    Onuma, Kazuo; Furubayashi, Naoki; Shibata, Fujiko; Kobayashi, Yoshiko; Kaito, Sachiko; Ohnishi, Yuki; Inaka, Koji

    2010-04-01

    Dynamic and static light scattering investigations of Taka-amylase A (TAA) protein monomers were done using solutions containing poly(ethylene)glycol (PEG) with molecular weights of 1500, 4000, 8000, and 20 000. The anomalies observed in a previous study using a weight of 8000, in which the hydrodynamic TAA monomer radius at a zero protein concentration and the molecular weight of the monomers decreased when the PEG concentration was increased, were observed for all four weights. These anomalies became more pronounced as the PEG molecular weight was increased. The overall interaction parameter did not move further in the direction of the attractive force despite an increase in the PEG concentration from 6% to 12.5% for the PEG 8000 and 20 000 solutions. This was due to the change in the relative contributions of the static structure factor (direct interaction) and the hydrodynamic interaction factor (indirect interaction) against the overall interaction parameter. For the PEG 1500 and 4000 solutions, the change in the overall interaction parameter with an increase in the PEG concentration was controlled by changing the static structure factor. For the PEG 8000 and 20 000 solutions, a change in the hydrodynamic interaction factor with an increase in the PEG concentration offset the change in the static structure factor, unexpectedly resulting in the overall interaction parameter being independent of the PEG concentration. This suggests that the scale and density of a PEG network structure, which are thought to be the origin of the observed anomalies, change nonlinearly with the PEG molecular weight.

  4. STRING v10: protein-protein interaction networks, integrated over the tree of life.

    PubMed

    Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan; Forslund, Kristoffer; Heller, Davide; Huerta-Cepas, Jaime; Simonovic, Milan; Roth, Alexander; Santos, Alberto; Tsafou, Kalliopi P; Kuhn, Michael; Bork, Peer; Jensen, Lars J; von Mering, Christian

    2015-01-01

    The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

  5. STRING v10: protein-protein interaction networks, integrated over the tree of life.

    PubMed

    Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan; Forslund, Kristoffer; Heller, Davide; Huerta-Cepas, Jaime; Simonovic, Milan; Roth, Alexander; Santos, Alberto; Tsafou, Kalliopi P; Kuhn, Michael; Bork, Peer; Jensen, Lars J; von Mering, Christian

    2015-01-01

    The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks. PMID:25352553

  6. The evolution and origin of animal Toll-like receptor signaling pathway revealed by network-level molecular evolutionary analyses.

    PubMed

    Song, Xiaojun; Jin, Ping; Qin, Sheng; Chen, Liming; Ma, Fei

    2012-01-01

    Genes carry out their biological functions through pathways in complex networks consisting of many interacting molecules. Studies on the effect of network architecture on the evolution of individual proteins will provide valuable information for understanding the origin and evolution as well as functional conservation of signaling pathways. However, the relationship between the network architecture and the individual protein sequence evolution is yet little known. In current study, we carried out network-level molecular evolution analysis on TLR (Toll-like receptor ) signaling pathway, which plays an important role in innate immunity in insects and mammals, and we found that: 1) The selection constraint of genes was negatively correlated with its position along TLR signaling pathway; 2) all genes in TLR signaling pathway were highly conserved and underwent strong purifying selection; 3) the distribution of selective pressure along the pathway was driven by differential nonsynonymous substitution levels; 4) The TLR signaling pathway might present in a common ancestor of sponges and eumetazoa, and evolve via the TLR, IKK, IκB and NF-κB genes underwent duplication events as well as adaptor molecular enlargement, and gene structure and conservation motif of NF-κB genes shifted in their evolutionary history. Our results will improve our understanding on the evolutionary history of animal TLR signaling pathway as well as the relationship between the network architecture and the sequences evolution of individual protein.

