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Sample records for georgia interactive network

  1. Implementing hospital library automation: the GaIN project. Georgia Interactive Network for Medical Information.

    Science.gov (United States)

    Rankin, J A; McInnis, K A; Rosner, A L

    1995-01-01

    The GaIN (Georgia Interactive Network for Medical Information) Hospital Libraries' Local Automation Project was a one-year, grant-funded initiative to implement an integrated library system in three Georgia hospitals. The purpose of the project was to install the library systems, describe the steps in hospital library automation, and identify issues and barriers related to automation in small libraries. The participating hospitals included a small, a medium, and a large institution. The steps and time required for project implementation were documented in order to develop a decision checklist. Although library automation proved a desirable approach for improving collection accessibility, simplifying daily routines, and improving the library's image in the hospital, planners must be sure to consider equipment as well as software support, staffing for the conversion, and training of the library staff and end users. PMID:7581184

  2. National Seismic Network of Georgia

    Science.gov (United States)

    Tumanova, N.; Kakhoberashvili, S.; Omarashvili, V.; Tserodze, M.; Akubardia, D.

    2016-12-01

    Georgia, as a part of the Southern Caucasus, is tectonically active and structurally complex region. It is one of the most active segments of the Alpine-Himalayan collision belt. The deformation and the associated seismicity are due to the continent-continent collision between the Arabian and Eurasian plates. Seismic Monitoring of country and the quality of seismic data is the major tool for the rapid response policy, population safety, basic scientific research and in the end for the sustainable development of the country. National Seismic Network of Georgia has been developing since the end of 19th century. Digital era of the network started from 2003. Recently continuous data streams from 25 stations acquired and analyzed in the real time. Data is combined to calculate rapid location and magnitude for the earthquake. Information for the bigger events (Ml>=3.5) is simultaneously transferred to the website of the monitoring center and to the related governmental agencies. To improve rapid earthquake location and magnitude estimation the seismic network was enhanced by installing additional 7 new stations. Each new station is equipped with coupled Broadband and Strong Motion seismometers and permanent GPS system as well. To select the sites for the 7 new base stations, we used standard network optimization techniques. To choose the optimal sites for new stations we've taken into account geometry of the existed seismic network, topographic conditions of the site. For each site we studied local geology (Vs30 was mandatory for each site), local noise level and seismic vault construction parameters. Due to the country elevation, stations were installed in the high mountains, no accessible in winter due to the heavy snow conditions. To secure online data transmission we used satellite data transmission as well as cell data network coverage from the different local companies. As a result we've already have the improved earthquake location and event magnitudes. We

  3. Network and computing infrastructure for scientific applications in Georgia

    Science.gov (United States)

    Kvatadze, R.; Modebadze, Z.

    2016-09-01

    Status of network and computing infrastructure and available services for research and education community of Georgia are presented. Research and Educational Networking Association - GRENA provides the following network services: Internet connectivity, network services, cyber security, technical support, etc. Computing resources used by the research teams are located at GRENA and at major state universities. GE-01-GRENA site is included in European Grid infrastructure. Paper also contains information about programs of Learning Center and research and development projects in which GRENA is participating.

  4. Georgia's Surface-Water Resources and Streamflow Monitoring Network, 2008

    Science.gov (United States)

    ,

    2008-01-01

    Surface water provides 5 billion gallons per day, or 78 percent, of the total freshwater used (including thermoelectric) in Georgia (Fanning, 2003). Climate, geology, and landforms control the natural distribution of Georgia's water resources. Georgia is a 'headwaters' State, with most of the rivers beginning in northern Georgia and increasing in size downstream (see map at right for major watersheds). Surface water is the primary source of water in the northern one-half of the State, including the Atlanta metropolitan area, where limited ground-water resources are difficult to obtain. In Georgia, periodic droughts exacerbate competition for surface-water supplies. Many areas of Georgia also face a threat of flooding because of spring frontal thunderstorms and the potential for hurricanes from both the Atlantic Ocean and Gulf of Mexico. As the population of Georgia increases, these flood risks will increase with development in flood-risk zones, particularly in the coastal region.

  5. New strong motion network in Georgia: basis for specifying seismic hazard

    Science.gov (United States)

    Kvavadze, N.; Tsereteli, N. S.

    2017-12-01

    Risk created by hazardous natural events is closely related to sustainable development of the society. Global observations have confirmed tendency of growing losses resulting from natural disasters, one of the most dangerous and destructive if which are earthquakes. Georgia is located in seismically active region. So, it is imperative to evaluate probabilistic seismic hazard and seismic risk with proper accuracy. National network of Georgia includes 35 station all of which are seismometers. There are significant gaps in strong motion recordings, which essential for seismic hazard assessment. To gather more accelerometer recordings, we have built a strong motion network distributed on the territory of Georgia. The network includes 6 stations for now, with Basalt 4x datalogger and strong motion sensor Episensor ES-T. For each site, Vs30 and soil resonance frequencies have been measured. Since all but one station (Tabakhmelam near Tbilisi), are located far from power and internet lines special system was created for instrument operation. Solar power is used to supply the system with electricity and GSM/LTE modems for internet access. VPN tunnel was set up using Raspberry pi, for two-way communication with stations. Tabakhmela station is located on grounds of Ionosphere Observatory, TSU and is used as a hub for the network. This location also includes a broadband seismometer and VLF electromagnetic waves observation antenna, for possible earthquake precursor studies. On server, located in Tabakhmela, the continues data is collected from all the stations, for later use. The recordings later will be used in different seismological and engineering problems, namely selecting and creating GMPE model for Caucasus, for probabilistic seismic hazard and seismic risk evaluation. These stations are a start and in the future expansion of strong motion network is planned. Along with this, electromagnetic wave observations will continue and additional antennas will be implemented

  6. GT-MSOCC - A domain for research on human-computer interaction and decision aiding in supervisory control systems. [Georgia Tech - Multisatellite Operations Control Center

    Science.gov (United States)

    Mitchell, Christine M.

    1987-01-01

    The Georgia Tech-Multisatellite Operations Control Center (GT-MSOCC), a real-time interactive simulation of the operator interface to a NASA ground control system for unmanned earth-orbiting satellites, is described. The GT-MSOCC program for investigating a range of modeling, decision aiding, and workstation design issues related to the human-computer interaction is discussed. A GT-MSOCC operator function model is described in which operator actions, both cognitive and manual, are represented as the lowest level discrete control network nodes, and operator action nodes are linked to information needs or system reconfiguration commands.

  7. Social Interaction in Learning Networks

    OpenAIRE

    Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

  8. Shared vision, collective impact, and persistent challenges: the first decade of Georgia's oncology research network.

    Science.gov (United States)

    Paris, Nancy M; Burke, James J; Schnell, Frederick M

    2013-11-01

    Ten years ago, Georgia was lauded for dedicating a portion of tobacco settlement funds to the Georgia Cancer Coalition (GCC). The plan championed by then-Governor Roy E. Barnes was designed to make Georgia a leader in prevention, treatment, and research. This plan called for the expansion of clinical trials to ensure Georgians had access to the highest quality care based on the most current treatments and discoveries. As a result, oncologists in the state were engaged in a planning process that resulted in a shared vision to improve the quality of cancer care through research and the formation of a new organization: the Georgia Center for Oncology Research and Education.

  9. Networks and Interactivity

    DEFF Research Database (Denmark)

    Considine, Mark; Lewis, Jenny

    2012-01-01

    The systemic reform of employment services in OECD countries was driven by New Public Management (NPM) and then post-NPM reforms, when first-phase changes such as privatization were amended with `joined up' processes to help manage fragmentation. This article examines the networking strategies...... of `street-level' employment services staff for the impacts of this. Contrary to expectations, networking has generally declined over the last decade. There are signs of path dependence in networking patterns within each country, but also a convergence of patterns for the UK and Australia......, but not The Netherlands. Networking appears to be mediated by policy and regulatory imperatives....

  10. Online Social Network Interactions:

    Directory of Open Access Journals (Sweden)

    Hui-Jung Chang

    2018-01-01

    Full Text Available A cross-cultural comparison of social networking structure on McDonald’s Facebook fan sites between Taiwan and the USA was conducted utilizing the individualism/collectivism dimension proposed by Hofstede. Four network indicators are used to describe the network structure of McDonald’s Facebook fan sites: size, density, clique and centralization. Individuals who post on both Facebook sites for the year of 2012 were considered as network participants for the purpose of the study. Due to the huge amount of data, only one thread of postings was sampled from each month of the year of 2012. The final data consists of 1002 postings written by 896 individuals and 5962 postings written by 5532 individuals from Taiwan and the USA respectively. The results indicated that the USA McDonald’s Facebook fan network has more fans, while Taiwan’s McDonald’s Facebook fan network is more densely connected. Cliques did form among the overall multiplex and within the individual uniplex networks in two countries, yet no significant differences were found between them. All the fan networks in both countries are relatively centralized, mostly on the site operators.

  11. Topology of molecular interaction networks

    NARCIS (Netherlands)

    Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; De Ridder, D.

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over

  12. Interactive Network Exploration with Orange

    Directory of Open Access Journals (Sweden)

    Miha Štajdohar

    2013-04-01

    Full Text Available Network analysis is one of the most widely used techniques in many areas of modern science. Most existing tools for that purpose are limited to drawing networks and computing their basic general characteristics. The user is not able to interactively and graphically manipulate the networks, select and explore subgraphs using other statistical and data mining techniques, add and plot various other data within the graph, and so on. In this paper we present a tool that addresses these challenges, an add-on for exploration of networks within the general component-based environment Orange.

  13. Dynamic and interacting complex networks

    Science.gov (United States)

    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

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

    Science.gov (United States)

    Pires, Mathias M; Guimarães, Paulo R

    2013-01-06

    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.

  15. Statistical Mechanics of Temporal and Interacting Networks

    Science.gov (United States)

    Zhao, Kun

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

  16. Delineating groundwater/surface water interaction in a karst watershed: Lower Flint River Basin, southwestern Georgia, USA

    Directory of Open Access Journals (Sweden)

    Kathleen Rugel

    2016-03-01

    Full Text Available Study region: Karst watershed in Lower Flint River Basin (LFRB, southwestern Georgia, USA. Study focus: Baseflow discharges in the LFRB have declined for three decades as regional irrigation has increased; yet, the location and nature of connectivity between groundwater and surface water in this karstic region are poorly understood. Because growing water demands will likely be met by further development of regional aquifers, an important management concern is the nature of interactions between groundwater and surface water components under natural and anthropogenic perturbations. We conducted coarse and fine-scale stream sampling on a major tributary of the Lower Flint River (Ichawaynochaway Creek in southwestern Georgia, USA, to identify locations and patterns of enhanced hydrologic connectivity between this stream and the Upper Floridan Aquifer. New hydrological insights for the region: Prior water resource studies in the LFRB were based on regional modeling that neglected local heterogeneities in groundwater/surface water connectivity. Our results demonstrated groundwater inputs were concentrated around five of fifty sampled reaches, evidenced by increases in multiple groundwater indicators at these sites. These five reaches contributed up to 42% of the groundwater detected along the entire 50-km sampling section, with ∼24% entering through one groundwater-dominated tributary, Chickasawhatchee Creek. Intermittent flows occurred in two of these upstream reaches during extreme drought and heavy groundwater pumping, suggesting reach-scale behaviors should be considered in resource management and policy. Keywords: Karst hydrogeology, Hydrologic connectivity, Groundwater/surface water interaction, Upper Floridan Aquifer, Groundwater Irrigation

  17. Network Physiology: How Organ Systems Dynamically Interact.

    Science.gov (United States)

    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.

  18. Interaction network among functional drug groups

    Science.gov (United States)

    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. Inferring network interactions using recurrent neural networks and swarm intelligence.

    Science.gov (United States)

    Ressom, Habtom W; Zhang, Yuji; Xuan, Jianhua; Wang, Yue; Clarke, Robert

    2006-01-01

    We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated. We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network. The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data.

  20. Understanding complex interactions using social network analysis.

    Science.gov (United States)

    Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

    2012-10-01

    The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

  1. Humic substances in the Suwannee River, Georgia; interactions, properties, and proposed structures

    Science.gov (United States)

    Averett, R.C.; Leenheer, J.A.; McKnight, Diane M.; Thorn, K.A.

    1989-01-01

    Humic substances as a collective term and humic and fulvic acids as specific terms are not household words. For about a century, these terms belonged to the domain of the soil scientist. Even^though their chemical structures remained elusive, they were recognized as important entities in soil. During the past decade or so, there has been a renewed interest in humic substances in soil and water. Such interest has been the result of improved analytical instrumentation, and by a need to understand the structure and function of natural organic substances in water.A responsibility of the U.S. Geological Survey is to assess the Nation's water resources; this includes water quality, which is the study of material in water. Such material may be suspended, colloidal, or in true solution. Because humic substances are a major carbon source in water, they have received attention by Geological Survey scientists. This attention has been a major focus by members of the Geological Survey's organic-chemistry group. For more than a decade, this group has collected samples, made analyses, and worked toward determining the structures and function of humic substances in water. Their work has brought worldwide recognition to the field, and in 1981, through Geological Survey support, they helped organize the International Humic Substances Society, which held its first meeting in Estes Park, Colorado, in August 1983.At the second meeting of the Society in Birmingham, England, in August 1984, it became apparent that Geological Survey scientists were rapidly advancing the study of the chemistry of humic substances. It seemed appropriate, therefore, to publish this Open-File report on humic and fulvic acids from the Suwannee River in Georgia. The results represent our most definitive findings to date (1986). Though this work is not conclusive, it is state-of-the-science. Hopefully, our reporting on this work will aid in moving the science of humic substances forward as others read of our

  2. Interactivity vs. fairness in networked linux systems

    Energy Technology Data Exchange (ETDEWEB)

    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.

  3. Evolution of a protein domain interaction network

    International Nuclear Information System (INIS)

    Li-Feng, Gao; Jian-Jun, Shi; Shan, Guan

    2010-01-01

    In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases. (general)

  4. Identifying hubs in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Ravishankar R Vallabhajosyula

    Full Text Available In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs, it is common to define an ad hoc degree scale that defines "hub" proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.

  5. Unraveling spurious properties of interaction networks with tailored random networks.

    Directory of Open Access Journals (Sweden)

    Stephan Bialonski

    Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

  6. Drivers and Effects of Groundwater-Surface Water Interaction in the Karstic Lower Flint River Basin, Southwestern Georgia, USA

    Science.gov (United States)

    Rugel, K.; Golladay, S. W.; Jackson, C. R.; Rasmussen, T. C.; Dowd, J. F.; Mcdowell, R. J.

    2017-12-01

    Groundwater provides the majority of global water resources for domestic and agricultural usage while contributing vital surface water baseflows which support healthy aquatic ecosystems. Understanding the extent and magnitude of hydrologic connectivity between groundwater and surface water components in karst watersheds is essential to the prudent management of these hydraulically-interactive systems. We examined groundwater and surface water connectivity between the Upper Floridan Aquifer (UFA) and streams in the Lower Flint River Basin (LFRB) in southwestern Georgia where development of agricultural irrigation intensified over the past 30 years. An analysis of USGS streamflow data for the pre- and post-irrigation period showed summer baseflows in some Lower Flint River tributaries were reduced by an order of magnitude in the post-irrigation period, reiterating the strong hydraulic connection between these streams and the underlying aquifer. Large and fine-scale monitoring of calcium, nitrate, specific conductance and stable isotopes (δ18O and δD) on 50 km of Ichawaynochaway Creek, a major tributary of the Lower Flint, detected discrete groundwater-surface water flow paths which accounted for 42% of total groundwater contributions in the 50 km study reach. This presentation will highlight a new analysis using the metadata EPA Reach File (1) and comparing stream reach and instream bedrock joint azimuths with stream geochemical results from previous field study. Our findings suggested that reaches with NNW bearing may be more likely to display enhanced groundwater-surface water connectivity. Our results show that local heterogeneity can significantly affect water budgets and quality within these watersheds, making the use of geomorphological stream attributes a valuable tool to water resource management for the prediction and protection of vulnerable regions of hydrologic connectivity in karst catchments.

  7. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  8. A simple model for studying interacting networks

    Science.gov (United States)

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

    2011-03-01

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

  9. Comparing species interaction networks along environmental gradients.

    Science.gov (United States)

    Pellissier, Loïc; Albouy, Camille; Bascompte, Jordi; Farwig, Nina; Graham, Catherine; Loreau, Michel; Maglianesi, Maria Alejandra; Melián, Carlos J; Pitteloud, Camille; Roslin, Tomas; Rohr, Rudolf; Saavedra, Serguei; Thuiller, Wilfried; Woodward, Guy; Zimmermann, Niklaus E; Gravel, Dominique

    2017-09-22

    Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis

  10. Exploring Disease Interactions Using Markov Networks

    OpenAIRE

    Haaren, J. Van; Davis, J; Lappenschaar, G.A.M.; Hommersom, A.J.

    2013-01-01

    Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often interlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between musculoskeletal disorders and cardiovascular diseases and compare this to pairwise approaches. Our experimental results confirm that the s...

  11. Structure of the human chromosome interaction network.

    Directory of Open Access Journals (Sweden)

    Sergio Sarnataro

    Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

  12. Network compression as a quality measure for protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Loic Royer

    Full Text Available 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.

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

    Science.gov (United States)

    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

  14. Business Education in Georgia: Challenges and Opportunities

    OpenAIRE

    Tatiana Papiashvili

    2010-01-01

    Knowledge-based society rests on the knowledge triangle that refers to the interaction between research, education, and innovation. The presented paper discusses efficiency of business education in Georgia. SWOT analysis and a questionnaire held by the author show challenges and opportunities of business education in Georgia.

  15. Network Interactions in the Great Altai Region

    Directory of Open Access Journals (Sweden)

    Lev Aleksandrovich Korshunov

    2017-12-01

    Full Text Available To improve the efficiency and competitiveness of the regional economy, an effective interaction between educational institutions in the Great Altai region is needed. The innovation growth can enhancing this interaction. The article explores the state of network structures in the economy and higher education in the border territories of the countries of Great Altai. The authors propose an updated approach to the three-level classification of network interaction. We analyze growing influence of the countries with emerging economies. We define the factors that impede the more stable and multifaceted regional development of these countries. Further, the authors determine indicators of the higher education systems and cooperation systems at the university level between the Shanghai Cooperation Organization countries (SCO and BRICS countries, showing the international rankings of the universities in these countries. The teaching language is important to overcome the obstacles in the interregional cooperation. The authors specify the problems of the development of the universities of the SCO and BRICS countries as global educational networks. The research applies basic scientific logical methods of analysis and synthesis, induction and deduction, as well as the SWOT analysis method. We have indentified and analyzed the existing economic and educational relations. To promote the economic innovation development of the border territories of the Great Altai, we propose a model of regional network university. Modern universities function in a new economic environment. Thus, in a great extent, they form the technological and social aspects of this environment. Innovative network structures contribute to the formation of a new network institutional environment of the regional economy, which impacts the macro- and microeconomic performance of the region as a whole. The results of the research can help to optimize the regional economies of the border

  16. The Global Alzheimer's Association Interactive Network.

    Science.gov (United States)

    Toga, Arthur W; Neu, Scott C; Bhatt, Priya; Crawford, Karen L; Ashish, Naveen

    2016-01-01

    The Global Alzheimer's Association Interactive Network (GAAIN) is consolidating the efforts of independent Alzheimer's disease data repositories around the world with the goals of revealing more insights into the causes of Alzheimer's disease, improving treatments, and designing preventative measures that delay the onset of physical symptoms. We developed a system for federating these repositories that is reliant on the tenets that (1) its participants require incentives to join, (2) joining the network is not disruptive to existing repository systems, and (3) the data ownership rights of its members are protected. We are currently in various phases of recruitment with over 55 data repositories in North America, Europe, Asia, and Australia and can presently query >250,000 subjects using GAAIN's search interfaces. GAAIN's data sharing philosophy, which guided our architectural choices, is conducive to motivating membership in a voluntary data sharing network. Copyright © 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

  17. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

    Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

  18. Temporal stability in human interaction networks

    Science.gov (United States)

    Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais

    2017-11-01

    This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.