  7. The Evolution and Origin of Animal Toll-Like Receptor Signaling Pathway Revealed by Network-Level Molecular Evolutionary Analyses

    PubMed Central

    Qin, Sheng; Chen, Liming; Ma, Fei

    2012-01-01

    Genes carry out their biological functions through pathways in complex networks consisting of many interacting molecules. Studies on the effect of network architecture on the evolution of individual proteins will provide valuable information for understanding the origin and evolution as well as functional conservation of signaling pathways. However, the relationship between the network architecture and the individual protein sequence evolution is yet little known. In current study, we carried out network-level molecular evolution analysis on TLR (Toll-like receptor ) signaling pathway, which plays an important role in innate immunity in insects and mammals, and we found that: 1) The selection constraint of genes was negatively correlated with its position along TLR signaling pathway; 2) all genes in TLR signaling pathway were highly conserved and underwent strong purifying selection; 3) the distribution of selective pressure along the pathway was driven by differential nonsynonymous substitution levels; 4) The TLR signaling pathway might present in a common ancestor of sponges and eumetazoa, and evolve via the TLR, IKK, IκB and NF-κB genes underwent duplication events as well as adaptor molecular enlargement, and gene structure and conservation motif of NF-κB genes shifted in their evolutionary history. Our results will improve our understanding on the evolutionary history of animal TLR signaling pathway as well as the relationship between the network architecture and the sequences evolution of individual protein. PMID:23236523

  8. Crop epigenetics and the molecular hardware of genotype × environment interactions

    PubMed Central

    King, Graham J.

    2015-01-01

    Crop plants encounter thermal environments which fluctuate on a diurnal and seasonal basis. Future climate resilient cultivars will need to respond to thermal profiles reflecting more variable conditions, and harness plasticity that involves regulation of epigenetic processes and complex genomic regulatory networks. Compartmentalization within plant cells insulates the genomic central processing unit within the interphase nucleus. This review addresses the properties of the chromatin hardware in which the genome is embedded, focusing on the biophysical and thermodynamic properties of DNA, histones and nucleosomes. It explores the consequences of thermal and ionic variation on the biophysical behavior of epigenetic marks such as DNA cytosine methylation (5mC), and histone variants such as H2A.Z, and how these contribute to maintenance of chromatin integrity in the nucleus, while enabling specific subsets of genes to be regulated. Information is drawn from theoretical molecular in vitro studies as well as model and crop plants and incorporates recent insights into the role epigenetic processes play in mediating between environmental signals and genomic regulation. A preliminary speculative framework is outlined, based on the evidence of what appears to be a cohesive set of interactions at molecular, biophysical and electrostatic level between the various components contributing to chromatin conformation and dynamics. It proposes that within plant nuclei, general and localized ionic homeostasis plays an important role in maintaining chromatin conformation, whilst maintaining complex genomic regulation that involves specific patterns of epigenetic marks. More generally, reversible changes in DNA methylation appear to be consistent with the ability of nuclear chromatin to manage variation in external ionic and temperature environment. Whilst tentative, this framework provides scope to develop experimental approaches to understand in greater detail the internal

  9. Interacting Bose gas, the logistic law, and complex networks

    NASA Astrophysics Data System (ADS)

    Sowa, A.

    2015-01-01

    We discuss a mathematical link between the Quantum Statistical Mechanics and the logistic growth and decay processes. It is based on an observation that a certain nonlinear operator evolution equation, which we refer to as the Logistic Operator Equation (LOE), provides an extension of the standard model of noninteracting bosons. We discuss formal solutions (asymptotic formulas) for a special calibration of the LOE, which sets it in the number-theoretic framework. This trick, in the tradition of Julia and Bost-Connes, makes it possible for us to tap into the vast resources of classical mathematics and, in particular, to construct explicit solutions of the LOE via the Dirichlet series. The LOE is applicable to a range of modeling and simulation tasks, from characterization of interacting boson systems to simulation of some complex man-made networks. The theoretical results enable numerical simulations, which, in turn, shed light at the unique complexities of the rich and multifaceted models resulting from the LOE.

  10. Properties of interacting 2D chiral tensor network states

    NASA Astrophysics Data System (ADS)

    Bradlyn, Barry; Dubail, Jerome; Read, Nicholas

    2015-03-01

    In a recent paper, Dubail and Read gave a construction for free fermion tensor network states(TNSs) in the chiral p + ip and ν = 1 Chern insulator topological phases in two dimensions, and gave a generalization to Laughlin-like states. However, on general principles these free fermion states must be ground states of gapless local Hamiltonians. In this talk, we address the issue of the energy gap in the interacting states, with a particular focus on the ν = 1 / 2 bosonic Laughlin-like TNS. Through a combination of analytic and numerical arguments, we will show that these states too have gapless local parent Hamiltonians. Nevertheless, we will explore to what degree they can be used as numerical approximations to gapped phases.

  11. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  12. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  13. Ochratoxin A: Molecular Interactions, Mechanisms of Toxicity and Prevention at the Molecular Level.

    PubMed

    Kőszegi, Tamás; Poór, Miklós

    2016-04-01

    Ochratoxin A (OTA) is a widely-spread mycotoxin all over the world causing major health risks. The focus of the present review is on the molecular and cellular interactions of OTA. In order to get better insight into the mechanism of its toxicity and on the several attempts made for prevention or attenuation of its toxic action, a detailed description is given on chemistry and toxicokinetics of this mycotoxin. The mode of action of OTA is not clearly understood yet, and seems to be very complex. Inhibition of protein synthesis and energy production, induction of oxidative stress, DNA adduct formation, as well as apoptosis/necrosis and cell cycle arrest are possibly involved in its toxic action. Since OTA binds very strongly to human and animal albumin, a major emphasis is done regarding OTA-albumin interaction. Displacement of OTA from albumin by drugs and by natural flavonoids are discussed in detail, hypothesizing their potentially beneficial effect in order to prevent or attenuate the OTA-induced toxic consequences. PMID:27092524

  14. Ochratoxin A: Molecular Interactions, Mechanisms of Toxicity and Prevention at the Molecular Level

    PubMed Central

    Kőszegi, Tamás; Poór, Miklós

    2016-01-01

    Ochratoxin A (OTA) is a widely-spread mycotoxin all over the world causing major health risks. The focus of the present review is on the molecular and cellular interactions of OTA. In order to get better insight into the mechanism of its toxicity and on the several attempts made for prevention or attenuation of its toxic action, a detailed description is given on chemistry and toxicokinetics of this mycotoxin. The mode of action of OTA is not clearly understood yet, and seems to be very complex. Inhibition of protein synthesis and energy production, induction of oxidative stress, DNA adduct formation, as well as apoptosis/necrosis and cell cycle arrest are possibly involved in its toxic action. Since OTA binds very strongly to human and animal albumin, a major emphasis is done regarding OTA-albumin interaction. Displacement of OTA from albumin by drugs and by natural flavonoids are discussed in detail, hypothesizing their potentially beneficial effect in order to prevent or attenuate the OTA-induced toxic consequences. PMID:27092524

  15. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  16. Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function

    PubMed Central

    Schwarz, Emanuel; Izmailov, Rauf; Liò, Pietro; Meyer-Lindenberg, Andreas

    2016-01-01

    Schizophrenia is a severe and highly heritable psychiatric disorder affecting approximately 1% of the population. Genome-wide association studies have identified 108 independent genetic loci with genome-wide significance but their functional importance has yet to be elucidated. Here, we develop a novel strategy based on network analysis of protein–protein interactions (PPI) to infer biological function associated with variants most strongly linked to illness risk. We show that the schizophrenia loci are strongly linked to synaptic transmission (P FWE < .001) and ion transmembrane transport (P FWE = .03), but not to ontological categories previously found to be shared across psychiatric illnesses. We demonstrate that brain expression of risk-linked genes within the identified processes is strongly modulated during birth and identify a set of synaptic genes consistently changed across multiple brain regions of adult schizophrenia patients. These results suggest synaptic function as a developmentally determined schizophrenia process supported by the illness’s most associated genetic variants and their PPI networks. The implicated genes may be valuable targets for mechanistic experiments and future drug development approaches. PMID:27056717

  17. Ensemble transcript interaction networks: a case study on Alzheimer's disease.

    PubMed

    Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

    2012-10-01

    Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD.