  19. Repulsive interactions between two polyelectrolyte networks

    Science.gov (United States)

    Erbas, Aykut; Olvera de La Cruz, Monica; Olvera Group Collaboration

    Surfaces formed by charged polymeric species are highly_abundant in both synthetic and biological systems, for which maintaining_an optimum contact distance and a pressure balance is paramount. We investigate interactions between surfaces of two same-charged and_highly swollen polyelectrolyte gels, using extensive molecular dynamic_simulations and minimal analytical methods. The external-pressure_responses of the gels and the polymer-free ionic solvent layer separating_two surfaces are considered. Simulations confirmed that the surfaces are_held apart by osmotic pressure resulting from excess charges diffusing out_of the network. Both the solvent layer and pressure dependence are well_described by an analytical model based on the Poisson -Boltzmann solution for low and moderate electrostatic strengths. Our results can be of great importance for systems where charged gels or gel-like structures interact in various solvents, including systems encapsulated by gels and microgels in confinement.

  20. Game theory in communication networks cooperative resolution of interactive networking scenarios

    CERN Document Server

    Antoniou, Josephina

    2012-01-01

    A mathematical tool for scientists and researchers who work with computer and communication networks, Game Theory in Communication Networks: Cooperative Resolution of Interactive Networking Scenarios addresses the question of how to promote cooperative behavior in interactive situations between heterogeneous entities in communication networking scenarios. It explores network design and management from a theoretical perspective, using game theory and graph theory to analyze strategic situations and demonstrate profitable behaviors of the cooperative entities. The book promotes the use of Game T

  1. Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

    Science.gov (United States)

    Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

    2018-02-05

    Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Cluster Approach to Network Interaction in Pedagogical University

    Science.gov (United States)

    Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

    2016-01-01

    The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

  3. Food Web Designer: a flexible tool to visualize interaction networks

    OpenAIRE

    Sint, Daniela; Traugott, Michael

    2015-01-01

    Species are embedded in complex networks of ecological interactions and assessing these networks provides a powerful approach to understand what the consequences of these interactions are for ecosystem functioning and services. This is mandatory to develop and evaluate strategies for the management and control of pests. Graphical representations of networks can help recognize patterns that might be overlooked otherwise. However, there is a lack of software which allows visualizing these compl...

  4. Georgia - Energy Rehabilitation

    Data.gov (United States)

    Millennium Challenge Corporation — Gustavson Associates was retained by Millennium Challenge Georgia (MCG) to prepare a model to calculate the economic rate of return (ERR) for rehabilitation work...

  5. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  6. Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution

    OpenAIRE

    Hsieh, Chih-Sheng; Lee, Lung fei

    2017-01-01

    In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives stemming from interaction benefits on certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interac...

  7. The computational power of interactive recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2012-04-01

    In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities.

  8. Application Interaction Model for Opportunistic Networking

    NARCIS (Netherlands)

    de Souza Schwartz, Ramon; van Dijk, H.W.; Scholten, Johan

    In Opportunistic Networks, autonomous nodes discover, assess and potentially seize opportunities for communication and distributed processing whenever these emerge. In this paper, we consider prerequisites for a successful implementation of such a way of processing in networks that consist mainly of

  9. Forests of Georgia, 2015

    Science.gov (United States)

    Thomas Brandeis; Consuelo Brandeis; Andy Hartsell; KaDonna Randolph; Sonja Oswalt

    2016-01-01

    This resource update provides an overview of forest resources in Georgia based on an inventory conducted by the U.S. Forest Service, Forest Inventory and Analysis (FIA) program at the Southern Research Station in cooperation with the Georgia Forestry Commission. Estimates are based on field data collected using the FIA annualized sample design and are updated yearly....

  10. Forests of Georgia, 2014

    Science.gov (United States)

    Thomas Brandeis; Andy Hartsell

    2015-01-01

    This resource update provides an overview of forest resources in Georgia based on an inventory conducted by the U.S. Forest Service, Forest Inventory and Analysis (FIA) program at the Southern Research Station in cooperation with the Georgia Forestry Commission. Estimates are based on field data collected using the FIA annualized sample design and are updated yearly....

  11. Guaranteeing global synchronization in networks with stochastic interactions

    Science.gov (United States)

    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.

  12. Investigating physics learning with layered student interaction networks

    DEFF Research Database (Denmark)

    Bruun, Jesper; Traxler, Adrienne

    Centrality in student interaction networks (SINs) can be linked to variables like grades [1], persistence [2], and participation [3]. Recent efforts in the field of network science have been done to investigate layered - or multiplex - networks as mathematical objects [4]. These networks can be e......, this study investigates how target entropy [5,1] and pagerank [6,7] are affected when we take time and modes of interaction into account. We present our preliminary models and results and outline our future work in this area....

  13. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  14. Specific non-monotonous interactions increase persistence of ecological networks.

    Science.gov (United States)

    Yan, Chuan; Zhang, Zhibin

    2014-03-22

    The relationship between stability and biodiversity has long been debated in ecology due to opposing empirical observations and theoretical predictions. Species interaction strength is often assumed to be monotonically related to population density, but the effects on stability of ecological networks of non-monotonous interactions that change signs have not been investigated previously. We demonstrate that for four kinds of non-monotonous interactions, shifting signs to negative or neutral interactions at high population density increases persistence (a measure of stability) of ecological networks, while for the other two kinds of non-monotonous interactions shifting signs to positive interactions at high population density decreases persistence of networks. Our results reveal a novel mechanism of network stabilization caused by specific non-monotonous interaction types through either increasing stable equilibrium points or reducing unstable equilibrium points (or both). These specific non-monotonous interactions may be important in maintaining stable and complex ecological networks, as well as other networks such as genes, neurons, the internet and human societies.

  15. Do networks of social interactions reflect patterns of kinship?

    Directory of Open Access Journals (Sweden)

    Joah R. MADDEN, Johanna F. NIELSEN, Tim H. CLUTTON-BROCK

    2012-04-01

    Full Text Available The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals, and is presumed to facilitate inclusive fitness benefits. Such structure may be evident at a finer, behavioural, scale with individuals preferentially interacting with kin. We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks: grooming, dominance or foraging competitions. Networks of dominance interactions were positively related to networks of kinship, with close relatives engaging in dominance interactions with each other. This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin, which are most likely to be able to discern kin through simple rules of thumb. Conversely, we found no relationship between kinship networks and either grooming networks or networks of foraging competitions. This is surprising because a positive association between kin in a grooming network, or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits. Indeed, the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members. We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits, and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2: 319-328, 2012].

  16. Probing the extent of randomness in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Joseph Ivanic

    Full Text Available Protein-protein interaction (PPI networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered features. We recently demonstrated that PPI networks show degree-weighted behavior, whereby the probability of interaction between two proteins is generally proportional to the product of their numbers of interacting partners or degrees. It was surmised that degree-weighted behavior is a characteristic of randomness. We expand upon these findings by developing a random, degree-weighted, network model and show that eight PPI networks determined from single high-throughput (HT experiments have global and local properties that are consistent with this model. The apparent random connectivity in HT PPI networks is counter-intuitive with respect to their observed degree distributions; however, we resolve this discrepancy by introducing a non-network-based model for the evolution of protein degrees or "binding affinities." This mechanism is based on duplication and random mutation, for which the degree distribution converges to a steady state that is identical to one obtained by averaging over the eight HT PPI networks. The results imply that the degrees and connectivities incorporated in HT PPI networks are characteristic of unbiased interactions between proteins that have varying individual binding affinities. These findings corroborate the observation that curated and high-confidence PPI networks are distinct from HT PPI networks and not consistent with a random connectivity. These results provide an avenue to discern indiscriminate organizations in biological networks and suggest caution in the analysis of curated and high-confidence networks.

  17. Probing the extent of randomness in protein interaction networks.

    Science.gov (United States)

    Ivanic, Joseph; Wallqvist, Anders; Reifman, Jaques

    2008-07-11

    Protein-protein interaction (PPI) networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered features. We recently demonstrated that PPI networks show degree-weighted behavior, whereby the probability of interaction between two proteins is generally proportional to the product of their numbers of interacting partners or degrees. It was surmised that degree-weighted behavior is a characteristic of randomness. We expand upon these findings by developing a random, degree-weighted, network model and show that eight PPI networks determined from single high-throughput (HT) experiments have global and local properties that are consistent with this model. The apparent random connectivity in HT PPI networks is counter-intuitive with respect to their observed degree distributions; however, we resolve this discrepancy by introducing a non-network-based model for the evolution of protein degrees or "binding affinities." This mechanism is based on duplication and random mutation, for which the degree distribution converges to a steady state that is identical to one obtained by averaging over the eight HT PPI networks. The results imply that the degrees and connectivities incorporated in HT PPI networks are characteristic of unbiased interactions between proteins that have varying individual binding affinities. These findings corroborate the observation that curated and high-confidence PPI networks are distinct from HT PPI networks and not consistent with a random connectivity. These results provide an avenue to discern indiscriminate organizations in biological networks and suggest caution in the analysis of curated and high-confidence networks.

  18. VANLO - Interactive visual exploration of aligned biological networks

    Directory of Open Access Journals (Sweden)

    Linsen Lars

    2009-10-01

    Full Text Available Abstract Background Protein-protein interaction (PPI is fundamental to many biological processes. In the course of evolution, biological networks such as protein-protein interaction networks have developed. Biological networks of different species can be aligned by finding instances (e.g. proteins with the same common ancestor in the evolutionary process, so-called orthologs. For a better understanding of the evolution of biological networks, such aligned networks have to be explored. Visualization can play a key role in making the various relationships transparent. Results We present a novel visualization system for aligned biological networks in 3D space that naturally embeds existing 2D layouts. In addition to displaying the intra-network connectivities, we also provide insight into how the individual networks relate to each other by placing aligned entities on top of each other in separate layers. We optimize the layout of the entire alignment graph in a global fashion that takes into account inter- as well as intra-network relationships. The layout algorithm includes a step of merging aligned networks into one graph, laying out the graph with respect to application-specific requirements, splitting the merged graph again into individual networks, and displaying the network alignment in layers. In addition to representing the data in a static way, we also provide different interaction techniques to explore the data with respect to application-specific tasks. Conclusion Our system provides an intuitive global understanding of aligned PPI networks and it allows the investigation of key biological questions. We evaluate our system by applying it to real-world examples documenting how our system can be used to investigate the data with respect to these key questions. Our tool VANLO (Visualization of Aligned Networks with Layout Optimization can be accessed at http://www.math-inf.uni-greifswald.de/VANLO.

  19. Default network modulation and large-scale network interactivity in healthy young and old adults.

    Science.gov (United States)

    Spreng, R Nathan; Schacter, Daniel L

    2012-11-01

    We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands.

  20. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  1. Defaunation leads to interaction deficits, not interaction compensation, in an island seed dispersal network.

    Science.gov (United States)

    Fricke, Evan C; Tewksbury, Joshua J; Rogers, Haldre S

    2018-01-01

    Following defaunation, the loss of interactions with mutualists such as pollinators or seed dispersers may be compensated through increased interactions with remaining mutualists, ameliorating the negative cascading impacts on biodiversity. Alternatively, remaining mutualists may respond to altered competition by reducing the breadth or intensity of their interactions, exacerbating negative impacts on biodiversity. Despite the importance of these responses for our understanding of the dynamics of mutualistic networks and their response to global change, the mechanism and magnitude of interaction compensation within real mutualistic networks remains largely unknown. We examined differences in mutualistic interactions between frugivores and fruiting plants in two island ecosystems possessing an intact or disrupted seed dispersal network. We determined how changes in the abundance and behavior of remaining seed dispersers either increased mutualistic interactions (contributing to "interaction compensation") or decreased interactions (causing an "interaction deficit") in the disrupted network. We found a "rich-get-richer" response in the disrupted network, where remaining frugivores favored the plant species with highest interaction frequency, a dynamic that worsened the interaction deficit among plant species with low interaction frequency. Only one of five plant species experienced compensation and the other four had significant interaction deficits, with interaction frequencies 56-95% lower in the disrupted network. These results do not provide support for the strong compensating mechanisms assumed in theoretical network models, suggesting that existing network models underestimate the prevalence of cascading mutualism disruption after defaunation. This work supports a mutualist biodiversity-ecosystem functioning relationship, highlighting the importance of mutualist diversity for sustaining diverse and resilient ecosystems. © 2017 John Wiley & Sons Ltd.

  2. A Topological Description of Hubs in Amino Acid Interaction Networks

    Directory of Open Access Journals (Sweden)

    Omar Gaci

    2010-01-01

    Full Text Available We represent proteins by amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. Once we have compared this type of graphs to the general model of scale-free networks, we analyze the existence of nodes which highly interact, the hubs. We describe these nodes taking into account their position in the primary structure to study their apparition frequency in the folded proteins. Finally, we observe that their interaction level is a consequence of the general rules which govern the folding process.

  3. Food Web Designer: a flexible tool to visualize interaction networks.

    Science.gov (United States)

    Sint, Daniela; Traugott, Michael

    Species are embedded in complex networks of ecological interactions and assessing these networks provides a powerful approach to understand what the consequences of these interactions are for ecosystem functioning and services. This is mandatory to develop and evaluate strategies for the management and control of pests. Graphical representations of networks can help recognize patterns that might be overlooked otherwise. However, there is a lack of software which allows visualizing these complex interaction networks. Food Web Designer is a stand-alone, highly flexible and user friendly software tool to quantitatively visualize trophic and other types of bipartite and tripartite interaction networks. It is offered free of charge for use on Microsoft Windows platforms. Food Web Designer is easy to use without the need to learn a specific syntax due to its graphical user interface. Up to three (trophic) levels can be connected using links cascading from or pointing towards the taxa within each level to illustrate top-down and bottom-up connections. Link width/strength and abundance of taxa can be quantified, allowing generating fully quantitative networks. Network datasets can be imported, saved for later adjustment and the interaction webs can be exported as pictures for graphical display in different file formats. We show how Food Web Designer can be used to draw predator-prey and host-parasitoid food webs, demonstrating that this software is a simple and straightforward tool to graphically display interaction networks for assessing pest control or any other type of interaction in both managed and natural ecosystems from an ecological network perspective.

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

    Directory of Open Access Journals (Sweden)

    Yang Zhihao

    2011-10-01

    Full Text Available Abstract 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.

  5. Competence–Based Support of Interaction between Business Network Members

    OpenAIRE

    Smirnov, Alexander; Kashevnik, Alexey; Shilov, Nikolay

    2008-01-01

    In a complicated business network finding a supplier can be a very time consuming task. The technology of competence management is aimed to support such kind of tasks. The paper presents an approach to support interaction between business network members based on such technologies as competence management and knowledge management. The conceptual models of the context-driven competence management system and production network member competence profile are described. The usage of th...

  6. Development of Attention Networks and Their Interactions in Childhood

    Science.gov (United States)

    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),…

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

    Directory of Open Access Journals (Sweden)

    Luke eBarrett

    2015-09-01

    Full Text Available 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  9. Building a glaucoma interaction network using a text mining approach.

    Science.gov (United States)

    Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

    2016-01-01

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

  10. Optimization of an interactive distributive computer network

    Science.gov (United States)

    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.

  11. Mean field interaction in biochemical reaction networks

    KAUST Repository

    Tembine, Hamidou

    2011-09-01

    In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples. © 2011 IEEE.

  12. Cortico-cardio-respiratory network interactions during anesthesia.

    Directory of Open Access Journals (Sweden)

    Yuri Shiogai

    Full Text Available General anesthetics are used during medical and surgical procedures to reversibly induce a state of total unconsciousness in patients. Here, we investigate, from a dynamic network perspective, how the cortical and cardiovascular systems behave during anesthesia by applying nonparametric spectral techniques to cortical electroencephalography, electrocardiogram and respiratory signals recorded from anesthetized rats under two drugs, ketamine-xylazine (KX and pentobarbital (PB. We find that the patterns of low-frequency cortico-cardio-respiratory network interactions may undergo significant changes in network activity strengths and in number of network links at different depths of anesthesia dependent upon anesthetics used.

  13. EVALUATING AUSTRALIAN FOOTBALL LEAGUE PLAYER CONTRIBUTIONS USING INTERACTIVE NETWORK SIMULATION

    Directory of Open Access Journals (Sweden)

    Jonathan Sargent

    2013-03-01

    Full Text Available This paper focuses on the contribution of Australian Football League (AFL players to their team's on-field network by simulating player interactions within a chosen team list and estimating the net effect on final score margin. A Visual Basic computer program was written, firstly, to isolate the effective interactions between players from a particular team in all 2011 season matches and, secondly, to generate a symmetric interaction matrix for each match. Negative binomial distributions were fitted to each player pairing in the Geelong Football Club for the 2011 season, enabling an interactive match simulation model given the 22 chosen players. Dynamic player ratings were calculated from the simulated network using eigenvector centrality, a method that recognises and rewards interactions with more prominent players in the team network. The centrality ratings were recorded after every network simulation and then applied in final score margin predictions so that each player's match contribution-and, hence, an optimal team-could be estimated. The paper ultimately demonstrates that the presence of highly rated players, such as Geelong's Jimmy Bartel, provides the most utility within a simulated team network. It is anticipated that these findings will facilitate optimal AFL team selection and player substitutions, which are key areas of interest to coaches. Network simulations are also attractive for use within betting markets, specifically to provide information on the likelihood of a chosen AFL team list "covering the line".

  14. Structural stability of interaction networks against negative external fields

    Science.gov (United States)

    Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

    2018-04-01

    We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

  15. End of Interactive Emailing from the Technical Network

    CERN Multimedia

    2006-01-01

    According to the CNIC Security Policy for Control Systems (EDMS #584092), interactive emailing on PCs (and other devices) connected to the Technical Network is prohibited. Please note that from November 6th, neither reading emails nor sending emails interactively using e.g. Outlook or Pine mail clients on PCs connected to the Technical Network will be possible anymore. However, automatically generated emails will not be blocked and can still be sent off using CERNMX.CERN.CH as mail server. These restrictions DO NOT apply to PCs connected to any other network, like the General Purpose (or office) network. If you have questions, please do not hesitate to contact Uwe Epting, Pierre Charrue or Stefan Lueders (Technical-Network.Administrator@cern.ch). Your CNIC Working Group

  16. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    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…

  17. Evidence of probabilistic behaviour in protein interaction networks.

    Science.gov (United States)

    Ivanic, Joseph; Wallqvist, Anders; Reifman, Jaques

    2008-01-31

    Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  18. Evidence of probabilistic behaviour in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2008-01-01

    Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  19. Network traffic intelligence using a low interaction honeypot

    Science.gov (United States)

    Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

    2017-11-01

    Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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. (paper)

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

    Science.gov (United States)

    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.

  2. Geometric de-noising of protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Oleksii Kuchaiev

    2009-08-01

    Full Text Available Understanding complex networks of protein-protein interactions (PPIs is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H, tandem affinity purification (TAP and other high-throughput methods for protein-protein interaction (PPI detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

  3. Interactively Evolving Compositional Sound Synthesis Networks

    DEFF Research Database (Denmark)

    Jónsson, Björn Þór; Hoover, Amy K.; Risi, Sebastian

    2015-01-01

    While the success of electronic music often relies on the uniqueness and quality of selected timbres, many musicians struggle with complicated and expensive equipment and techniques to create their desired sounds. Instead, this paper presents a technique for producing novel timbres that are evolved...... the space of potential sounds that can be generated through such compositional sound synthesis networks (CSSNs). To study the effect of evolution on subjective appreciation, participants in a listener study ranked evolved timbres by personal preference, resulting in preferences skewed toward the first...