  18. Efficient quantum transport in disordered interacting many-body networks

    NASA Astrophysics Data System (ADS)

    Ortega, Adrian; Stegmann, Thomas; Benet, Luis

    2016-10-01

    The coherent transport of n fermions in disordered networks of l single-particle states connected by k -body interactions is studied. These networks are modeled by embedded Gaussian random matrix ensemble (EGE). The conductance bandwidth and the ensemble-averaged total current attain their maximal values if the system is highly filled n ˜l -1 and k ˜n /2 . For the cases k =1 and k =n the bandwidth is minimal. We show that for all parameters the transport is enhanced significantly whenever centrosymmetric embedded Gaussian ensemble (csEGE) are considered. In this case the transmission shows numerous resonances of perfect transport. Analyzing the transmission by spectral decomposition, we find that centrosymmetry induces strong correlations and enhances the extrema of the distributions. This suppresses destructive interference effects in the system and thus causes backscattering-free transmission resonances that enhance the overall transport. The distribution of the total current for the csEGE has a very large dominating peak for n =l -1 , close to the highest observed currents.

  19. Pleistocene megafaunal interaction networks became more vulnerable after human arrival.

    PubMed

    Pires, Mathias M; Koch, Paul L; Fariña, Richard A; de Aguiar, Marcus A M; dos Reis, Sérgio F; Guimarães, Paulo R

    2015-09-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions. PMID:26336175

  20. Ensemble transcript interaction networks: a case study on Alzheimer's disease.

    PubMed

    Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

    2012-10-01

    Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD. PMID:22281045

  1. Module organization and variance in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-03-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

  2. Pleistocene megafaunal interaction networks became more vulnerable after human arrival

    PubMed Central

    Pires, Mathias M.; Koch, Paul L.; Fariña, Richard A.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Guimarães, Paulo R.

    2015-01-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions. PMID:26336175

  3. Pleistocene megafaunal interaction networks became more vulnerable after human arrival.

    PubMed

    Pires, Mathias M; Koch, Paul L; Fariña, Richard A; de Aguiar, Marcus A M; dos Reis, Sérgio F; Guimarães, Paulo R

    2015-09-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions.

  4. Inferring causal molecular networks: empirical assessment through a community-based effort.

    PubMed

    Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

    2016-04-01

    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

  5. Inferring causal molecular networks: empirical assessment through a community-based effort.

    PubMed

    Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

    2016-04-01

    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense. PMID:26901648

  6. Molecular Interaction between Magainin 2 and Model Membranes in Situ

    PubMed Central

    Nguyen, Khoi; Le Clair, Stéphanie V.; Ye, Shuji; Chen, Zhan

    2009-01-01

    In this paper, we investigated the molecular interactions of Magainin 2 with model cell membranes using Sum Frequency Generation (SFG) vibrational spectroscopy and Attenuated Total Reflectance – Fourier Transform Infrared spectroscopy (ATR-FTIR). Symmetric 1-Palmitoyl-2-Oleoyl-sn-Glycero-3-[Phospho-rac-(1-glycerol)] (POPG) and 1-Palmitoyl-2-Oleoyl-sn-Glycero-3-Phosphocholine (POPC) bilayers, which model the bacterial and mammalian cell membranes respectively, were used in the studies. It was observed by SFG that Magainin 2 orients relatively parallel to the POPG lipid bilayer surface at low solution concentrations, around 200 nM. When increasing the Magainin 2 concentration to 800 nM, both SFG and ATR-FTIR results indicate that Magainin 2 molecules insert into the POPG bilayer and adopt a transmembrane orientation with an angle of about 20 degrees from the POPG bilayer normal. For the POPC bilayer, even at a much higher peptide concentration of 2.0 µM, no ATR-FTIR signal was detected. For this concentration on POPC, SFG studies indicated that Magainin 2 molecules adopt an orientation nearly parallel to the bilayer surface, with an orientation angle of 75 degrees from the surface normal. This shows that SFG has a much better detection limit than ATR-FTIR and can therefore be applied to study interfacial molecules with much lower surface coverage. This Magainin 2 orientation study and further investigation of the lipid bilayer SFG signals support the proposed toroidal pore model for the antimicrobial activity of Magainin 2. PMID:19728722

  7. Understanding Molecular Interactions within Chemically Selective Layered Polymer Assemblies

    SciTech Connect

    Gary J. Blanchard

    2009-06-30

    This work focuses on two broad issues. These are (1) the molecular origin of the chemical selectivity achieved with ultrathin polymer multilayers, and (2) how the viscoelastic properties of the polymer layers are affected by exposure to solvent and analytes. These issues are inter-related, and to understand them we need to design experiments that probe both the energetic and kinetic aspects of interfacial adsorption processes. This project focuses on controling the chemical structure, thickness, morphology and sequential ordering of polymer layers bound to interfaces using maleimide-vinyl ether and closely related alternating copolymerization chemistry and efficient covalent cross-linking reactions that allow for layer-by-layer polymer deposition. This chemistry has been developed during the funding cycle of this Grant. We have measure the equilibrium constants for interactions between specific layers within the polymer interfaces and size-controlled, surface-functionalized gold nanoparticles. The ability to control both size and functionality of gold nanoparticle model analytes allows us to evaluate the average “pore size” that characterizes our polymer films. We have measured the “bulk” viscosity and shear modulus of the ultrathin polymer films as a function of solvent overlayer identity using quartz crystal microbalance complex impedance measurements. We have measured microscopic viscosity at specific locations within the layered polymer interfaces with time-resolved fluorescence lifetime and depolarization techniques. We combine polymer, cross-linking and nanoparticle synthetic expertise with a host of characterization techniques, including QCM gravimetry and complex impedance analysis, steady state and time-resolved spectroscopies.

  8. Raman probing of molecular interactions of alginate biopolymers with cells

    NASA Astrophysics Data System (ADS)

    Chourpa, Igor; Carpentier, Philippe; Maingault, Philippe; Fetissoff, Franck; Dubois, Pierre

    2000-05-01

    The biological polymers extracted from brown algae, alginates, are novel materials in biotechnology and biomedicine. Their ability to form viscous gels is used to immobilize or encapsulate yeast, enzymes, living cells and drugs. Calcium-alginate fibers are extensively used in wound dressings since exhibit antihaemostatic and healing properties. The problem with alginate-made dressings in surgery is their slow biodegradability: if entrapped within tissues, they can induce a local cellular recruitment with an inflammatory response contemporaneous to the resorption phase. In part, this problem is a consequence of poor solubility of the calcium alginates in water. Although calcium alginate fibers can exchange calcium ions with sodium ions from the wound exudate to create a calcium/sodium alginate fibers, the residual alginates are thought to be not totally degradable in vivo. Rapid and non- destructive characterization of series of the crude alginates and calcium alginate fibers has been performed using Raman spectroscopy with near IR excitation. Study of structural organization of the polymeric chains within calcium alginate fibers have been previously reported as made by confocal Raman multispectral imaging (CRMSI) in visible. Here, the Raman approach has been used to monitor the ion exchange reactions for different types of alginates and their salts in vitro. For in vivo evaluation, histological sections of alginate-treated rat tissue have been analyzed by light microscopy and CRMSI. The in vitro Raman modeling and the histochemical mapping were a necessary precursor for application of the Raman microprobe to follow in a non-invasive way the alginate-cell molecular interactions in rat tissue.

  9. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.

  10. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network.

    PubMed

    Hu, Yang; Zhang, Ying; Ren, Jun; Wang, Yadong; Wang, Zhenzhen; Zhang, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  11. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  12. Nanovoid formation and mechanics: a comparison of poly(dicyclopentadiene) and epoxy networks from molecular dynamics simulations.