  4. Evolution of protein-protein interaction networks in yeast.

    Directory of Open Access Journals (Sweden)

    Andrew Schoenrock

    Full Text Available Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE, which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

  5. Predicting genetic interactions with random walks on biological networks

    Directory of Open Access Journals (Sweden)

    Singh Ambuj K

    2009-01-01

    Full Text Available Abstract 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

  6. Snapshots of biodiversity in Georgia agroecosystems

    Science.gov (United States)

    Georgia agricultural landscapes are composed of a diversity of commodities. Here we present biodiversity and biotic interaction data from multiple agricultural systems including: cotton, corn, peanut, blueberry and non-cropping wildflower areas over multiple years. Our goal is to better understand t...

  7. Georgia Public Health Laboratory, Decatur, Georgia

    Energy Technology Data Exchange (ETDEWEB)

    2002-12-01

    This case study was prepared as one in a series for the Laboratories for the 21st Century program, a joint endeavor of the U.S. Environmental Protection Agency and the U.S. Department of Energy's Federal Energy Management Program. The goal of this program is to foster greater energy efficiency in new and retrofit laboratory buildings in both the public and the private sectors. The energy-efficient elements of the laboratory featured in this case study-the Georgia Public Health Laboratory, Decatur, Georgia-include sustainable design features, light-filled interior spaces for daylighting, closely grouped loads (such as freezers), the use of recirculated air in administrative areas, direct digital controls for heating and cooling equipment, sunscreens, and low-emissivity window glazing. These elements, combined with an attractive design and well-lighted work spaces, add up to a building that ranks high in comfort and low in energy use.

  8. Point Process Modeling for Directed Interaction Networks

    Science.gov (United States)

    2011-10-01

    Enron corporation between 1998 and 2002. These e-mail interaction data give rise to the following questions: Homophily To what extent are traits shared...methods Our example analysis uses publicly available data from the Enron e-mail corpus (Cohen, 2009), a large subset of the e-mail messages sent within...the Enron corporation between 1998 and 2002, and made public as the result of a subpoena by the U.S. Federal Energy Regulatory Commission during an

  9. Interacting epidemics and coinfection on contact networks.

    Directory of Open Access Journals (Sweden)

    M E J Newman

    Full Text Available The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend.

  10. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  11. Synchronization of fractional fuzzy cellular neural networks with interactions

    Science.gov (United States)

    Ma, Weiyuan; Li, Changpin; Wu, Yujiang; Wu, Yongqing

    2017-10-01

    In this paper, we introduce fuzzy theory into the fractional cellular neural networks to dynamically enhance the coupling strength and propose a fractional fuzzy neural network model with interactions. Using the Lyapunov principle of fractional differential equations, we design the adaptive control schemes to realize the synchronization and obtain the synchronization criteria. Finally, we provide some numerical examples to show the effectiveness of our obtained results.

  12. Protein complexes predictions within protein interaction networks using genetic algorithms.

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  13. FACETS: multi-faceted functional decomposition of protein interaction networks

    Science.gov (United States)

    Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

    2012-01-01

    Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

  14. Evaluating Australian football league player contributions using interactive network simulation.

    Science.gov (United States)

    Sargent, Jonathan; Bedford, Anthony

    2013-01-01

    This paper focuses on the contribution of Australian Football League (AFL) players to their team's on-field network by simulating player interactions within a chosen team list and estimating the net effect on final score margin. A Visual Basic computer program was written, firstly, to isolate the effective interactions between players from a particular team in all 2011 season matches and, secondly, to generate a symmetric interaction matrix for each match. Negative binomial distributions were fitted to each player pairing in the Geelong Football Club for the 2011 season, enabling an interactive match simulation model given the 22 chosen players. Dynamic player ratings were calculated from the simulated network using eigenvector centrality, a method that recognises and rewards interactions with more prominent players in the team network. The centrality ratings were recorded after every network simulation and then applied in final score margin predictions so that each player's match contribution-and, hence, an optimal team-could be estimated. The paper ultimately demonstrates that the presence of highly rated players, such as Geelong's Jimmy Bartel, provides the most utility within a simulated team network. It is anticipated that these findings will facilitate optimal AFL team selection and player substitutions, which are key areas of interest to coaches. Network simulations are also attractive for use within betting markets, specifically to provide information on the likelihood of a chosen AFL team list "covering the line ". Key pointsA simulated interaction matrix for Australian Rules football players is proposedThe simulations were carried out by fitting unique negative binomial distributions to each player pairing in a sideEigenvector centrality was calculated for each player in a simulated matrix, then for the teamThe team centrality measure adequately predicted the team's winning marginA player's net effect on margin could hence be estimated by replacing him in

  15. Ising models of strongly coupled biological networks with multivariate interactions

    Science.gov (United States)

    Merchan, Lina; Nemenman, Ilya

    2013-03-01

    Biological networks consist of a large number of variables that can be coupled by complex multivariate interactions. However, several neuroscience and cell biology experiments have reported that observed statistics of network states can be approximated surprisingly well by maximum entropy models that constrain correlations only within pairs of variables. We would like to verify if this reduction in complexity results from intricacies of biological organization, or if it is a more general attribute of these networks. We generate random networks with p-spin (p > 2) interactions, with N spins and M interaction terms. The probability distribution of the network states is then calculated and approximated with a maximum entropy model based on constraining pairwise spin correlations. Depending on the M/N ratio and the strength of the interaction terms, we observe a transition where the pairwise approximation is very good to a region where it fails. This resembles the sat-unsat transition in constraint satisfaction problems. We argue that the pairwise model works when the number of highly probable states is small. We argue that many biological systems must operate in a strongly constrained regime, and hence we expect the pairwise approximation to be accurate for a wide class of problems. This research has been partially supported by the James S McDonnell Foundation grant No.220020321.

  16. A network-based approach for predicting missing pathway interactions.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    Full Text Available Embedded within large-scale protein interaction networks are signaling pathways that encode response cascades in the cell. Unfortunately, even for well-studied species like S. cerevisiae, only a fraction of all true protein interactions are known, which makes it difficult to reason about the exact flow of signals and the corresponding causal relations in the network. To help address this problem, we introduce a framework for predicting new interactions that aid connectivity between upstream proteins (sources and downstream transcription factors (targets of a particular pathway. Our algorithms attempt to globally minimize the distance between sources and targets by finding a small set of shortcut edges to add to the network. Unlike existing algorithms for predicting general protein interactions, by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions. We applied our method to extend pathways in osmotic stress response in yeast and identified several missing interactions, some of which are supported by published reports. We also performed experiments that support a novel interaction not previously reported. Our framework is general and may be applicable to edge prediction problems in other domains.

  17. Prelimbic and infralimbic prefrontal cortex interact during fast network oscillations.

    Directory of Open Access Journals (Sweden)

    Karlijn I van Aerde

    Full Text Available BACKGROUND: The medial prefrontal cortex has been implicated in a variety of cognitive and executive processes such as decision making and working memory. The medial prefrontal cortex of rodents consists of several areas including the prelimbic and infralimbic cortex that are thought to be involved in different aspects of cognitive performance. Despite the distinct roles in cognitive behavior that have been attributed to prelimbic and infralimbic cortex, little is known about neuronal network functioning of these areas, and whether these networks show any interaction during fast network oscillations. METHODOLOGY/PRINCIPAL FINDINGS: Here we show that fast network oscillations in rat infralimbic cortex slices occur at higher frequencies and with higher power than oscillations in prelimbic cortex. The difference in oscillation frequency disappeared when prelimbic and infralimbic cortex were disconnected. CONCLUSIONS/SIGNIFICANCE: Our data indicate that neuronal networks of prelimbic and infralimbic cortex can sustain fast network oscillations independent of each other, but suggest that neuronal networks of prelimbic and infralimbic cortex are interacting during these oscillations.

  18. Causal interactions in resting-state networks predict perceived loneliness.

    Science.gov (United States)

    Tian, Yin; Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

    2017-01-01

    Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

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

    Science.gov (United States)

    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.

  20. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

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

    Directory of Open Access Journals (Sweden)

    Preecha Patumcharoenpol

    2016-03-01

    Full Text Available 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

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

    Science.gov (United States)

    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).

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

    Science.gov (United States)

    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…

  4. Exploring Classroom Interaction with Dynamic Social Network Analysis

    Science.gov (United States)

    Bokhove, Christian

    2018-01-01

    This article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and…

  5. Characterizing interactions in online social networks during exceptional events

    Science.gov (United States)

    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.

  6. Drug-domain interaction networks in myocardial infarction.

    Science.gov (United States)

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco; Zhao, Xing-Ming

    2013-09-01

    It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p drugs, their known targets, and seemingly unrelated proteins can be revealed.

  7. Learning Neuroscience: An Interactive Case-Based Online Network (ICON).

    Science.gov (United States)

    Quattrochi, James J.; Pasquale, Susan; Cerva, Barbara; Lester, John E.

    2002-01-01

    Presents an interactive, case-based online network (ICON) that provides a learning environment that integrates student thinking across different concentration tracks and allows students to get away from interpreting vast amounts of available information, move toward selecting useful information, recognize discriminating findings, and build a…

  8. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  9. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  10. Response of the mosquito protein interaction network to dengue infection

    Directory of Open Access Journals (Sweden)

    Pike Andrew D

    2010-06-01

    Full Text Available Abstract Background Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions. Results We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 Aedes aegypti proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT, immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0% randomly selected genes. Conclusions Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission.

  11. Hubs with network motifs organize modularity dynamically in the protein-protein interaction network of yeast.

    Science.gov (United States)

    Jin, Guangxu; Zhang, Shihua; Zhang, Xiang-Sun; Chen, Luonan

    2007-11-21

    It has been recognized that modular organization pervades biological complexity. Based on network analysis, 'party hubs' and 'date hubs' were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of 'party hubs' and 'date hubs'. Thus, an open question has been raised as to whether or not 'party hubs' and 'date hubs' truly exist in yeast interactome. In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub's network motifs and protein complexes, we define two new types of hubs, 'motif party hubs' and 'motif date hubs', which have the same characteristics as the original 'party hubs' and 'date hubs' respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity. By virtue of network motifs, we basically solved the open question about 'party hubs' and 'date hubs' which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks.

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

    Directory of Open Access Journals (Sweden)

    Xia Jiang

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

  13. Wind Power in Georgia

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2012-11-01

    Georgia has good wind power potential. Preliminary analyses show that the technical wind power potential in Georgia is good. Meteorological data shows that Georgia has four main areas in Georgia with annual average wind speeds of over 6 m/s and two main areas with 5-6 m/s at 80m. The most promising areas are the high mountain zone of the Great Caucasus, The Kura river valley, The South-Georgian highland and the Southern part of the Georgian Black Sea coast. Czech company Wind Energy Invest has recently signed a Memorandum of Understanding with Georgian authorities for development of the first wind farm in Georgia, a 50MW wind park in Paravani, Southern Georgia, to be completed in 2014. Annual generation is estimated to 170.00 GWh and the investment estimated to 101 million US$. Wind power is suited to balance hydropower in the Georgian electricity sector Electricity generation in Georgia is dominated by hydro power, constituting 88% of total generation in 2009. Limited storage capacity and significant spring and summer peaks in river flows result in an uneven annual generation profile and winter time shortages that are covered by three gas power plants. Wind power is a carbon-free energy source well suited to balance hydropower, as it is available (often strongest) in the winter and can be exported when there is a surplus. Another advantage with wind power is the lead time for the projects; the time from site selection to operation for a wind power park (approximately 2.5 years) is much shorter than for hydro power (often 6-8 years). There is no support system or scheme for renewable sources in Georgia, so wind power has to compete directly with other energy sources and is in most cases more expensive to build than hydro power. In a country and region with rapidly increasing energy demands, the factors described above nevertheless indicate that there is a commercial niche and a role to play for Georgian wind power. Skra: An example of a wind power development

  14. Exact tensor network ansatz for strongly interacting systems

    Science.gov (United States)

    Zaletel, Michael P.

    It appears that the tensor network ansatz, while not quite complete, is an efficient coordinate system for the tiny subset of a many-body Hilbert space which can be realized as a low energy state of a local Hamiltonian. However, we don't fully understand precisely which phases are captured by the tensor network ansatz, how to compute their physical observables (even numerically), or how to compute a tensor network representation for a ground state given a microscopic Hamiltonian. These questions are algorithmic in nature, but their resolution is intimately related to understanding the nature of quantum entanglement in many-body systems. For this reason it is useful to compute the tensor network representation of various `model' wavefunctions representative of different phases of matter; this allows us to understand how the entanglement properties of each phase are expressed in the tensor network ansatz, and can serve as test cases for algorithm development. Condensed matter physics has many illuminating model wavefunctions, such as Laughlin's celebrated wave function for the fractional quantum Hall effect, the Bardeen-Cooper-Schrieffer wave function for superconductivity, and Anderson's resonating valence bond ansatz for spin liquids. This thesis presents some results on exact tensor network representations of these model wavefunctions. In addition, a tensor network representation is given for the time evolution operator of a long-range one-dimensional Hamiltonian, which allows one to numerically simulate the time evolution of power-law interacting spin chains as well as two-dimensional strips and cylinders.

  15. Digital Ecology: Coexistence and Domination among Interacting Networks

    Science.gov (United States)

    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.

  16. Digital Ecology: Coexistence and Domination among Interacting Networks

    Science.gov (United States)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-01-01

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

  17. My Classroom: Georgia

    Science.gov (United States)

    Baker, Melanie

    2017-01-01

    Khatuna Kharkheli is an English language teacher in the Faculty of Education, Exact, and Natural Sciences at Gori State Teaching University (GSTU) in Gori, Georgia. With her passion for developing innovative and creative lessons and with her commitment to professional development, Ms. Kharkheli works to inspire her students to achieve success both…

  18. Georgia's Teacher Performance Assessment

    Science.gov (United States)

    Fenton, Anne Marie; Wetherington, Pamela

    2016-01-01

    Like most states, Georgia until recently depended on an assessment of content knowledge to award teaching licenses, along with a licensure recommendation from candidates' educator preparation programs. While the content assessment reflected candidates' grasp of subject matter, licensure decisions did not hinge on direct, statewide assessment of…

  19. Georgia Street Reconstruction

    OpenAIRE

    Boyce, Ken

    2012-01-01

    Georgia Street between Conseco Fieldhouse and the Indiana Convention Center has been completely rebuilt to function as the centerpiece for the 2012 Super Bowl Village. It showcases “green” infrastructure components and is complete with outdoor dining and entertainment, as well as overhead sound and lighting systems.

  20. Georgia: natural energy resources

    OpenAIRE

    Chomakhidze, Demur

    2007-01-01

    This article examines Georgia's natural fuel and energy resources (FER), both conventional (hydropower, oil, gas, coal) and non-conventional (alternative). Special attention is paid to hydropower and to alternative energy sources. The author assesses the current level of their development in the republic.

  1. Self-organization of social hierarchy on interaction networks

    International Nuclear Information System (INIS)

    Fujie, Ryo; Odagaki, Takashi

    2011-01-01

    In order to examine the effects of interaction network structures on the self-organization of social hierarchy, we introduce the agent-based model: each individual as on a node of a network has its own power and its internal state changes by fighting with its neighbors and relaxation. We adopt three different networks: regular lattice, small-world network and scale-free network. For the regular lattice, we find the emergence of classes distinguished by the internal state. The transition points where each class emerges are determined analytically, and we show that each class is characterized by the local ranking relative to their neighbors. We also find that the antiferromagnetic-like configuration emerges just above the critical point. For the heterogeneous networks, individuals become winners (or losers) in descending order of the number of their links. By using mean-field analysis, we reveal that the transition point is determined by the maximum degree and the degree distribution in its neighbors

  2. Auditing Medical Records Accesses via Healthcare Interaction Networks

    Science.gov (United States)

    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

  3. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

  4. Cytoscape Web: an interactive web-based network browser.

    Science.gov (United States)

    Lopes, Christian T; Franz, Max; Kazi, Farzana; Donaldson, Sylva L; Morris, Quaid; Bader, Gary D

    2010-09-15

    Cytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/.

  5. Effects of peer network interactions on adolescent cannabis use

    OpenAIRE

    Moriarty, John; Higgins, Kathryn

    2015-01-01

    PurposeThis study capitalises on three waves of longitudinal data from a cohort of 4351 secondary school pupils to examine the effects on individuals’ cannabis use uptake of both peer cannabis use and position within a peer network.Design/methodology/approachBoth cross-sectional and individual fixed effects models are used to estimate the effect on cannabis use of nominated friends’ cannabis use, of reciprocity and transitivity of nominations across the friendship cluster, and of interactions...

  6. Consensus of Multiagent Networks with Intermittent Interaction and Directed Topology

    Directory of Open Access Journals (Sweden)

    Li Xiao

    2014-01-01

    Full Text Available Intermittent interaction control is introduced to solve the consensus problem for second-order multiagent networks due to the limited sensing abilities and environmental changes periodically. And, we get some sufficient conditions for the agents to reach consensus with linear protocol from the theoretical findings by using the Lyapunov control approach. Finally, the validity of the theoretical results is validated through the numerical example.

  7. Games as Actors - Interaction, Play, Design, and Actor Network Theory

    OpenAIRE

    Jessen, Jari Due; Jessen, Carsten

    2014-01-01

    When interacting with computer games, users are forced to follow the rules of the game in return for the excitement, joy, fun, or other pursued experiences. In this paper, we investigate how games a chieve these experiences in the perspective of Actor Network Theory (ANT). Based on a qualitative data from a study of board games , computer games, and exergames, we conclude that games are actors that produce experiences by exercising power over the user’ s abilities, for example their cognitive...

  8. Reconstituting Protein Interaction Networks Using Parameter-Dependent Domain-Domain Interactions

    Science.gov (United States)

    2013-05-07

    that approximately 80% of eukaryotic proteins and 67% of prokaryotic proteins have multiple domains [13,14]. Most annotation databases characterize...domain annotations, Domain-domain interactions, Protein-protein interaction networks Background The living cell is a dynamic, interconnected system...detailed in Methods. Here, we illustrate its application on a well- annotated single- cell organism. We created a merged set of protein-domain annotations

  9. Network characteristics emerging from agent interactions in balanced distributed system.

    Science.gov (United States)

    Salman, Mahdi Abed; Bertelle, Cyrille; Sanlaville, Eric

    2015-01-01

    A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between system nodes. It aims at enhancing the resource usage. A load balancing strategy specifies scenarios for the cooperation. Its efficiency depends on quantity, accuracy, and distribution of available information. Nevertheless, the distribution of information on the nodes, together with the initial network structure, may create different logical network structures. In this paper, different load balancing strategies are tested on different network structures using a simulation. The four tested strategies are able to distribute evenly the load so that the system reaches a steady state (the mean response time of the jobs is constant), but it is shown that a given strategy indeed behaves differently according to structural parameters and information spreading. Such a study, devoted to distributed computing systems (DCSs), can be useful to understand and drive the behavior of other complex systems.

  10. KNOWNET: Exploring Interactive Knowledge Networking across Insurance Supply Chains

    Directory of Open Access Journals (Sweden)

    Susan Grant

    2014-01-01

    Full Text Available Social media has become an extremely powerful phenomenon with millions of users who post status updates, blog, links and pictures on social networking sites such as Facebook, LinkedIn, and Twitter. However, social networking has so far spread mainly among consumers. Businesses are only now beginning to acknowledge the benefits of using social media to enhance employee and supplier collaboration to support new ideas and innovation through knowledge sharing across functions and organizational boundaries. Many businesses are still trying to understand the various implications of integrating internal communication systems with social media tools and private collaboration and networking platforms. Indeed, a current issue in organizations today is to explore the value of social media mechanisms across a range of functions within their organizations and across their supply chains.The KNOWNET project (an EC funded Marie Curie IAPP seeks to assess the value of social networking for knowledge exchange across Insurance supply chains. A key objective of the project being to develop and build a web based interactive environment - a Supplier Social Network or SSN, to support and facilitate exchange of good ideas, insights, knowledge, innovations etc across a diverse group of suppliers within a multi level supply chain within the Insurance sector.

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

    Science.gov (United States)

    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…

  12. AtPIN: Arabidopsis thaliana Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Silva-Filho Marcio C

    2009-12-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3 which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS (AT5G26710 we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630, a disease resistance protein (AT3G50950 and a zinc finger protein (AT5G24930, which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

  13. Energy balance of Georgia

    Directory of Open Access Journals (Sweden)

    Demur Chomakhidze

    2016-09-01

    It is shown that, traditionally, the energy balance of Georgia is in deep deficit. The suggestions for its improvement are provided in the Article. The country imports almost all amount of oil and natural gas. Electricity balance is relatively stable. In the recent years, some amount of electricity is exported to the neighboring countries. Generally, the country satisfies only 30–35% of own energy consumption by local generation, and the rest amount of resources are imported from abroad. The reason of deficit to some extent is irrational and wasteful consumption of energy resources. The article examines the organizational difficulties in drawing up energy balance of Georgia and statistical problems of recording of energy production and consumption at the initial stage of market economy.

  14. Control of Synchronization Regimes in Networks of Mobile Interacting Agents

    Science.gov (United States)

    Perez-Diaz, Fernando; Zillmer, Ruediger; Groß, Roderich

    2017-05-01

    We investigate synchronization in a population of mobile pulse-coupled agents with a view towards implementations in swarm-robotics systems and mobile sensor networks. Previous theoretical approaches dealt with range and nearest-neighbor interactions. In the latter case, a synchronization-hindering regime for intermediate agent mobility is found. We investigate the robustness of this intermediate regime under practical scenarios. We show that synchronization in the intermediate regime can be predicted by means of a suitable metric of the phase response curve. Furthermore, we study more-realistic K -nearest-neighbor and cone-of-vision interactions, showing that it is possible to control the extent of the synchronization-hindering region by appropriately tuning the size of the neighborhood. To assess the effect of noise, we analyze the propagation of perturbations over the network and draw an analogy between the response in the hindering regime and stable chaos. Our findings reveal the conditions for the control of clock or activity synchronization of agents with intermediate mobility. In addition, the emergence of the intermediate regime is validated experimentally using a swarm of physical robots interacting with cone-of-vision interactions.

  15. Exploring the Interactions Between Network Data Analysis and Security Information/Event Management

    Science.gov (United States)

    2011-01-01

    2011 Carnegie Mellon University Exploring the Interactions Between Network Data Analysis and Security Information/ Event Management Timothy J...AND SUBTITLE Exploring the Interactions Between Network Data Analysis and Security Information/ Event Management 5a. CONTRACT NUMBER 5b. GRANT...Network Data Security Information/ Events The Problem Events , Revisited Analysis leading to Events The Problem, Revisited Summary 4 Network Data larger

  16. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

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

    Science.gov (United States)

    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.

  18. Plants, mycorrhizal fungi, and bacteria: a network of interactions.

    Science.gov (United States)

    Bonfante, Paola; Anca, Iulia-Andra

    2009-01-01

    This review focuses on interactions among plants, mycorrhizal fungi, and bacteria, testing the hypothesis whether mycorrhizas can be defined as tripartite associations. After summarizing the main biological features of mycorrhizas, we illustrate the different types of interaction occurring between mycorrhizal fungi and bacteria, from loosely associated microbes to endobacteria. We then discuss, in the context of nutritional strategies, the mechanisms that operate among members of the consortium and that often promote plant growth. Release of active molecules, including volatiles, and physical contact among the partners seem important for the establishment of the bacteria/mycorrhizal fungus/plant network. The potential involvement of quorum sensing and Type III secretion systems is discussed, even if the exact nature of the complex interspecies/interphylum interactions remains unclear.

  19. Peptide microarrays to probe for competition for binding sites in a protein interaction network

    NARCIS (Netherlands)

    Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

    2013-01-01

    Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

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

    Directory of Open Access Journals (Sweden)

    Paul Pawelzyk

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    Hadiseh Safdari

    Full Text Available 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.

  2. A Physical Interaction Network of Dengue Virus and Human Proteins*

    Science.gov (United States)

    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.

    2011-01-01

    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. PMID:21911577

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

    Science.gov (United States)

    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.

  4. Interactive social contagions and co-infections on complex networks

    Science.gov (United States)

    Liu, Quan-Hui; Zhong, Lin-Feng; Wang, Wei; Zhou, Tao; Eugene Stanley, H.

    2018-01-01

    What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

  5. Supply Chain Management: from Linear Interactions to Networked Processes

    Directory of Open Access Journals (Sweden)

    Doina FOTACHE

    2006-01-01

    Full Text Available Supply Chain Management is a distinctive product, with a tremendous impact on the software applications market. SCM applications are back-end solutions intended to link suppliers, manufacturers, distributors and resellers in a production and distribution network, which allows the enterprise to track and consolidate the flows of materials and data trough the process of manufacturing and distribution of goods/services. The advent of the Web as a major means of conducting business transactions and business-tobusiness communications, coupled with evolving web-based supply chain management (SCM technology, has resulted in a transition period from “linear” supply chain models to "networked" supply chain models. The technologies to enable dynamic process changes and real time interactions between extended supply chain partners are emerging and being deployed at an accelerated pace.

  6. Visualization of protein interaction networks: problems and solutions

    Science.gov (United States)

    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. Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

    Science.gov (United States)

    Podder, Avijit; Jatana, Nidhi; Latha, N

    2014-09-21

    Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Measuring Interactions in Networks: Evidence from Soccer Teams

    OpenAIRE

    Unlu, Emre; Sarangi, Sudipta; Dev, Pritha

    2013-01-01

    This paper contributes to the literature on centrality measures in economics by defining a team game and identifying the key players in the game. As an illustration of the theory, we create a unique data set from the UEFA Euro 2008 tournament. To capture the interaction between players, we create the passing network of each team. This all allows us to identify the key player and key groups of players for both teams in each game. We then use our measure to explain player ratings by experts and...

  9. Games as Actors - Interaction, Play, Design, and Actor Network Theory

    DEFF Research Database (Denmark)

    Jessen, Jari Due; Jessen, Carsten

    2014-01-01

    When interacting with computer games, users are forced to follow the rules of the game in return for the excitement, joy, fun, or other pursued experiences. In this paper, we investigate how games a chieve these experiences in the perspective of Actor Network Theory (ANT). Based on a qualitative...... data from a study of board games , computer games, and exergames, we conclude that games are actors that produce experiences by exercising power over the user’ s abilities, for example their cognitive functions. Games are designed to take advantage of the characteristics of the human players...

  10. IDPs in the new Georgia

    Directory of Open Access Journals (Sweden)

    Freya von Groote

    2005-05-01

    Full Text Available Secession of the Abkhazia and South Ossetia regions of Georgia in the early 1990s displaced over a quarter of a million Georgians, many of whom remain in collective shelters. As Georgia embraces democracy, what can be done to resolve the country’s protracted IDP crisis?

  11. Landslide Hazard in Georgia

    Science.gov (United States)

    Gaprindashvili, George; Tsereteli, Emil; Gaprindashvili, Merab

    2014-05-01

    In the last decades of the XX century, protect the population from geological hazards, to maintain land and safe operation of the engineering facilities has become the most important social - economic, demographic, political and environmental problems for the whole world. Georgia, with its scales of origination of the natural-catastrophic processes (landslide, mudflow, rockfall, erosion and etc.), their re-occurrence and with the negative results inflicted by these processes to the population, agricultural lands and engineering objects, is one of the most complex mountainous region. The extremely sensitive conditions were conditioned by: 1. Activation of highly intense earthquakes; 2. Activation of the negative meteorological events provoking the disaster processes on the background of global climatic changes and their abnormally frequent occurrence (mostly increased atmospheric precipitations, temperature and humidity); 3. Large-scale Human impact on the environment. Following the problem urgency, a number of departmental and research institutions have made their operations more intense in the given direction within the limits of their competence. First of all, the activity of the Department of Geology of Georgia (which is at present included in the National Environmental Agency of the Ministry of Environment and Natural Resources Protection), which mapped, identified and cataloged the hazardous processes on the territory of the country and identified the spatial limits and developmental regularities of these processes for tens of years. The increased risk of Geological catastrophes in Georgia first of all is caused by insufficient information between society and responsible persons toward this event. The existed situation needs the base assessment of natural disasters level, the identification of events, to determine their caused reasons, to develop special maps in GIS system, and continuous functioning of geo monitoring researches for develop safety early

  12. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  13. The paradox of caffeine-zolpidem interaction: a network analysis.

    Science.gov (United States)

    Myslobodsky, Michael

    2009-10-01

    A widely prescribed and potent short-acting hypnotic, zolpidem has become the mainstay for the treatment of middle-of-the-night sleeplessness. It is expected to be antagonized by caffeine. Paradoxically, in some cases caffeine appears to slightly enhance zolpidem sedation. The pharmacokinetic and pharmacodynamic nature of this odd effect remains unexplored. The purpose of this study is to reproduce a hypothetical molecular network recruited by caffeine when co-administered with zolpidem using Ingenuity Pathway Analysis. Thus generated, network drew attention to several possible contributors to caffeine sedation, such as tachykinin precursor 1, cannabinoid, and GABA receptors. The present overview is centered on the possibility that caffeine potentiation of zolpidem sedation does not involve a centralized interaction of specific neurotransmitters, but rather is contributed by its antioxidant capacity. It is proposed that by modifying the cellular redox state, caffeine ultimately reduces the pool of reactive oxygen species, thereby increasing the bioavailability of endogenous melatonin for interaction with zolpidem. This side effect of caffeine encourages further studies of multiple antioxidants as an attractive way to potentially increasing somnolence.

  14. Community structure of non-coding RNA interaction network

    Directory of Open Access Journals (Sweden)

    Nacher Jose C.

    2013-06-01

    Full Text Available Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between noncoding RNAs (ncRNAs and proteins related bio-macrolecules (PRMs using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell.

  15. Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network

    Directory of Open Access Journals (Sweden)

    Andrews Brenda

    2005-06-01

    Full Text Available Abstract Background Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction. Results To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' – classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions – the 'compensatory complexes' theme. Thematic maps – networks rendered in terms of such themes – can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes. Conclusion Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships.

  16. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

    Science.gov (United States)

    Li, Ruowang; Dudek, Scott M; Kim, Dokyoon; Hall, Molly A; Bradford, Yuki; Peissig, Peggy L; Brilliant, Murray H; Linneman, James G; McCarty, Catherine A; Bao, Le; Ritchie, Marylyn D

    2016-01-01

    The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Developing the computational tools to explore genetic associations beyond main

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  19. Social Networking Sites as Communication, Interaction, and Learning Environments: Perceptions and Preferences of Distance Education Students

    Science.gov (United States)

    Bozkurt, Aras; Karadeniz, Abdulkadir; Kocdar, Serpil

    2017-01-01

    The advent of Web 2.0 technologies transformed online networks into interactive spaces in which user-generated content has become the core material. With the possibilities that emerged from Web 2.0, social networking sites became very popular. The capability of social networking sites promises opportunities for communication and interaction,…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

  3. Modeling attacker-defender interactions in information networks.

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  5. NatalieQ: a web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background: Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  6. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

    Full Text Available 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%.

  7. Co-creating value through agents interaction within service network

    Energy Technology Data Exchange (ETDEWEB)

    Okdinawati, L.; Simatupang, T.M.; Sunitiyoso, Y.

    2017-07-01

    The purpose of this paper is to gives further understanding on value co-creation mechanisms in B-to-B service network by reinforcing the processes, the relationships, and influences of other agents where Collaborative Transportation Management (CTM) forms might be best employed. Design/methodology/approach: In order to model the interactions among agents in the collaboration processes and the value co-creation processes, this research used three collaboration cases in Indonesia. Then, the agent-based simulation was used to capture both the collaboration process and the value co-creation process of the three collaboration cases. Findings: The interactions among the agents both inside and outside their collaboration environment determined agent’s role as a value co-creator. The willingness of an agent to accept the opinion of another agent determined the degree of their willingness to co-operate and to change their strategies, and perceptions. Therefore, influenced the size of the value obtained by them in each collaboration process. Research limitations/implications: The findings of the simulations subject to assumptions based on the collaboration cases. Further research is related to how to encourage agents to co-operate and adjust their perceptions. Practical implications: It is crucial for the practitioners to interact with another agent both inside and outside their collaboration environment. The opinions of another agent inside the collaboration environment also need to be considered. Originality/value: This research is derived from its emphasis on how a value is co-created by reinforcing both the collaborative processes and the interactions among agents as well as on how CTM might be best employed.

  8. Fire blight in Georgia

    Directory of Open Access Journals (Sweden)

    Dali L. Gaganidze

    2018-03-01

    Full Text Available Fire blight is distinguished among the fruit tree diseases by harmfulness. Fire blight damages about 180 cultural and wild plants belonging to the Rosaceae family. Quince, apple and pear are the most susceptible to the disease. At present, the disease occurs in over 40 countries of Europe and Asia. Economic damage caused by fire blight is expressed not only in crop losses, but also, it poses threat of eradication to entire fruit tree gardens. Erwinia amylovora, causative bacteria of fire blight in fruit trees, is included in the A2 list of quarantine organisms. In 2016, the employees of the Plant Pest Diagnostic Department of the Laboratory of the Georgian Ministry of Agriculture have detected Erwinia amylovora in apple seedlings from Mtskheta district. National Food Agency, Ministry of Agriculture of Georgia informed FAO on pathogen detection. The aim of the study is detection of the bacterium Erwinia amylovora by molecular method (PCR in the samples of fruit trees, suspicious on fire blight collected in the regions of Eastern (Kvemo Kartli, Shida Kartli and Kakheti and Western Georgia (Imereti.The bacterium Erwinia amylovora was detected by real time and conventional PCR methods using specific primers and thus the fire blight disease confirmed in 23 samples of plant material from Shida Kartli (11 apples, 6 pear and 6 quince samples, in 5 samples from Kvemo Kartli (1 quince and 4 apple samples, in 2 samples of apples from Kakheti region and 1 sample of pear collected in Imereti (Zestafoni. Keywords: Fire blight, Erwinia amylovora, Conventional PCR, Real time PCR, DNA, Bacterium

  9. SMEX03 Vegetation Data: Georgia

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes data collected over the Soil Moisture Experiment 2003 (SMEX03) area of Georgia, USA between 16 June and 21 July 2003. The parameters measured...

  10. Distribution of Georgia Oyster Reefs

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The feature class in this ESRI Geodatabase contains polygons representing oyster reefs along the Georgia coastal waterways from Chatham County south to Glynn County....

  11. 2009 Chatham County Georgia Lidar

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR generated point cloud acquired in spring 2009 for Chatham County, Georgia for the Metropolitan Planning Commission. The data are classified as follows: Class 1...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2014-10-01

    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.

  15. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  16. Weighted Protein Interaction Network Analysis of Frontotemporal Dementia.

    Science.gov (United States)

    Ferrari, Raffaele; Lovering, Ruth C; Hardy, John; Lewis, Patrick A; Manzoni, Claudia

    2017-02-03

    The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein-protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

  17. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  18. Cooperation in networks where the learning environment differs from the interaction environment.

    Science.gov (United States)

    Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Weissing, Franz J

    2014-01-01

    We study the evolution of cooperation in a structured population, combining insights from evolutionary game theory and the study of interaction networks. In earlier studies it has been shown that cooperation is difficult to achieve in homogeneous networks, but that cooperation can get established relatively easily when individuals differ largely concerning the number of their interaction partners, such as in scale-free networks. Most of these studies do, however, assume that individuals change their behaviour in response to information they receive on the payoffs of their interaction partners. In real-world situations, subjects do not only learn from their interaction partners, but also from other individuals (e.g. teachers, parents, or friends). Here we investigate the implications of such incongruences between the 'interaction network' and the 'learning network' for the evolution of cooperation in two paradigm examples, the Prisoner's Dilemma game (PDG) and the Snowdrift game (SDG). Individual-based simulations and an analysis based on pair approximation both reveal that cooperation will be severely inhibited if the learning network is very different from the interaction network. If the two networks overlap, however, cooperation can get established even in case of considerable incongruence between the networks. The simulations confirm that cooperation gets established much more easily if the interaction network is scale-free rather than random-regular. The structure of the learning network has a similar but much weaker effect. Overall we conclude that the distinction between interaction and learning networks deserves more attention since incongruences between these networks can strongly affect both the course and outcome of the evolution of cooperation.

  19. Integration and visualization of non-coding RNA and protein interaction networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    Association and Interaction Networks) - a database that combines ncRNA-ncRNA, ncRNA-mRNA and ncRNA-protein interactions with large-scale protein association networks available in the STRING database. By integrating ncRNA and protein networks, RAIN provides a more complete picture of the cell’s complex...... interaction network. RAIN aggregates associations and (predicted) interactions of a vast collection of ncRNA classes, including microRNAs and long ncRNAs, collected from a wide range of resources: a) curated knowledge, b) experimentally supported interactions, c) predicted microRNA-target interactions, and d......) co-occurrences found by text mining Medline abstracts. Each resource was assigned a reliability score by assessing its agreement with a gold standard set of microRNA-target interactions. RAIN is available at: http://rth.dk/resources/rain...

  20. Fading characterization for context aware body area networks (caban) in interactive smart environments

    NARCIS (Netherlands)

    Heaney, S.F.; Scanlon, W.G.; Garcia-Palacios, E.; Cotton, S.L.

    2010-01-01

    Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains. This presents the possibility of creating interactive smart environments where Context Aware Body Area Networks can sense and co-operate with

  1. Interaction in agent-based economics: A survey on the network approach

    Science.gov (United States)

    Bargigli, Leonardo; Tedeschi, Gabriele

    2014-04-01

    In this paper we aim to introduce the reader to some basic concepts and instruments used in a wide range of economic networks models. In particular, we adopt the theory of random networks as the main tool to describe the relationship between the organization of interaction among individuals within different components of the economy and overall aggregate behavior. The focus is on the ways in which economic agents interact and the possible consequences of their interaction on the system. We show that network models are able to introduce complex phenomena in economic systems by allowing for the endogenous evolution of networks.