    PubMed

    Elder, Robert M; Knorr, Daniel B; Andzelm, Jan W; Lenhart, Joseph L; Sirk, Timothy W

    2016-05-11

    Protective equipment in civilian and military applications requires the use of polymer materials that are both stiff and tough over a wide range of strain rates. However, typical structural materials, like tightly cross-linked epoxies, are very brittle. Recent experiments demonstrated that cross-linked poly(dicyclopentadiene) (pDCPD) networks can circumvent this trade-off by providing structural properties such as a high glass transition temperature and glassy modulus, while simultaneously exhibiting excellent toughness and high-rate impact resistance. The greater performance of pDCPD was attributed to more facile plastic deformation and nano-scale void formation, but the chemical and structural mechanisms underlying this response were not clear. Here, we use atomistic molecular dynamics to compare the molecular- and chain-level properties of pDCPD and epoxy networks undergoing high strain rate deformation. We quantify the tensile modulus and yield strength of the networks as well as the prevalence and characteristics of nanovoids that form during deformation. Networks of similar molecular weight between cross-links are compared. Two key molecular-level properties are identified - monomer flexibility and polar chemistry - that influence the behavior of the networks. Increasing monomer flexibility reduces the modulus and yield strength, while strong non-covalent interactions (e.g., hydrogen bonds) that accompany polar moieties provide higher modulus and yield strength. The lack of strong non-covalent interactions in pDCPD was found to account for its lower modulus and yield strength compared to the epoxies. We examine the molecular-level properties of nanovoids, such as shape, alignment, and local stress distribution, as well as the local chemical environment, finding that nanovoid formation and growth are increased by the monomer rigidity but decreased by polar chemistry. As a result, the pDCPD network, which has a stiff chain backbone with nonpolar alkane

  13. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis.

    PubMed

    Gan, Yanxiong; Zheng, Shichao; Baak, Jan P A; Zhao, Silei; Zheng, Yongfeng; Luo, Nini; Liao, Wan; Fu, Chaomei

    2015-11-01

    Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein-protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. PMID:26713275

  14. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

    PubMed Central

    Gan, Yanxiong; Zheng, Shichao; Baak, Jan P.A.; Zhao, Silei; Zheng, Yongfeng; Luo, Nini; Liao, Wan; Fu, Chaomei

    2015-01-01

    Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. PMID:26713275

  15. VirHostNet 2.0: surfing on the web of virus/host molecular interactions data.

    PubMed

    Guirimand, Thibaut; Delmotte, Stéphane; Navratil, Vincent

    2015-01-01

    VirHostNet release 2.0 (http://virhostnet.prabi.fr) is a knowledgebase dedicated to the network-based exploration of virus-host protein-protein interactions. Since the previous VirhostNet release (2009), a second run of manual curation was performed to annotate the new torrent of high-throughput protein-protein interactions data from the literature. This resource is shared publicly, in PSI-MI TAB 2.5 format, using a PSICQUIC web service. The new interface of VirHostNet 2.0 is based on Cytoscape web library and provides a user-friendly access to the most complete and accurate resource of virus-virus and virus-host protein-protein interactions as well as their projection onto their corresponding host cell protein interaction networks. We hope that the VirHostNet 2.0 system will facilitate systems biology and gene-centered analysis of infectious diseases and will help to identify new molecular targets for antiviral drugs design. This resource will also continue to help worldwide scientists to improve our knowledge on molecular mechanisms involved in the antiviral response mediated by the cell and in the viral strategies selected by viruses to hijack the host immune system.

  16. HubAlign: an accurate and efficient method for global alignment of protein–protein interaction networks

    PubMed Central

    Hashemifar, Somaye; Xu, Jinbo

    2014-01-01

    Motivation: High-throughput experimental techniques have produced a large amount of protein–protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency. Results: This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins. Availability: HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip Contact: jinboxu@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161231

  17. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

    PubMed Central

    Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

    2014-01-01

    Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology. PMID:25549968

  18. Opinion dynamics on interacting networks: media competition and social influence.

    PubMed

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

  19. Opinion dynamics on interacting networks: media competition and social influence

    PubMed Central

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

  20. Opinion dynamics on interacting networks: media competition and social influence.

    PubMed

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-27

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  1. Opinion dynamics on interacting networks: media competition and social influence

    NASA Astrophysics Data System (ADS)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-01

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  2. Commentary: Biochemistry and Molecular Biology Educators Launch National Network

    ERIC Educational Resources Information Center

    Bailey, Cheryl; Bell, Ellis; Johnson, Margaret; Mattos, Carla; Sears, Duane; White, Harold B.

    2010-01-01

    The American Society of Biochemistry and Molecular Biology (ASBMB) has launched an National Science Foundation (NSF)-funded 5 year project to support biochemistry and molecular biology educators learning what and how students learn. As a part of this initiative, hundreds of life scientists will plan and develop a rich central resource for…

  3. Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions

    PubMed Central

    Nishikawa, Takashi; Motter, Adilson E.

    2010-01-01

    Synchronization, in which individual dynamical units keep in pace with each other in a decentralized fashion, depends both on the dynamical units and on the properties of the interaction network. Yet, the role played by the network has resisted comprehensive characterization within the prevailing paradigm that interactions facilitating pairwise synchronization also facilitate collective synchronization. Here we challenge this paradigm and show that networks with best complete synchronization, least coupling cost, and maximum dynamical robustness, have arbitrary complexity but quantized total interaction strength, which constrains the allowed number of connections. It stems from this characterization that negative interactions as well as link removals can be used to systematically improve and optimize synchronization properties in both directed and undirected networks. These results extend the recently discovered compensatory perturbations in metabolic networks to the realm of oscillator networks and demonstrate why “less can be more” in network synchronization. PMID:20489183

  4. A survey for Galactic supernova remnant/molecular cloud interactions ssing carbon monoxide

    NASA Astrophysics Data System (ADS)

    Kilpatrick, Charles; Rieke, George; Bieging, John

    2016-06-01

    Supernova remnants are one of the primary engines through which stars add energy to the interstellar medium. The efficiency of this transfer of energy is enhanced where supernova remnants encounter dense interstellar gas, such as in molecular clouds. Unique signatures have been observed toward these supernova remnant/molecular cloud interactions in the form of unusual molecular line profiles and bright non-thermal radiation. The sites of these interactions also provide some of the best examples for evidence of cosmic-ray acceleration and Galactic sources of very high-energy gamma-rays. Despite the large number of individual studies that examine supernova remnant/molecular cloud interactions, very little is known about their overall rates and characteristics. This lack of information limits the usefulness of individual supernova remnant/molecular cloud interactions to enhance our understanding of supernova feedback and cosmic-ray acceleration. I will discuss recent work studying large populations of supernova remnants in the 12CO J = 2 ‑ 1 and J = 3 ‑ 2 lines and the observational signatures associated with molecular shocks from supernova ejecta. Broadened molecular lines and molecular line ratios indicative of warm gas can be used to identify and characterize populations of supernova remnant/molecular cloud interactions. From this large sample, I will discuss new constraints on the energetic processes to which supernova remnants contribute, especially the rate of GeV and TeV gamma-ray production associated with supernova remnant/molecular cloud interfaces.

  5. Functional features, biological pathways, and protein interaction networks of addiction-related genes.

    PubMed

    Sun, Jingchun; Zhao, Zhongming

    2010-05-01

    Addictions are chronic and common brain disorders affected by many genetic, environmental, and behavioral factors. Recent genome-wide linkage and association studies have revealed several promising genomic regions and multiple genes relating to addictions. To explore the underlying biological processes in the development of addictions, we used 62 genes recently reviewed by Li and Burmeister (2009) as representative addiction-related genes, and then we investigated their features in gene function, pathways, and protein interaction networks. We performed enrichment tests of their Gene Ontology (GO) annotations and of their pathways in the Ingenuity Pathways Analysis (IPA) system. The tests revealed that these addiction-related genes were highly enriched in neurodevelopment-related processes. Interestingly, we found circadian rhythm signaling in one of the enriched pathways. Moreover, these addiction-related genes tended to have higher connectivity and shorter characteristic shortest-path distances compared to control genes in the protein-protein interaction (PPI) network. This investigation is the first of such kind in addiction studies, and it is useful for further addiction candidate-gene prioritization and verification, thus helping us to better understand molecular mechanisms of addictions.