  2. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

  3. Antagonistic interaction networks are structured independently of latitude and host guild

    Science.gov (United States)

    Morris, Rebecca J; Gripenberg, Sofia; Lewis, Owen T; Roslin, Tomas

    2014-01-01

    An increase in species richness with decreasing latitude is a prominent pattern in nature. However, it remains unclear whether there are corresponding latitudinal gradients in the properties of ecological interaction networks. We investigated the structure of 216 quantitative antagonistic networks comprising insect hosts and their parasitoids, drawn from 28 studies from the High Arctic to the tropics. Key metrics of network structure were strongly affected by the size of the interaction matrix (i.e. the total number of interactions documented between individuals) and by the taxonomic diversity of the host taxa involved. After controlling for these sampling effects, quantitative networks showed no consistent structural patterns across latitude and host guilds, suggesting that there may be basic rules for how sets of antagonists interact with resource species. Furthermore, the strong association between network size and structure implies that many apparent spatial and temporal variations in network structure may prove to be artefacts. PMID:24354432

  4. Non-criticality of interaction network over system's crises: A percolation analysis.

    Science.gov (United States)

    Shirazi, Amir Hossein; Saberi, Abbas Ali; Hosseiny, Ali; Amirzadeh, Ehsan; Toranj Simin, Pourya

    2017-11-20

    Extraction of interaction networks from multi-variate time-series is one of the topics of broad interest in complex systems. Although this method has a wide range of applications, most of the previous analyses have focused on the pairwise relations. Here we establish the potential of such a method to elicit aggregated behavior of the system by making a connection with the concepts from percolation theory. We study the dynamical interaction networks of a financial market extracted from the correlation network of indices, and build a weighted network. In correspondence with the percolation model, we find that away from financial crises the interaction network behaves like a critical random network of Erdős-Rényi, while close to a financial crisis, our model deviates from the critical random network and behaves differently at different size scales. We perform further analysis to clarify that our observation is not a simple consequence of the growth in correlations over the crises.

  5. Georgia-Armenia Transboarder seismicity studies

    Science.gov (United States)

    Godoladze, T.; Tvaradze, N.; Javakishvili, Z.; Elashvili, M.; Durgaryan, R.; Arakelyan, A.; Gevorgyan, M.

    2012-12-01

    In the presented study we performed Comprehensive seismic analyses for the Armenian-Georgian transboarder active seismic fault starting on Armenian territory, cutting the state boarder and having possibly northern termination on Adjara-Triealeti frontal structure in Georgia. In the scope of International projects: ISTC A-1418 "Open network of scientific Centers for mitigation risk of natural hazards in the Southern Caucasus and Central Asia" and NATO SfP- 983284 Project "Caucasus Seismic Emergency Response" in Akhalkalaki (Georgia) seismic center, Regional Summer school trainings and intensive filed investigations were conducted. Main goal was multidisciplinary study of the Javakheti fault structure and better understanding seismicity of the area. Young scientists from Turkey, Armenia, Azerbaijan and Georgia were participated in the deployment of temporal seismic network in order to monitor seisimity on the Javakheti highland and particularly delineate fault scarf and identify active seismic structures. In the scope of international collaboration the common seismic database has been created in the southern Caucasus and collected data from the field works is available now online. Javakheti highland, which is located in the central part of the Caucasus, belongs to the structure of the lesser Caucasus and represents a history of neotectonic volcanism existed in the area. Jasvakheti highland is seismicalu active region devastating from several severe earthquakes(1088, 1283, 1899…). Hypocenters located during analogue network were highly scattered and did not describe real pattern of seismicity of the highland. We relocated hypocenters of the region and improved local velocity model. The hypocenters derived from recently deployed local seismic network in the Javakheti highland, clearly identified seismically active structures. Fault plane solutions of analogue data of the Soviet times have been carefully analyzed and examined. Moment tensor inversion were preformed

  6. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs

    Science.gov (United States)

    2014-01-01

    Background In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located. Results By analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance. Conclusions We conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory

  7. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs.

    Science.gov (United States)

    Fadhal, Emad; Gamieldien, Junaid; Mwambene, Eric C

    2014-01-18

    In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located. By analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance. We conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence

  8. Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining.

    Science.gov (United States)

    Hur, Junguk; Ozgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2012-12-20

    Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since

  9. Interacting with Networks : How Does Structure Relate to Controllability in Single-Leader, Consensus Networks?

    NARCIS (Netherlands)

    Egerstedt, Magnus; Martini, Simone; Cao, Ming; Camlibel, Kanat; Bicchi, Antonio

    As networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multiagent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to

  10. Interaction network of vascular epiphytes and trees in a subtropical forest

    Science.gov (United States)

    Ceballos, Sergio Javier; Chacoff, Natacha Paola; Malizia, Agustina

    2016-11-01

    The commensalistic interaction between vascular epiphytes and host trees is a type of biotic interaction that has been recently analysed with a network approach. This approach is useful to describe the network structure with metrics such as nestedness, specialization and interaction evenness, which can be compared with other vascular epiphyte-host tree networks from different forests of the world. However, in several cases these comparisons showed different and inconsistent patterns between these networks, and their possible ecological and evolutionary determinants have been scarcely studied. In this study, the interactions between vascular epiphytes and host trees of a subtropical forest of sierra de San Javier (Tucuman, Argentina) were analysed with a network approach. We calculated metrics to characterize the network and we analysed factors such as the abundance of species, tree size, tree bark texture, and tree wood density in order to predict interaction frequencies and network structure. The interaction network analysed exhibited a nested structure, an even distribution of interactions, and low specialization, properties shared with other obligated vascular epiphyte-host tree networks with a different assemblage structure. Interaction frequencies were predicted by the abundance of species, tree size and tree bark texture. Species abundance and tree size also predicted nestedness. Abundance indicated that abundant species interact more frequently; and tree size was an important predictor, since larger-diameter trees hosted more vascular epiphyte species than small-diameter trees. This is one of the first studies analyzing interactions between vascular epiphytes and host trees using a network approach in a subtropical forest, and taking the whole vascular epiphyte assemblage of the sampled community into account.

  11. Semantic integration to identify overlapping functional modules in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules 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 functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, 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. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  12. Prediction of Protein-Protein Interacting Sites: How to Bridge Molecular Events to Large Scale Protein Interaction Networks

    Science.gov (United States)

    Bartoli, Lisa; Martelli, Pier Luigi; Rossi, Ivan; Fariselli, Piero; Casadio, Rita

    Most of the cellular functions are the result of the concerted action of protein complexes forming pathways and networks. For this reason, efforts were devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs. However residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. To this purpose, we developed a neural network-based method that is able to characterize protein complexes, by predicting amino acid residues that mediate the interactions. All the Protein Data Bank (PDB) chains, both in the unbound and in the complexed form, are predicted and the results are stored in a database of interaction surfaces (http://gpcr.biocomp.unibo.it/zenpatches). Finally, we performed a survey on the different computational methods for protein-protein interaction prediction and on their training/testing sets in order to highlight the most informative properties of protein interfaces.

  13. The management of interaction networks. The ???in-between??? concept within social work and counseling

    OpenAIRE

    Hern??ndez-Aristu, Jes??s

    2015-01-01

    We are familiar with the field of group interaction through the traditional work of Kurt Lewin and also systemic thinking talks about network interaction that builds up the system. Martin Buber also discusses the ???in-between??? concept as the third element.The therapist or counselor, social worker and clients are part of an interaction network, representing therapeutic and social working situations. Success in treatment and reflective processes, depends on the perception and managemen...

  14. Integrated network analysis reveals the importance of microbial interactions for maize growth.

    Science.gov (United States)

    Tao, Jiemeng; Meng, Delong; Qin, Chong; Liu, Xueduan; Liang, Yili; Xiao, Yunhua; Liu, Zhenghua; Gu, Yabing; Li, Juan; Yin, Huaqun

    2018-04-01

    Microbes play a critical role in soil global biogeochemical circulation and microbe-microbe interactions have also evoked enormous interests in recent years. Utilization of green manures can stimulate microbial activity and affect microbial composition and diversity. However, few studies focus on the microbial interactions or detect the key functional members in communities. With the advances of metagenomic technologies, network analysis has been used as a powerful tool to detect robust interactions between microbial members. Here, random matrix theory-based network analysis was used to investigate the microbial networks in response to four different green manure fertilization regimes (Vicia villosa, common vetch, milk vetch, and radish) over two growth cycles from October 2012 to September 2014. The results showed that the topological properties of microbial networks were dramatically altered by green manure fertilization. Microbial network under milk vetch amendment showed substantially more intense complexity and interactions than other fertilization systems, indicating that milk vetch provided a favorable condition for microbial interactions and niche sharing. The shift of microbial interactions could be attributed to the changes in some major soil traits and the interactions might be correlated to plant growth and production. With the stimuli of green manures, positive interactions predominated the network eventually and the network complexity was in consistency with maize productivity, which suggested that the complex soil microbial networks might benefit to plants rather than simple ones, because complex networks would hold strong the ability to cope with environment changes or suppress soil-borne pathogen infection on plants. In addition, network analyses discerned some putative keystone taxa and seven of them had directly positive interactions with maize yield, which suggested their important roles in maintaining environmental functions and in improving

  15. A novel interacting multiple model based network intrusion detection scheme

    Science.gov (United States)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  16. Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

    Directory of Open Access Journals (Sweden)

    Xuyan Xiang

    2015-01-01

    Full Text Available A framework of moment neuronal networks with intra- and inter-interactions is presented. It is to show how the spontaneous activity is propagated across the homogeneous and heterogeneous network. The input-output firing relationship and the stability are first explored for a homogeneous network. For heterogeneous network without the constraint of the correlation coefficients between neurons, a more sophisticated dynamics is then explored. With random interactions, the network gets easily synchronized. However, desynchronization is produced by a lateral interaction such as Mexico hat function. It is the external intralayer input unit that offers a more sophisticated and unexpected dynamics over the predecessors. Hence, the work further opens up the possibility of carrying out a stochastic computation in neuronal networks.

  17. What do interaction network metrics tell us about specialization and biological traits?

    Science.gov (United States)

    Blüthgen, Nico; Fründ, Jochen; Vázquez, Diego P; Menzel, Florian

    2008-12-01

    The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted network metrics (connectance, nestedness, and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as "specialists," irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the lognormal), combined with a relatively low sampling density typical for ecological data, already generates a "nested" and poorly "connected" network with "asymmetric interaction strengths" when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter coevolution and suggests a lower ecological redundancy than implied by nested networks.

  18. Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

    Science.gov (United States)

    Berry, David; Widder, Stefanie

    2014-01-01

    Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  19. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks...

  20. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

    Directory of Open Access Journals (Sweden)

    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  1. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

  2. Limitation of degree information for analyzing the interaction evolution in online social networks

    Science.gov (United States)

    Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

    2014-04-01

    Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

  3. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Botstein David

    2006-06-01

    Full Text Available Abstract Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org and SGD (http://www.yeastgenome.org/ databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

  4. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  5. Modularity, pollination systems, and interaction turnover in plant-pollinator networks across space.

    Science.gov (United States)

    Carstensen, Daniel W; Sabatino, Malena; Morellato, Leonor Patricia C

    2016-05-01

    Mutualistic interaction networks have been shown to be structurally conserved over space and time while pairwise interactions show high variability. In such networks, modularity is the division of species into compartments, or modules, where species within modules share more interactions with each other than they do with species from other modules. Such a modular structure is common in mutualistic networks and several evolutionary and ecological mechanisms have been proposed as underlying drivers. One prominent explanation is the existence of pollination syndromes where flowers tend to attract certain pollinators as determined by a set of traits. We investigate the modularity of seven community level plant-pollinator networks sampled in rupestrian grasslands, or campos rupestres, in SE Brazil. Defining pollination systems as corresponding groups of flower syndromes and pollinator functional groups, we test the two hypotheses that (1) interacting species from the same pollination system are more often assigned to the same module than interacting species from different pollination systems and; that (2) interactions between species from the same pollination system are more consistent across space than interactions between species from different pollination systems. Specifically we ask (1) whether networks are consistently modular across space; (2) whether interactions among species of the same pollination system occur more often inside modules, compared to interactions among species of different pollination systems, and finally; (3) whether the spatial variation in interaction identity, i.e., spatial interaction rewiring, is affected by trait complementarity among species as indicated by pollination systems. We confirm that networks are consistently modular across space and that interactions within pollination systems principally occur inside modules. Despite a strong tendency, we did not find a significant effect of pollination systems on the spatial consistency of

  6. Constructing ecological interaction networks by correlation analysis: hints from community sampling

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2011-09-01

    Full Text Available A set of methodology for constructing ecological interaction networks by correlation analysis of community sampling data was presented in this study. Nearly 30 data sets at different levels of taxa for different sampling seasons and locations were used to construct networks and find network properties. I defined the network constructed by Pearson linear correlation is the linear network, and the network constructed by quasi-linear correlation measure (e.g., Spearman correlation is the quasi-linear network. Two taxa with statistically significant linear or quasi-linear correlation are determined to interact. The quasi-linear network is more general than linear network.The results reveled that correlation distributions of Pearson linear correlation and partial linear correlation constructed networks are unimodal functions and most of them are short-head (mostly negative correlations and long-tailed (mostly positive correlations. Spearman correlation distributions are either long-head and short-tailed unimodal functions or monotonically increasing functions. It was found that both mean partial linear correlation and mean Pearson linear correlation were approximately 0. The proportion of positive (partial linear correlations declined significantly with the increase in taxa. The mean (partial linear correlation declined significantly with the increase of taxa. More than 90% of network interactions are positive interactions. The average connectance was 9.8% (9.3% for (partial linear correlation constructed network. The parameter λ in power low distribution (L(x=x-λ increased as the decline of taxon level (from functional group to species for the partial linear correlation constructed network. λ is in average 0.8 to 0.9. The number of (positive interactions increased with the number of taxa for both linear and partial linear correlations constructed networks. The addition of a taxon would result in an increase of 0.4 (0.3 interactions (positive

  7. Residential Energy Efficiency Potential: Georgia

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, Eric J [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-11-17

    Energy used by Georgia single-family homes that can be saved through cost-effective improvements. Prepared by Eric Wilson and Noel Merket, NREL, and Erin Boyd, U.S. Department of Energy Office of Energy Policy and Systems Analysis.

  8. Fighting Corruption in Georgia's Universities

    Science.gov (United States)

    Janashia, Natia

    2004-01-01

    In the Republic of Georgia, about 240 institutions of higher education serve a population of 5 million. On the surface, these numbers suggest a prosperous, highly educated society. Behind this facade, however, lies a reality of degraded standards, crumbling infrastructure, rampant academic fraud, and deteriorating educational quality. At the…

  9. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.

    Science.gov (United States)

    Yamanishi, Yoshihiro; Araki, Michihiro; Gutteridge, Alex; Honda, Wataru; Kanehisa, Minoru

    2008-07-01

    The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently. In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery. Softwares are available upon request. Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

  10. NatalieQ: a web server for protein-protein interaction network querying.

    Science.gov (United States)

    El-Kebir, Mohammed; Brandt, Bernd W; Heringa, Jaap; Klau, Gunnar W

    2014-04-01

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

  11. Method for designing networking adaptive interactive hybrid systems

    NARCIS (Netherlands)

    Kester, L. J.H.M.

    2010-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public

  12. KNOWNET: Exploring Interactive Knowledge Networking across Insurance Supply Chains

    OpenAIRE

    Grant, Susan

    2014-01-01

    [EN] Social media has become an extremely powerful phenomenon with millions of users who post status updates, blog, links and pictures on social networking sites such as Facebook, LinkedIn, and Twitter. However, social networking has so far spread mainly among consumers. Businesses are only now beginning to acknowledge the benefits of using social media to enhance employee and supplier collaboration to support new ideas and innovation through knowledge sharing across functions and organizatio...

  13. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

    Directory of Open Access Journals (Sweden)

    Boucher Charles AB

    2010-07-01

    Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

  14. Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

    Directory of Open Access Journals (Sweden)

    Tommi Aho

    2010-05-01

    Full Text Available Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our

  15. Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; He, Yongqun

    2017-03-14

    Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

  16. Structural Modeling and Characteristics Analysis of Flow Interaction Networks in the Internet

    International Nuclear Information System (INIS)

    Wu Xiao-Yu; Gu Ren-Tao; Pan Zhuo-Ya; Jin Wei-Qi; Ji Yue-Feng

    2015-01-01

    Applying network duality and elastic mechanics, we investigate the interactions among Internet flows by constructing a weighted undirected network, where the vertices and the edges represent the flows and the mutual dependence between flows, respectively. Based on the obtained flow interaction network, we find the existence of ‘super flow’ in the Internet, indicating that some flows have a great impact on a huge number of other flows; moreover, one flow can spread its influence to another through a limited quantity of flows (less than 5 in the experimental simulations), which shows strong small-world characteristics like the social network. To reflect the flow interactions in the physical network congestion evaluation, the ‘congestion coefficient’ is proposed as a new metric which shows a finer observation on congestion than the conventional one. (paper)

  17. ANSIBLE: A Network of Social Interactions for Bilateral Life Enhancement Project

    Data.gov (United States)

    National Aeronautics and Space Administration — ANSIBLE (A Network of Social Interactions for Bilateral Life Enhancement) can be used pre, during, and post flight to connect the flight crew with their family,...

  18. ANSIBLE: A Network of Social Interactions for Bilateral Life Enhancement, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — ANSIBLE (A Network of Social Interactions for Bilateral Life Enhancement) can be used pre, during, and post flight to connect the flight crew with their family,...

  19. Flexible foraging shapes the topology of plant-pollinator interaction networks.

    Science.gov (United States)

    Spiesman, Brian J; Gratton, Claudio

    2016-06-01

    In plant-pollinator networks, foraging choices by pollinators help form the connecting links between species. Flexible foraging should therefore play an important role in defining network topology. Factors such as morphological trait complementarity limit a pollinator's pool of potential floral resources, but which potential resource species are actually utilized at a location depends on local environmental and ecological context. Pollinators can be highly flexible foragers, but the effect of this flexibility on network topology remains unclear. To understand how flexible foraging affects network topology, we examined differences between sets of locally realized interactions and corresponding sets of potential interactions within 25 weighted plant-pollinator networks in two different regions of the United States. We examined two possible mechanisms for flexible foraging effects on realized networks: (1) preferential targeting of higher-density plant resources, which should increase network nestedness, and (2) context-dependent resource partitioning driven by interspecific competition, which should increase modularity and complementary specialization. We found that flexible foraging has strong effects on realized network topology. Realized connectance was much lower than connectance based on potential interactions, indicating a local narrowing of diet breadth. Moreover, the foraging choices pollinators made, which particular plant species to visit and at what rates, resulted in networks that were significantly less nested and significantly more modular and specialized than their corresponding networks of potential interactions. Preferentially foraging on locally abundant resources was not a strong driver of the realization of potential interactions. However, the degree of modularity and complementary specialization both increased with the number of competing pollinator species and with niche availability. We therefore conclude that flexible foraging affects realized

  20. The management of interaction network. The “In-between” concept within Social Work and Counseling

    Directory of Open Access Journals (Sweden)

    Jesús Hernández-Aristu

    2015-06-01

    Full Text Available We are familiar with the field of group interaction through the traditional work of Kurt Lewin and also systemic thinking talks about network interaction that builds up the system. Martin Buber also discusses the “in-between” concept as the third element.The therapist or counselor, social worker and clients are part of an interaction network, representing therapeutic and social working situations. Success in treatment and reflective processes, depends on the perception and management of the situation by  experts.  Painting the moment of interaction offers the possibility to know what happens between professional and client. Afterwards it is possible to reflect on this experience.

  1. Network graph analysis of gene-gene interactions in genome-wide association study data.

    Science.gov (United States)

    Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

    2012-12-01

    Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  2. The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

    Science.gov (United States)

    Özgür, Arzucan; Hur, Junguk; He, Yongqun

    2016-01-01

    The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A

  3. Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2015-01-01

    Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

  4. Flower-Visiting Social Wasps and Plants Interaction: Network Pattern and Environmental Complexity

    Directory of Open Access Journals (Sweden)

    Mateus Aparecido Clemente

    2012-01-01

    Full Text Available Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland. Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants.

  5. The Social Fabric of Elementary Schools: A Network Typology of Social Interaction among Teachers

    Science.gov (United States)

    Moolenaar, Nienke M.; Sleegers, Peter J. C.; Karsten, Sjoerd; Daly, Alan J.

    2012-01-01

    While researchers are currently studying various forms of social network interaction among teachers for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. The goal of this study is to understand the dimensionality that may underlie various types of social networks…

  6. The Effect of Social Interaction on Learning Engagement in a Social Networking Environment

    Science.gov (United States)

    Lu, Jie; Churchill, Daniel

    2014-01-01

    This study investigated the impact of social interactions among a class of undergraduate students on their learning engagement in a social networking environment. Thirteen undergraduate students enrolled in a course in a university in Hong Kong used an Elgg-based social networking platform throughout a semester to develop their digital portfolios…

  7. Financial Stability and Interacting Networks of Financial Institutions and Market Infrastructures

    NARCIS (Netherlands)

    Léon, C.; Berndsen, R.J.; Renneboog, L.D.R.

    2014-01-01

    An interacting network coupling financial institutions’ multiplex (i.e. multi-layer) and financial market infrastructures’ single-layer networks gives an accurate picture of a financial system’s true connective architecture. We examine and compare the main properties of Colombian multiplex and

  8. Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks.

    Science.gov (United States)

    Gernat, Tim; Rao, Vikyath D; Middendorf, Martin; Dankowicz, Harry; Goldenfeld, Nigel; Robinson, Gene E

    2018-02-13

    Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations. Copyright © 2018 the Author(s). Published by PNAS.

  9. NETAL: a new graph-based method for global alignment of protein-protein interaction networks.

    Science.gov (United States)

    Neyshabur, Behnam; Khadem, Ahmadreza; Hashemifar, Somaye; Arab, Seyed Shahriar

    2013-07-01

    The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together. We present a novel algorithm for the global alignment of protein-protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein-protein interaction networks. Binaries supported on linux are freely available for download at http://www.bioinf.cs.ipm.ir/software/netal. Supplementary data are available at Bioinformatics online.

  10. Cooperation in networks where the learning environment differs from the interaction environment

    NARCIS (Netherlands)

    Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Weissing, Franz J.

    2014-01-01

    We study the evolution of cooperation in a structured population, combining insights from evolutionary game theory and the study of interaction networks. In earlier studies it has been shown that cooperation is difficult to achieve in homogeneous networks, but that cooperation can get established

  11. Differential C3NET reveals disease networks of direct physical interactions

    Directory of Open Access Journals (Sweden)

    Markowetz Florian

    2011-07-01

    Full Text Available Abstract Background Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. Results C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network

  12. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Science.gov (United States)

    Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  13. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Directory of Open Access Journals (Sweden)

    Cristina Tur

    Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  14. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  15. Interactive network visualization in Jupyter notebooks: visJS2jupyter.

    Science.gov (United States)

    Rosenthal, Sara Brin; Len, Julia; Webster, Mikayla; Gary, Aaron; Birmingham, Amanda; Fisch, Kathleen M

    2018-01-01

    Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. sbrosenthal@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  16. Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment.

    Science.gov (United States)

    Chand, Ganesh B; Wu, Junjie; Hajjar, Ihab; Qiu, Deqiang

    2017-09-01

    Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals. However, the patterns of interactions have remained largely unknown in healthy aging or those with cognitive decline. In this study, we assess the patterns of interactions between the three networks using dynamical causal modeling in resting state fMRI data and compare them between subjects with normal cognition and mild cognitive impairment (MCI). In healthy elderly subjects, our analysis showed that the salience network, especially its dorsal subnetwork, modulates the interaction between the default-mode network and the central-executive network (Mann-Whitney U test; p network was disrupted in MCI (p network control correlated significantly with lower overall cognitive performance measured by Montreal Cognitive Assessment (r = 0.295; p network control, especially the dorsal salience network, over other networks provides a neuronal basis for cognitive decline and may be a candidate neuroimaging biomarker of cognitive impairment.

  17. Exploration of the pathways and interaction network involved in bladder cancer cell line with knockdown of Opa interacting protein 5.

    Science.gov (United States)

    He, Xuefeng; Ding, Xiang; Wen, Duangai; Hou, Jianquan; Ping, Jigen; He, Jun

    2017-09-01

    In our previous study, we displayed that knockdown of Opa interacting protein 5 (OIP5) inhibited cell growth, disturbed cell cycle and increased cell apoptosis in bladder cancer (BC) cell line. Our present study aimed to explore the underlying pathways and interaction network involved in the roles of OIP5 in BC. Microarray analysis was conducted to obtain mRNA expression profiling of OIP5 knockdown (shOIP5) and control (shCtrl) BC cell lines. Bioinformatics analyses were performed including differentially expressed mRNAs (DEGs) identification, protein-protein interaction network construction, biological functions of prediction and ingenuity pathways analysis (IPA). Western Blotting (WB) was subjected to validate the protein expression levels of candidate DEGs in shOIP5 BC cell line. Respective 255 up- and 184 down-regulated DEGs were identified in shOIP5 group compared with shCtrl group. In the PPI network, CAND1 and MYC had the highest connectivity with DEGs. 439 DEGs were significantly enriched in inflammatory response, regulation of cell proliferation, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction and bladder cancer. In the disease and function enrichment, DEGs were obviously involved in cellular movement, cellular growth and proliferation, cancer, inflammatory response, cell death and survival. In the OIP5 regulatory network, CDH2, IRS1, IRAK3, ID1, TNF, IL6, ITGA6, MYC and SOD2 interacted with OIP5. The WB validation results were compatible with our bioinformatics analyses. OIP5 interaction network might function as an oncogene in BC progression based on aberrant inflammatory responses. Our study might provide valuable information for investigation of tumorigenesis mechanism in BC. Copyright © 2017 Elsevier GmbH. All rights reserved.

  18. Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

    Science.gov (United States)

    Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

    2017-01-01

    The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

  19. The IRIS network of excellence : Integrating research in interactive storytelling

    NARCIS (Netherlands)

    Cavazza, Marc; Donikian, Stéphane; Christie, Marc; Spierling, Ulrike; Szilas, Nicolas; Vorderer, Peter; Hartmann, Tilo; Klimmt, Christoph; André, Elisabeth; Champagnat, Ronan; Petta, Paolo; Olivier, Patrick

    2008-01-01

    Interactive Storytelling is a major endeavour to develop new media which could offer a radically new user experience, with a potential to revolutionise digital entertainment. European research in Interactive Storytelling has played a leading role in the development of the field, and this creates a

  20. Estimating the Local Size and Coverage of Interaction Network Regions

    Science.gov (United States)

    Eagle, Michael; Barnes, Tiffany

    2015-01-01

    Interactive problem solving environments, such as intelligent tutoring systems and educational video games, 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 the student-tutor interactions using complex network…

  1. CollaborationViz: interactive visual exploration of biomedical research collaboration networks.

    Directory of Open Access Journals (Sweden)

    Jiang Bian

    Full Text Available Social network analysis (SNA helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs. Especially, in the Clinical Translational Science Award (CTSA community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences--a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization.

  2. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  3. Networked Mobilities and new sites of mediated interaction

    DEFF Research Database (Denmark)

    Jensen, Ole B.

    2008-01-01

    This paper takes point of departure in an understanding of mobility as an important cultural dimension to contemporary life. The movement of objects, signs, and people constitutes material sites of networked relationships. However, as an increasing number of mobility practices are making up our...

  4. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge.

    Science.gov (United States)

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H; Wang, Yanli

    2017-01-01

    Drug-drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.Graphical abstract.

  5. Impedance-Based Harmonic Instability Assessment in Multiple Electric Trains and Traction Network Interaction System

    DEFF Research Database (Denmark)

    Tao, Haidong; Hu, Haitao; Wang, Xiongfei

    2018-01-01

    interconnection of the two subsystems, i.e., an equivalent output impedance of the traction network and an equivalent input admittance of the multi-train. Then the harmonic instability is evaluated through a series of pole-zero diagrams drawn from the closed loop transfer matrix of the multi-train and traction......This paper presents an impedance-based method to systematically investigate the interaction between multi-train and traction networks, focusing on evaluating the harmonic instability problems. Firstly, the interaction mechanism of multi-train and the traction network is represented as a feedback...

  6. The Perception of National Security in Georgia

    Directory of Open Access Journals (Sweden)

    Merabishvili Gela

    2016-12-01

    Full Text Available This article examines how the on-going confrontation between Russia and the West affects perceptions of security in Georgia. Our angle is twofold: in addition to comparing previous National Security Concepts of Georgia we examine both governmental and public perceptions of security in the light of Georgia’s foreign policy priorities, its relationship with neighbouring countries and conflict resolution policy. Since Georgia declares 20 per cent of its territory to be occupied, the article focuses particularly on the crisis in Ukraine and its effect on security debates in Georgia. As the upcoming parliamentary elections in Georgia in autumn 2016 are highly important to maintain the current foreign policy course and secure achievements, the paper also tries to answer how these global and regional developments may be interpreted and reflected in the next National Security Concept of Georgia (whenever it might be published.

  7. Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks

    Directory of Open Access Journals (Sweden)

    Arzucan Özgür

    2010-01-01

    Full Text Available Interferon-gamma (IFN-γ regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γ research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP and network centrality analysis. The interaction network of human IFN-γ (Gene symbol: IFNG and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γ relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

  8. Defining the protein interaction network of human malaria parasite Plasmodium falciparum

    KAUST Repository

    Ramaprasad, Abhinay

    2012-02-01

    Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.

  9. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

    Science.gov (United States)

    de Vos, Marjon G. J.; Bollenbach, Tobias

    2017-01-01

    Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. PMID:28923953

  10. Crucial role of strategy updating for coexistence of strategies in interaction networks

    NARCIS (Netherlands)

    Zhang, Jianlei; Zhang, Chunyan; Cao, Ming; Weissing, Franz

    2015-01-01

    Network models are useful tools for studying the dynamics of social interactions in a structured population. After a round of interactions with the players in their local neighborhood, players update their strategy based on the comparison of their own payoff with the payoff of one of their

  11. QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Asa Thibodeau

    2016-06-01

    Full Text Available Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1 building and visualizing chromatin interaction networks, 2 annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3 querying network components based on gene name or chromosome location, and 4 utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

  12. Conspicuous political brand interactions on Social Network Sites

    OpenAIRE

    Marder, Ben; Marchant, Caroline; Archer-Brown, Chris; Yau, Amy; Colliander, Jonas

    2018-01-01

    Purpose Acquiring ‘Likes’ for a political party or candidate’s Facebook pages is important for political marketers. For consumers these ‘Likes’ are conspicuous, making their political affiliation visible to their network. Our study examines the roles of the undesired social-self and visibility (conspicuous vs. inconspicuous) in predicting consumers’ intention to ‘Like’ political brands. We extend knowledge on the undesired social-self, transference of theory from general marketing to a politi...

  13. Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly

    Directory of Open Access Journals (Sweden)

    Jialiang Kuang

    2017-08-01

    Full Text Available Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits are more conserved than those of taxonomic measures, with significantly lower variation of network characteristics along the environmental gradient in acid mine drainage. The results showed that although the overall network characteristics remained similar, the structural variation was significantly lower at trait levels. The higher conserved individual node topological properties at trait level rather than at species level indicated that the responses of diverse traits remained relatively consistent even though different species played key roles under different environmental conditions. Additionally, the randomization tests revealed that it could not reject the null hypothesis that species-based correlations were random, while the tests suggested that correlation patterns of traits were non-random. Furthermore, relationships between trait-based network characteristics and environmental properties implied that trait-based networks might be more useful in reflecting the variation of ecosystem function. Taken together, our results suggest that deterministic trait-based community assembly results in greater conservation of network interaction, which may ensure ecosystem function across environmental regimes, emphasizing the potential importance of measuring the complexity and conservation of network interaction in evaluating the ecosystem stability and functioning.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

    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.

  15. NEURAL NETWORK INTERACTIONS AND INGESTIVE BEHAVIOR CONTROL DURING ANOREXIA

    Science.gov (United States)

    Watts, Alan G.; Salter, Dawna S.; Neuner, Christina M.

    2007-01-01

    Many models have been proposed over the years to explain how motivated feeding behavior is controlled. One of the most compelling is based on the original concepts of Eliot Stellar whereby sets of interosensory and exterosensory inputs converge on a hypothalamic control network that can either stimulate or inhibit feeding. These inputs arise from information originating in the blood, the viscera, and the telencephalon. In this manner the relative strengths of the hypothalamic stimulatory and inhibitory networks at a particular time dictates how an animal feeds. Anorexia occurs when the balance within the networks consistently favors the restraint of feeding. This article discusses experimental evidence supporting a model whereby the increases in plasma osmolality that result from drinking hypertonic saline activate pathways projecting to neurons in the paraventricular nucleus of the hypothalamus (PVH) and lateral hypothalamic area (LHA). These neurons constitute the hypothalamic controller for ingestive behavior, and receive a set of afferent inputs from regions of the brain that process sensory information that is critical for different aspects of feeding. Important sets of inputs arise in the arcuate nucleus, the hindbrain, and in the telencephalon. Anorexia is generated in dehydrated animals by way of osmosensitive projections to the behavior control neurons in the PVH and LHA, rather than by actions on their afferent inputs. PMID:17531275

  16. Evaluating the Network: A Workflow for Tracking Twitter Interactions Using Social Networking Analysis

    Science.gov (United States)

    Goodier, Sarah

    2018-01-01

    Networking plays an important role in research projects to build a community and audience around a research area. Using social media is popular in project communication as it provides the ability to engage with a group of followers daily. Such online networking tools provide the advantage of providing nearrealtime data, which can be used to…

  17. Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications

    Directory of Open Access Journals (Sweden)

    Wagner Andreas

    2004-11-01

    Full Text Available Abstract Background The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection. Results We present a detailed statistical analysis of the protein interactions in Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i gene duplications and (ii gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks. Conclusions According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i a broad distribution of the connectivities (i.e., the number of binding partners of a protein and (ii correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of S. cerevisiae.

  18. Identification of putative drug targets for human sperm-egg interaction defect using protein network approach.

    Science.gov (United States)

    Sabetian, Soudabeh; Shamsir, Mohd Shahir

    2015-07-18

    Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis. We have created human sperm-egg protein interaction networks with high confidence, including 106 nodes and 415 interactions. Through topological analysis of the network with calculation of some metrics, such as connectivity and betweenness centrality, we have identified 13 essential proteins as putative drug targets. The potential drug targets are from integrins, fibronectins, epidermal growth factor receptors, collagens and tetraspanins protein families. We evaluated these targets by ingenuity pathway analysis, and the known drugs for the targets have been detected, and the possible effective role of the drugs on sperm-egg interaction defect has been considered. These results showed that the drugs ocriplasmin (Jetrea©), gefitinib (Iressa©), erlotinib hydrochloride (Tarceva©), clingitide, cetuximab (Erbitux©) and panitumumab (Vectibix©) are possible candidates for efficacy testing for the treatment of sperm-egg interaction deficiency. Further experimental validation can be carried out to confirm these results. We have identified the first potential list of

  19. Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks.

    Science.gov (United States)

    Xu, Bin; Guan, Jihong; Wang, Yang; Wang, Zewei

    2017-05-12

    Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by highthroughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection. Moreover, most existing studies focused on the local information of proteins in PPI networks, while ignoring the influence of indirect protein interactions on essentiality. In this paper, we propose a novel method, called Essentiality Ranking (EssRank in short), to boost the accuracy of essential protein detection. To deal with the inaccuracy of PPI data, confidence scores of interactions are evaluated by integrating various biological information. Weighted edge clustering coefficient (WECC), considering both interaction confidence scores and network topology, is proposed to calculate edge weights in PPI networks. The weight of each node is evaluated by the sum of WECC values of its linking edges. A random walk method, making use of both direct and indirect protein interactions, is then employed to calculate protein essentiality iteratively. Experimental results on the yeast PPI network show that EssRank outperforms most existing methods, including the most commonly-used centrality measures (SC, DC, BC, CC, IC, EC), topology based methods (DMNC and NC) and the data integrating method IEW.

  20. Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks.

    Science.gov (United States)

    Rouault, Hervé; Hakim, Vincent

    2012-02-08

    The acquisition of different fates by cells that are initially in the same state is central to development. Here, we investigate the possible structures of bistable genetic networks that can allow two identical cells to acquire different fates through cell-cell interactions. Cell-autonomous bistable networks have been previously sampled using an evolutionary algorithm. We extend this evolutionary procedure to take into account interactions between cells. We obtain a variety of simple bistable networks that we classify into major subtypes. Some have long been proposed in the context of lateral inhibition through the Notch-Delta pathway, some have been more recently considered and others appear to be new and based on mechanisms not previously considered. The results highlight the role of posttranscriptional interactions and particularly of protein complexation and sequestration, which can replace cooperativity in transcriptional interactions. Some bistable networks are entirely based on posttranscriptional interactions and the simplest of these is found to lead, upon a single parameter change, to oscillations in the two cells with opposite phases. We provide qualitative explanations as well as mathematical analyses of the dynamical behaviors of various created networks. The results should help to identify and understand genetic structures implicated in cell-cell interactions and differentiation. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  1. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Web_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism....

  2. 76 FR 25330 - Georgia Power Company; Project No. 485-063-Georgia and Alabama, Bartletts Ferry Hydroelectric...

    Science.gov (United States)

    2011-05-04

    ... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission Georgia Power Company; Project No. 485-063--Georgia and Alabama, Bartletts Ferry Hydroelectric Project; Notice of Proposed Restricted Service... Ferry Hydroelectric Project. The Programmatic Agreement, when executed by the Commission, the Georgia...

  3. A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

    Directory of Open Access Journals (Sweden)

    Roger Guimerà

    Full Text Available Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.

  4. Invasive Carassius Carp in Georgia: Current state of knowledge and future perspectives

    Directory of Open Access Journals (Sweden)

    Bella JAPOSHVILI, Levan MUMLADZE, Fahrettin KÜÇÜK

    2013-12-01

    Full Text Available In Georgia, crucian carp Carassius carassius (Linnaeus, 1758 was known from only one locality after Kesslers record (1877–1878 with no new findings until 1985. Since then C. carassius rapidly and simultaneously invaded almost all water bodies of Georgia. In 2004, it was for the first time noted that this invasive Carassius sp. could not be a C. Carassius, but was a form of Carassius gibelio (Bloch, 1792. However no further data is available about this invasive species in Georgia. The aim of the present study was to investigate taxonomic status of Carassius sp. in Georgia using mtDNA phylogenetic analyses and morphometric study of truss network system. Genetic analysis revealed that invasive Carassius sp. is closely related to the C. gibelio from Turkey and other countries. In contrast, morphometrically Carassius sp. from Georgia can be easily differentiated from those of Turkey indicating high intraspecific variability. This is the first time discussion on the current knowledge of the present distribution of invasive carp in Georgia with identifying current problems and future research directions needed [Current Zoology 59 (6: 732–739, 2013].

  5. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  6. Identification of human disease genes from interactome network using graphlet interaction.

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

    Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

  7. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed

    2013-05-01

    Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

  8. Fluctuating interaction network and time-varying stability of a natural fish community

    Science.gov (United States)

    Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

    2018-02-01

    Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

  9. A graph modification approach for finding core-periphery structures in protein interaction networks.

    Science.gov (United States)

    Bruckner, Sharon; Hüffner, Falk; Komusiewicz, Christian

    2015-01-01

    The core-periphery model for protein interaction (PPI) networks assumes that protein complexes in these networks consist of a dense core and a possibly sparse periphery that is adjacent to vertices in the core of the complex. In this work, we aim at uncovering a global core-periphery structure for a given PPI network. We propose two exact graph-theoretic formulations for this task, which aim to fit the input network to a hypothetical ground truth network by a minimum number of edge modifications. In one model each cluster has its own periphery, and in the other the periphery is shared. We first analyze both models from a theoretical point of view, showing their NP-hardness. Then, we devise efficient exact and heuristic algorithms for both models and finally perform an evaluation on subnetworks of the S. cerevisiae PPI network.

  10. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  11. Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks

    Science.gov (United States)

    Liu, Quan-Hui; Wang, Wei; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

    2018-02-01

    Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

  12. Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

    Science.gov (United States)

    Semino, Laura N; Marksteiner, Josef; Brauchle, Gernot; Danay, Erik

    2017-04-13

    Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these interactions in dynamical networks and in a linear way over time depending on the severity of depression. Participants included 110 patients with depressive symptoms (DSM-5 criteria) who were recruited between October 2015 and February 2016 during their inpatient stay in a general psychiatric hospital in Hall in Tyrol, Austria. The patients filled out the Beck Depression Inventory-II, a German emotional competence questionnaire (Emotionale Kompetenz Fragebogen), Positive and Negative Affect Schedule, and the German versions of the Big Five Inventory-short form and State-Trait-Anxiety-Depression Inventory regarding symptoms, emotions, and personality during their inpatient stay and at a 3-month follow-up by mail. Network and regression analyses were performed to explore interactions both in a linear and a dynamical way at baseline and 3 months later. Regression analyses showed that emotions and personality traits gain importance for the prediction of depressive symptoms with decreasing symptomatology at follow-up (personality: baseline, adjusted R2 = 0.24, P emotions, and personality traits is significantly denser and more interconnected (network comparison test: P = .03) at follow-up than at baseline, meaning that with decreased symptoms interconnections get stronger. During depression, personality traits and emotions are walled off and not strongly interconnected with depressive symptoms in networks. With decreasing depressive symptomatology, interfusing of these areas begins and interconnections become stronger. This finding has practical implications for interventions in an acute depressive state and with decreased symptoms. The network approach offers a new perspective on interactions and is a way to make the complexity of these interactions more tangible. © Copyright 2017 Physicians Postgraduate Press, Inc.

  13. Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

    Science.gov (United States)

    Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

    2015-01-16

    Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

  14. Building dynamic capabilities in large global advertising agency networks: managing the shift from mass communication to digital interactivity

    DEFF Research Database (Denmark)

    Suheimat, Wisam; Prætorius, Thim; Brambini-Pedersen, Jan Vang

    2018-01-01

    Interactive digital technologies result in significant managerial challenges for the largest global advertising agency networks. This paper, based on original data from in-depth case research in three of the largest global advertising networks, investigates how advertising agency networks manage ...... the cognitive, structural, operational and process changes needed to develop digital interactivity, thereby highlighting important managerial implications....

  15. Diversity in a complex ecological network with two interaction types

    Czech Academy of Sciences Publication Activity Database

    Melián, C. J.; Bascompte, J.; Jordano, P.; Křivan, Vlastimil

    2009-01-01

    Roč. 118, č. 1 (2009), s. 122-130 ISSN 0030-1299 R&D Projects: GA AV ČR IAA100070601 Grant - others:University of California(US) DEB-0553768; The Spanish Ministry of Science and Technology(ES) REN2003-04774; The Spanish Ministry of Science and Technology(ES) REN2003-00273 Institutional research plan: CEZ:AV0Z50070508 Keywords : complex ecological network Subject RIV: EH - Ecology, Behaviour Impact factor: 3.147, year: 2009

  16. Experimental FSO network availability estimation using interactive fog condition monitoring

    Science.gov (United States)

    Turán, Ján.; Ovseník, Łuboš

    2016-12-01

    Free Space Optics (FSO) is a license free Line of Sight (LOS) telecommunication technology which offers full duplex connectivity. FSO uses infrared beams of light to provide optical broadband connection and it can be installed literally in a few hours. Data rates go through from several hundreds of Mb/s to several Gb/s and range is from several 100 m up to several km. FSO link advantages: Easy connection establishment, License free communication, No excavation are needed, Highly secure and safe, Allows through window connectivity and single customer service and Compliments fiber by accelerating the first and last mile. FSO link disadvantages: Transmission media is air, Weather and climate dependence, Attenuation due to rain, snow and fog, Scattering of laser beam, Absorption of laser beam, Building motion and Air pollution. In this paper FSO availability evaluation is based on long term measured data from Fog sensor developed and installed at TUKE experimental FSO network in TUKE campus, Košice, Slovakia. Our FSO experimental network has three links with different physical distances between each FSO heads. Weather conditions have a tremendous impact on FSO operation in terms of FSO availability. FSO link availability is the percentage of time over a year that the FSO link will be operational. It is necessary to evaluate the climate and weather at the actual geographical location where FSO link is going to be mounted. It is important to determine the impact of a light scattering, absorption, turbulence and receiving optical power at the particular FSO link. Visibility has one of the most critical influences on the quality of an FSO optical transmission channel. FSO link availability is usually estimated using visibility information collected from nearby airport weather stations. Raw data from fog sensor (Fog Density, Relative Humidity, Temperature measured at each ms) are collected and processed by FSO Simulator software package developed at our Department. Based

  17. Systematic discovery of new recognition peptides mediating protein interaction networks

    DEFF Research Database (Denmark)

    Neduva, Victor; Linding, Rune; Su-Angrand, Isabelle

    2005-01-01

    Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains are kn...

  18. Interaction between fatty acid and the elastin network

    NARCIS (Netherlands)

    Vreeswijk, van J.

    1995-01-01

    The aim of the present study was to investigate the interaction between salts of fatty acids (FAS) and elastin. Absorption of fatty acids in elastin may affect the elasticity of elastin-containing tissue. Such phenomena could, for instance, be of relevance for the understanding of the

  19. Analysing Students' Interactions through Social Presence and Social Network Metrics

    Science.gov (United States)

    Martins da Silva, Vanessa Cristina; Siqueira, Sean Wolfgand Matsui

    2016-01-01

    In online learning environments, tutors have several problems to carry out their activities, such as evaluating the student, knowing the right way to guide each student, promoting discussions, and knowing the right time to interact or let students build knowledge alone. We consider scenarios in which teaching and learning occurs in online social…

  20. Ariadne's Thread - Interactive Navigation in a World of Networked Information

    NARCIS (Netherlands)

    Koopman, Rob; Wang, Shenghui; Scharnhorst, Andrea; Englebienne, Gwenn

    2015-01-01

    This work-in-progress paper introduces an interface for the interactive visual exploration of the context of queries using the ArticleFirst database, a product of OCLC. We describe a workflow which allows the user to browse live entities associated with 65 million articles. In the on-line interface,

  1. Protein-lipid interactions: from membrane domains to cellular networks

    National Research Council Canada - National Science Library

    Tamm, Lukas K

    2005-01-01

    ... membranes is the lipid bilayer. Embedded in the fluid lipid bilayer are proteins of various shapes and traits. This volume illuminates from physical, chemical and biological angles the numerous - mostly quite weak - interactions between lipids, proteins, and proteins and lipids that define the delicate, highly dynamic and yet so stable fabri...

  2. Bird-plant interaction networks: a study on frugivory in Brazilian urban areas

    Directory of Open Access Journals (Sweden)

    Diego Silva Freitas Oliveira

    2015-12-01

    Full Text Available In Brazil, few studies compare the consumption of native and exotic fruits, especially in an urban environment. The Network Theory may be useful in such studies, because it allows evaluating many bird and plant species involved in interactions. The goals of this study were: evaluate a bird frugivory interaction network in an urban environment; checking the role played by native and exotic plants in the network and comparing the consumer assemblies of these two plant groups. A literature review on bird frugivory in Brazilian urban areas was conducted, as well as an analysis to create an interaction network on a regional scale. The analysis included 15 papers with 70 bird species eating fruits from 15 plant species (6 exotic and 9 native. The exotic and native fruit consumers did not form different groups and the interaction network was significantly nested (NODF = 0.30; p < 0.01 and not modular (M = 0.36; p = 0.16. Two exotic plant species are in the generalist core of the frugivory network (Ficus microcarpa and Michelia champaca. The results point out that a relatively diversified bird group eats fruits in Brazilian urban areas in an opportunistic way, with no preference for native or exotic plants.

  3. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  4. Relevance of different prior knowledge sources for inferring gene interaction networks.

    Science.gov (United States)

    Olsen, Catharina; Bontempi, Gianluca; Emmert-Streib, Frank; Quackenbush, John; Haibe-Kains, Benjamin

    2014-01-01

    When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two

  5. Functional Ecological Gene Networks to Reveal the Changes Among Microbial Interactions Under Elevated Carbon Dioxide Conditions

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-05-17

    Biodiversity and its responses to environmental changes is a central issue in ecology, and for society. Almost all microbial biodiversity researches focus on species richness and abundance but ignore the interactions among different microbial species/populations. However, determining the interactions and their relationships to environmental changes in microbial communities is a grand challenge, primarily due to the lack of information on the network structure among different microbial species/populations. Here, a novel random matrix theory (RMT)-based conceptual framework for identifying functional ecological gene networks (fEGNs) is developed with the high throughput functional gene array hybridization data from the grassland microbial communities in a long-term FACE (Free Air CO2 Enrichment) experiment. Both fEGNs under elevated CO2 (eCO2) and ambient CO2 (aCO2) possessed general characteristics of many complex systems such as scale-free, small-world, modular and hierarchical. However, the topological structure of the fEGNs is distinctly different between eCO2 and aCO2, suggesting that eCO2 dramatically altered the interactions among different microbial functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen dynamics, and plant productivity, indicating the potential importance of network interactions in ecosystem functioning. Elucidating network interactions in microbial communities and their responses to environmental changes are fundamentally important for research in microbial ecology, systems microbiology, and global change.

  6. Reconstructing past ecological networks: the reconfiguration of seed-dispersal interactions after megafaunal extinction.

    Science.gov (United States)

    Pires, Mathias M; Galetti, Mauro; Donatti, Camila I; Pizo, Marco A; Dirzo, Rodolfo; Guimarães, Paulo R

    2014-08-01

    The late Quaternary megafaunal extinction impacted ecological communities worldwide, and affected key ecological processes such as seed dispersal. The traits of several species of large-seeded plants are thought to have evolved in response to interactions with extinct megafauna, but how these extinctions affected the organization of interactions in seed-dispersal systems is poorly understood. Here, we combined ecological and paleontological data and network analyses to investigate how the structure of a species-rich seed-dispersal network could have changed from the Pleistocene to the present and examine the possible consequences of such changes. Our results indicate that the seed-dispersal network was organized into modules across the different time periods but has been reconfigured in different ways over time. The episode of megafaunal extinction and the arrival of humans changed how seed dispersers were distributed among network modules. However, the recent introduction of livestock into the seed-dispersal system partially restored the original network organization by strengthening the modular configuration. Moreover, after megafaunal extinctions, introduced species and some smaller native mammals became key components for the structure of the seed-dispersal network. We hypothesize that such changes in network structure affected both animal and plant assemblages, potentially contributing to the shaping of modern ecological communities. The ongoing extinction of key large vertebrates will lead to a variety of context-dependent rearranged ecological networks, most certainly affecting ecological and evolutionary processes.

  7. Temporal variation in bat-fruit interactions: Foraging strategies influence network structure over time

    Science.gov (United States)

    Zapata-Mesa, Natalya; Montoya-Bustamante, Sebastián; Murillo-García, Oscar E.

    2017-11-01

    Mutualistic interactions, such as seed dispersal, are important for the maintenance of structure and stability of tropical communities. However, there is a lack of information about spatial and temporal variation in plant-animal interaction networks. Thus, our goal was to assess the effect of bat's foraging strategies on temporal variation in the structure and robustness of bat-fruit networks in both a dry and a rain tropical forest. We evaluated monthly variation in bat-fruit networks by using seven structure metrics: network size, average path length, nestedness, modularity, complementary specialization, normalized degree and betweenness centrality. Seed dispersal networks showed variations in size, species composition and modularity; did not present nested structures and their complementary specialization was high compared to other studies. Both networks presented short path lengths, and a constantly high robustness, despite their monthly variations. Sedentary bat species were recorded during all the study periods and occupied more central positions than nomadic species. We conclude that foraging strategies are important structuring factors that affect the dynamic of networks by determining the functional roles of frugivorous bats over time; thus sedentary bats are more important than nomadic species for the maintenance of the network structure, and their conservation is a must.

  8. Generalist Bee Species on Brazilian Bee-Plant Interaction Networks

    Directory of Open Access Journals (Sweden)

    Astrid de Matos Peixoto Kleinert

    2012-01-01

    Full Text Available Determining bee and plant interactions has an important role on understanding general biology of bee species as well as the potential pollinating relationship between them. Bee surveys have been conducted in Brazil since the end of the 1960s. Most of them applied standardized methods and had identified the plant species where the bees were collected. To analyze the most generalist bees on Brazilian surveys, we built a matrix of bee-plant interactions. We estimated the most generalist bees determining the three bee species of each surveyed locality that presented the highest number of interactions. We found 47 localities and 39 species of bees. Most of them belong to Apidae (31 species and Halictidae (6 families and to Meliponini (14 and Xylocopini (6 tribes. However, most of the surveys presented Apis mellifera and/or Trigona spinipes as the most generalist species. Apis mellifera is an exotic bee species and Trigona spinipes, a native species, is also widespread and presents broad diet breath and high number of individuals per colony.

  9. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis.

    Directory of Open Access Journals (Sweden)

    Matthew D Dyer

    2010-08-01

    Full Text Available Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.

  10. Specialization of mutualistic interaction networks decreases toward tropical latitudes

    DEFF Research Database (Denmark)

    Schleuning, Matthias; Fründ, Jochen; Klein, Alexandra-Maria

    2012-01-01

    to low plant diversity. This could explain why the latitudinal specialization gradient is reversed relative to the latitudinal diversity gradient. Low mutualistic network specialization in the tropics suggests higher tolerance against extinctions in tropical than in temperate communities.......Species-rich tropical communities are expected to be more specialized than their temperate counterparts [1-3]. Several studies have reported increasing biotic specialization toward the tropics [4-7], whereas others have not found latitudinal trends once accounting for sampling bias [8, 9......] or differences in plant diversity [10, 11]. Thus, the direction of the latitudinal specialization gradient remains contentious. With an unprecedented global data set, we investigated how biotic specialization between plants and animal pollinators or seed dispersers is associated with latitude, past...

  11. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

  12. A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

    Science.gov (United States)

    Moberg, Fallon B; Anestis, Michael D

    2015-01-01

    Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

  13. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

    Science.gov (United States)

    Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

    2015-04-01

    The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. The Missing Part of Seed Dispersal Networks: Structure and Robustness of Bat-Fruit Interactions

    Science.gov (United States)

    Mello, Marco Aurelio Ribeiro; Marquitti, Flávia Maria Darcie; Guimarães, Paulo Roberto; Kalko, Elisabeth Klara Viktoria; Jordano, Pedro; de Aguiar, Marcus Aloizio Martinez

    2011-01-01

    Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i) some bat species depend more on fruits than others, and (ii) that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H2' = 0.37±0.10, mean ± SD) and similar nestedness (NODF = 0.56±0.12) than pollination networks. All networks were modular (M = 0.32±0.07), and had on average four cohesive subgroups (modules) of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum), although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55±0.10) and plants (R = 0.68±0.09). Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks. PMID:21386981

  15. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    DEFF Research Database (Denmark)

    Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

    2008-01-01

    is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...... Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks...

  16. Evolution of quantum and classical strategies on networks by group interactions

    International Nuclear Information System (INIS)

    Li Qiang; Chen Minyou; Iqbal, Azhar; Abbott, Derek

    2012-01-01

    In this paper, quantum strategies are introduced within evolutionary games in order to investigate the evolution of quantum and classical strategies on networks in the public goods game. Comparing the results of evolution on a scale-free network and a square lattice, we find that a quantum strategy outperforms the classical strategies, regardless of the network. Moreover, a quantum strategy dominates the population earlier in group interactions than it does in pairwise interactions. In particular, if the hub node in a scale-free network is occupied by a cooperator initially, the strategy of cooperation will prevail in the population. However, in other situations, a quantum strategy can defeat the classical ones and finally becomes the dominant strategy in the population. (paper)

  17. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  18. When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks.

    Science.gov (United States)

    Iván, Gábor; Grolmusz, Vince

    2011-02-01

    Enormous and constantly increasing quantity of biological information is represented in metabolic and in protein interaction network databases. Most of these data are freely accessible through large public depositories. The robust analysis of these resources needs novel technologies, being developed today. Here we demonstrate a technique, originating from the PageRank computation for the World Wide Web, for analyzing large interaction networks. The method is fast, scalable and robust, and its capabilities are demonstrated on metabolic network data of the tuberculosis bacterium and the proteomics analysis of the blood of melanoma patients. The Perl script for computing the personalized PageRank in protein networks is available for non-profit research applications (together with sample input files) at the address: http://uratim.com/pp.zip.

  19. Social network analysis as a method for analyzing interaction in collaborative online learning environments

    Directory of Open Access Journals (Sweden)

    Patricia Rice Doran

    2011-12-01

    Full Text Available Social network analysis software such as NodeXL has been used to describe participation and interaction in numerous social networks, but it has not yet been widely used to examine dynamics in online classes, where participation is frequently required rather than optional and participation patterns may be impacted by the requirements of the class, the instructor’s activities, or participants’ intrinsic engagement with the subject matter. Such social network analysis, which examines the dynamics and interactions among groups of participants in a social network or learning group, can be valuable in programs focused on teaching collaborative and communicative skills, including teacher preparation programs. Applied to these programs, social network analysis can provide information about instructional practices likely to facilitate student interaction and collaboration across diverse student populations. This exploratory study used NodeXL to visualize students’ participation in an online course, with the goal of identifying (1 ways in which NodeXL could be used to describe patterns in participant interaction within an instructional setting and (2 identifying specific patterns in participant interaction among students in this particular course. In this sample, general education teachers demonstrated higher measures of connection and interaction with other participants than did those from specialist (ESOL or special education backgrounds, and tended to interact more frequently with all participants than the majority of participants from specialist backgrounds. We recommend further research to delineate specific applications of NodeXL within an instructional context, particularly to identify potential patterns in student participation based on variables such as gender, background, cultural and linguistic heritage, prior training and education, and prior experience so that instructors can ensure their practice helps to facilitate student interaction

  20. Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2012-12-01

    Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  1. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo

    2013-06-21

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

  2. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  3. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  4. Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity.

    Science.gov (United States)

    Pardikes, Nicholas A; Lumpkin, Will; Hurtado, Paul J; Dyer, Lee A

    2018-01-01

    Most of earth's biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships

  5. The Application of Information Network Computing Model in the Study of Molecular Interaction

    Directory of Open Access Journals (Sweden)

    Yongchao Li

    2014-01-01

    Full Text Available As the network information technology was proceeding to the field of biotechnology, the successful application of network method in the field of molecular biology expanded human’s understanding of complex system, and achieved many important results. This article selected exogenous substances and target molecules as the research object, and through the method of informational network, studied the research results and problems of biological information under the interaction of exogenous substances and target molecules from the following aspects: the network related to target molecules of the organism, the related database of the interaction of exogenous substances and target molecules, and the method of biological information analysis, etc. The study showed that we could predict the interaction of unknown target molecules and exogenous molecules through the network diagram; the use of informational network analysis methods not only greatly reduced the research cost and time, but provided s novel way of thinking and the new approach to elucidate the pathogenesis and diagnosis and treatment of diseases.

  6. Topological and functional properties of the small GTPases protein interaction network.

    Directory of Open Access Journals (Sweden)

    Anna Delprato

    Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

  7. Reorganization of interaction networks modulates the persistence of species in late successional stages.

    Science.gov (United States)

    Saavedra, Serguei; Cenci, Simone; Del-Val, Ek; Boege, Karina; Rohr, Rudolf P

    2017-09-01

    Ecological interaction networks constantly reorganize as interspecific interactions change across successional stages and environmental gradients. This reorganization can also be associated with the extent to which species change their preference for types of niches available in their local sites. Despite the pervasiveness of these interaction changes, previous studies have revealed that network reorganizations have a minimal or insignificant effect on global descriptors of network architecture, such as connectance, modularity and nestedness. However, little is known about whether these reorganizations may have an effect on community dynamics and composition. To answer the question above, we study the multi-year dynamics and reorganization of plant-herbivore interaction networks across secondary successional stages of a tropical dry forest. We develop new quantitative tools based on a structural stability approach to estimate the potential impact of network reorganization on species persistence. Then, we investigate whether this impact can explain the likelihood of persistence of herbivore species in the observed communities. We find that resident (early-arriving) herbivore species increase their likelihood of persistence across time and successional stages. Importantly, we demonstrate that, in late successional stages, the reorganization of interactions among resident species has a strong inhibitory effect on the likelihood of persistence of colonizing (late-arriving) herbivores. These findings support earlier predictions suggesting that, in mature communities, changes of species interactions can act as community-control mechanisms (also known as priority effects). Furthermore, our results illustrate that the dynamics and composition of ecological communities cannot be fully understood without attention to their reorganization processes, despite the invariability of global network properties. © 2017 The Authors. Journal of Animal Ecology © 2017 British

  8. Statistics of interacting networks with extreme preferred degrees: Simulation results and theoretical approaches

    Science.gov (United States)

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

    2012-02-01

    Network studies have played a central role for understanding many systems in nature - e.g., physical, biological, and social. So far, much of the focus has been the statistics of networks in isolation. Yet, many networks in the world are coupled to each other. Recently, we considered this issue, in the context of two interacting social networks. In particular, We studied networks with two different preferred degrees, modeling, say, introverts vs. extroverts, with a variety of ``rules for engagement.'' As a first step towards an analytically accessible theory, we restrict our attention to an ``extreme scenario'': The introverts prefer zero contacts while the extroverts like to befriend everyone in the society. In this ``maximally frustrated'' system, the degree distributions, as well as the statistics of cross-links (between the two groups), can depend sensitively on how a node (individual) creates/breaks its connections. The simulation results can be reasonably well understood in terms of an approximate theory.

  9. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Science.gov (United States)

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  10. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  11. Control of Grid Interactive PV Inverters for High Penetration in Low Voltage Distribution Networks

    DEFF Research Database (Denmark)

    Demirok, Erhan

    network management scheme or weakly-prepared grid connection requirements without paying attention to PV integration problems can bring potential risk of unintentional disconnections of these generating plants that likely increase payback time and extra energy losses of these renewable energy sources...... which do not consider interaction among multiple PV inverters with the grid may allow unlimited number of PV plant connections and lead to unstable network operation. Harmonic emissions from multiple inverters connected to the same feeder and resulting in a network resonance can be a good example...

  12. Interactions between Financial and Environmental Networks in OECD Countries.

    Directory of Open Access Journals (Sweden)

    Franco Ruzzenenti

    Full Text Available We analysed a multiplex of financial and environmental networks between OECD countries from 2002 to 2010. Foreign direct investments and portfolio investment showing the flows in equity securities, short-term, long-term and total debt, these securities represent the financial layers; emissions of NOx, PM10, SO2, CO2 equivalent and the water footprint associated with international trade represent the environmental layers. We present a new measure of cross-layer correlations between flows in different layers based on reciprocity. For the assessment of results, we implement a null model for this measure based on the exponential random graph theory. We find that short-term financial flows are more correlated with environmental flows than long-term investments. Moreover, the correlations between reverse financial and environmental flows (i.e. the flows of different layers going in opposite directions are generally stronger than correlations between synergic flows (flows going in the same direction. This suggests a trade-off between financial and environmental layers, where, more financialised countries display higher correlations between outgoing financial flows and incoming environmental flows than from lower financialised countries. Five countries are identified as hubs in this finance-environment multiplex: The United States, France, Germany, Belgium-Luxembourg and United Kingdom.

  13. Network structure beyond food webs: mapping non-trophic and trophic interactions on Chilean rocky shores.

    Science.gov (United States)

    Sonia Kéfi; Berlow, Eric L; Wieters, Evie A; Joppa, Lucas N; Wood, Spencer A; Brose, Ulrich; Navarrete, Sergio A

    2015-01-01

    How multiple types of non-trophic interactions map onto trophic networks in real communities remains largely unknown. We present the first effort, to our knowledge, describing a comprehensive ecological network that includes all known trophic and diverse non-trophic links among >100 coexisting species for the marine rocky intertidal community of the central Chilean coast. Our results suggest that non-trophic interactions exhibit highly nonrandom structures both alone and with respect to food web structure. The occurrence of different types of interactions, relative to all possible links, was well predicted by trophic structure and simple traits of the source and target species. In this community, competition for space and positive interactions related to habitat/refuge provisioning by sessile and/or basal species were by far the most abundant non-trophic interactions. If these patterns are orroborated in other ecosystems, they may suggest potentially important dynamic constraints on the combined architecture of trophic and non-trophic interactions. The nonrandom patterning of non-trophic interactions suggests a path forward for developing a more comprehensive ecological network theory to predict the functioning and resilience of ecological communities.

  14. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Synchronization unveils the organization of ecological networks with positive and negative interactions.

    Science.gov (United States)

    Girón, Andrea; Saiz, Hugo; Bacelar, Flora S; Andrade, Roberto F S; Gómez-Gardeñes, Jesús

    2016-06-01

    Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

  16. Rainwater Harvesting and Social Networks: Visualising Interactions for Niche Governance, Resilience and Sustainability

    Directory of Open Access Journals (Sweden)

    Sarah Ward

    2016-11-01

    Full Text Available Visualising interactions across urban water systems to explore transition and change processes requires the development of methods and models at different scales. This paper contributes a model representing the network interactions of rainwater harvesting (RWH infrastructure innovators and other organisations in the UK RWH niche to identify how resilience and sustainability feature within niche governance in practice. The RWH network interaction model was constructed using a modified participatory social network analysis (SNA. The SNA was further analysed through the application of a two-part analytical framework based on niche management and the safe, resilient and sustainable (‘Safe and SuRe’ framework. Weak interactions between some RWH infrastructure innovators and other organisations highlighted reliance on a limited number of persuaders to influence the regime and landscape, which were underrepresented. Features from niche creation and management were exhibited by the RWH network interaction model, though some observed characteristics were not represented. Additional Safe and SuRe features were identified covering diverse innovation, responsivity, no protection, unconverged expectations, primary influencers, polycentric or adaptive governance and multiple learning-types. These features enable RWH infrastructure innovators and other organisations to reflect on improving resilience and sustainability, though further research in other sectors would be useful to verify and validate observation of the seven features.

  17. Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

    Science.gov (United States)

    Naderi, Elnaz; Mostafaei, Mehdi; Pourshams, Akram

    2014-01-01

    Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches. PMID:24895587

  18. Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

    Directory of Open Access Journals (Sweden)

    Elnaz Naderi

    2014-01-01

    Full Text Available Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.

  19. AtPID: the overall hierarchical functional protein interaction network interface and analytic platform for Arabidopsis.

    Science.gov (United States)

    Li, Peng; Zang, Weidong; Li, Yuhua; Xu, Feng; Wang, Jigang; Shi, Tieliu

    2011-01-01

    Protein interactions are involved in important cellular functions and biological processes that are the fundamentals of all life activities. With improvements in experimental techniques and progress in research, the overall protein interaction network frameworks of several model organisms have been created through data collection and integration. However, most of the networks processed only show simple relationships without boundary, weight or direction, which do not truly reflect the biological reality. In vivo, different types of protein interactions, such as the assembly of protein complexes or phosphorylation, often have their specific functions and qualifications. Ignorance of these features will bring much bias to the network analysis and application. Therefore, we annotate the Arabidopsis proteins in the AtPID database with further information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways. The latest updated AtPID database is available at http://www.megabionet.org/atpid/.

  20. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    Science.gov (United States)

    Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

  1. STITCH 2: an interaction network database for small molecules and proteins

    DEFF Research Database (Denmark)

    Kuhn, Michael; Szklarczyk, Damian; Franceschini, Andrea

    2010-01-01

    Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug......-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other...... chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/....

  2. 77 FR 67639 - Liberty Energy (Georgia) Corp.; Notice of Application

    Science.gov (United States)

    2012-11-13

    ... Energy Regulatory Commission Liberty Energy (Georgia) Corp.; Notice of Application Take notice that on October 25, Liberty Energy (Georgia) Corp. (Liberty Georgia), 2845 Bristol Circle, Oakville, Ontario... Gas Act (NGA) requesting the determination of a service area within which Liberty Georgia may, without...

  3. A Network-Centric Formalism for Disturbance Rejection Design and Human Swarm Interaction

    Science.gov (United States)

    2015-07-06

    the context of swarms of aerial and ground robotic systems have also been examined and tested in the laboratory. The PI has pursued establishing a...AFRL-AFOSR-VA-TR-2016-0007 Formalism for Disturbance Rejection Design and Human- swarm Interaction Mehran Mesbahi UNIVERSITY OF WASHINGTON Final...SUBTITLE A Network-centric Formalism for Disturbance Rejection Design and Human Swarm Interaction 5a. CONTRACT NUMBER FA9550-12-1-0203 5b. GRANT NUMBER

  4. The structure and dynamics of complex microbe-host interaction networks

    OpenAIRE

    Björk, Johannes

    2016-01-01

    Microbes form intricate and intimate relationships with most animals and plants, many of which are crucial for host development, health and functioning. Microbe--host symbiotic associations are poorly explored in comparison with other species interaction networks. The current paradigm on symbiosis research stems from species-poor systems where pairwise and reciprocally specialised interactions between a single microbe and host that coevolve are the norm. These symbioses involving just a few s...

  5. Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network

    OpenAIRE

    Patil, Ashwini; Kinoshita, Kengo; Nakamura, Haruki

    2010-01-01

    Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non-hubs in protein–protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non-hubs. We found that proteins with a more diverse domain composition were over-re...

  6. Building protein-protein interaction networks for Leishmania species through protein structural information.

    Science.gov (United States)

    Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

    2018-03-06

    Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

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

    Science.gov (United States)

    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. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

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    Neszt Michael

    2008-07-01

    Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

  9. Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network

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    Avijit Podder

    2017-06-01

    Full Text Available Intercommunication of Dopamine Receptors (DRs with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled “Human Dopamine Receptors Interaction Network (DRIN: a systems biology perspective on topology, stability and functionality of the network” (Podder et al., 2014 [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

  10. The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks.

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    Guillermo Abramson

    Full Text Available Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad range involving specialists and generalists. It has been suggested that this asymmetric--or disassortative--assemblage could play an important role in determining the observed equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the analysis of the phenomenon lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with a few generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.

  11. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

    Directory of Open Access Journals (Sweden)

    Kirouac Daniel C

    2012-05-01

    Full Text Available Abstract Background Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID, PANTHER, Reactome, I2D, and STRING. We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. Results We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless, we find a multiplicity of network topologies in which receptors couple to downstream

  12. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

    Science.gov (United States)

    LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

    2015-01-01

    High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

  13. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

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    Jan eHahne

    2015-09-01

    Full Text Available Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy...

  14. Networks of Neuroscientists: Professional Interactions within an Interdisciplinary Brain Research Institute

    Science.gov (United States)

    Godley, Jenny; Sharkey, Keith A.; Weiss, Samuel

    2013-01-01

    This paper uses social network analysis to evaluate how the formation of an interdisciplinary brain research institute affected interaction and collaboration among neuroscientists at one Canadian university. The research institute, formed in 2004, has about 100 members representing ten different departments across the university campus. We…

  15. Differential variation patterns between hubs and bottlenecks in human protein-protein interaction networks.

    Science.gov (United States)

    Pang, Erli; Hao, Yu; Sun, Ying; Lin, Kui

    2016-12-01

    The identification, description and understanding of protein-protein networks are important in cell biology and medicine, especially for the study of system biology where the focus concerns the interaction of biomolecules. Hubs and bottlenecks refer to the important proteins of a protein interaction network. Until now, very little attention has been paid to differentiate these two protein groups. By integrating human protein-protein interaction networks and human genome-wide variations across populations, we described the differences between hubs and bottlenecks in this study. Our findings showed that similar to interspecies, hubs and bottlenecks changed significantly more slowly than non-hubs and non-bottlenecks. To distinguish hubs from bottlenecks, we extracted their special members: hub-non-bottlenecks and non-hub-bottlenecks. The differences between these two groups represent what is between hubs and bottlenecks. We found that the variation rate of hubs was significantly lower than that of bottlenecks. In addition, we verified that stronger constraint is exerted on hubs than on bottlenecks. We further observed fewer non-synonymous sites on the domains of hubs than on those of bottlenecks and different molecular functions between them. Based on these results, we conclude that in recent human history, different variation patterns exist in hubs and bottlenecks in protein interaction networks. By revealing the difference between hubs and bottlenecks, our results might provide further insights in the relationship between evolution and biological structure.

  16. Increasing Learner Retention in a Simulated Learning Network using Indirect Social Interaction

    NARCIS (Netherlands)

    Koper, Rob

    2004-01-01

    Please refer to original publication: Koper, E.J.R. (2005). Increasing Learner Retention in a Simulated Learning Network Using Indirect Social Interaction. Journal of Artificial Societies and Social Simulation vol. 8, no. 2. http://jasss.soc.surrey.ac.uk/8/2/5.html Software is only stored to ensure

  17. Elements of Engagement: A Model of Teacher Interactions via Professional Learning Networks

    Science.gov (United States)

    Krutka, Daniel G.; Carpenter, Jeffrey P.; Trust, Torrey

    2016-01-01

    In recent years, many educators have turned to participatory online affinity spaces for professional growth with peers who are more accessible because of reduced temporal and spatial constraints. Specifically, professional learning networks (PLNs) are "uniquely personalized, complex systems of interactions consisting of people, resources, and…

  18. Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

    Directory of Open Access Journals (Sweden)

    Alexandru Topirceanu

    2016-01-01

    Full Text Available One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual’s inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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

    DEFF Research Database (Denmark)

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

    2016-01-01

    , 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...

  20. An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

    NARCIS (Netherlands)

    Commu, Charlotte; Theelen, Mathilde; Treur, J.

    2017-01-01

    In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The

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

    Science.gov (United States)

    Darabos, Christian; Qiu, Jingya; Moore, Jason H

    2016-01-01

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

  2. Functional network interactions during sensorimotor synchronization in musicians and non-musicians.

    Science.gov (United States)

    Krause, Vanessa; Schnitzler, Alfons; Pollok, Bettina

    2010-08-01

    Precise timing as determined by sensorimotor synchronization is crucial for a wide variety of activities. Although it is well-established that musicians show superior timing as compared to non-musicians, the neurophysiological foundations - in particular the underlying functional brain network - remain to be characterized. To this end, drummers, professional pianists and non-musicians performed an auditory synchronization task while neuromagnetic activity was measured using a 122-channel whole-head magnetoencephalography (MEG) system. The underlying functional brain network was determined using the beamformer approach Dynamic Imaging of Coherent Sources (DICS). Behaviorally, drummers performed less variably than non-musicians. Neuromagnetic analysis revealed a cerebello-thalamo-cortical network in all subjects comprising bilateral primary sensorimotor cortices (S1/M1), contralateral supplementary motor and premotor regions (SMA and PMC), thalamus, posterior parietal cortex (PPC), ipsilateral cerebellum and bilateral auditory cortices. Stronger PMC-thalamus and PPC-thalamus interactions at alpha and beta frequencies were evident in drummers as compared to non-musicians. In professional pianists stronger PMC-thalamus interaction as compared to non-musicians at beta frequency occurred. The present data suggest that precise timing is associated with increased functional interaction within a PMC-thalamus-PPC network. The PMC-thalamus connectivity at beta frequency might be related to musical expertise, whereas the PPC-thalamus interaction might have specific relevance for precise timing. Copyright 2010 Elsevier Inc. All rights reserved.

  3. The reconstruction of a social network abroad. An analysis of the interaction patterns of Erasmus students

    NARCIS (Netherlands)

    Van Mol, C.; Michielsen, J.

    2015-01-01

    Most studies of interaction patterns of international students focus on ‘degree mobility’ and flows from ‘non-Western’ towards ‘Western’ countries. Nevertheless, in Europe, the shorter alternative of ‘credit mobility’ is more prevalent. However, empirical evidence on social network formation within

  4. On the importance of personal profiles to enhance social interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns F., & Sloep, P.B. (2008). On the importance of personal profiles to enhance social interaction in Learning Networks. In P. Kommers (Ed.), Proceedings of Web Based Communities Conference (WEBC 2008) (pp. 55-62). July, 24-26, 2008, Amsterdam, The

  5. On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M. E., Brouns F., & Sloep, P. B. (2008). On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks. Presented at the IADIS International Conference on Web Based Communities 2008. July, 24-26, 2008, Amsterdam, The Netherlands.

  6. Inferring protein function by domain context similarities in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Sun Zhirong

    2009-12-01

    Full Text Available Abstract Background Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions. Results The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves. Conclusion This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.

  7. Processes entangling interactions in communities: forbidden links are more important than abundance in a hummingbird-plant network.

    Science.gov (United States)

    Vizentin-Bugoni, Jeferson; Maruyama, Pietro Kiyoshi; Sazima, Marlies

    2014-04-07

    Understanding the relative importance of multiple processes on structuring species interactions within communities is one of the major challenges in ecology. Here, we evaluated the relative importance of species abundance and forbidden links in structuring a hummingbird-plant interaction network from the Atlantic rainforest in Brazil. Our results show that models incorporating phenological overlapping and morphological matches were more accurate in predicting the observed interactions than models considering species abundance. This means that forbidden links, by imposing constraints on species interactions, play a greater role than species abundance in structuring the ecological network. We also show that using the frequency of interaction as a proxy for species abundance and network metrics to describe the detailed network structure might lead to biased conclusions regarding mechanisms generating network structure. Together, our findings suggest that species abundance can be a less important driver of species interactions in communities than previously thought.

  8. Network theory-based analysis of risk interactions in large engineering projects

    International Nuclear Information System (INIS)

    Fang, Chao; Marle, Franck; Zio, Enrico; Bocquet, Jean-Claude

    2012-01-01

    This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project. - Highlights: ► The method addresses the modeling of complexity in project risk analysis. ► Network theory indicators enable other risks than classical criticality analysis to be highlighted. ► This topological analysis improves project manager's understanding of risks and risk interactions. ► This helps project manager to make decisions considering the position in the risk network. ► An application to a real tramway implementation project in a city is provided.

  9. Structural breakdown of specialized plant-herbivore interaction networks in tropical forest edges

    Directory of Open Access Journals (Sweden)

    Bruno Ximenes Pinho

    2017-10-01

    Full Text Available Plant-herbivore relationships are essential for ecosystem functioning, typically forming an ecological network with a compartmentalized (i.e. modular structure characterized by highly specialized interactions. Human disturbances can favor habitat generalist species and thus cause the collapse of this modular structure, but its effects are rarely assessed using a network-based approach. We investigate how edge proximity alters plant-insect herbivore networks by comparing forest edge and interior in a large remnant (3.500 ha of the Brazilian Atlantic forest. Given the typical dominance of pioneer plants and generalist herbivores in edge-affected habitats, we test the hypothesis that the specialized structure of plant-herbivore networks collapse in forest edges, resulting in lower modularity and herbivore specialization. Despite no differences in the number of species and interactions, the network structure presented marked differences between forest edges and interior. Herbivore specialization, modularity and number of modules were significantly higher in forest interior than edge-affected habitats. When compared to a random null model, two (22.2% and eight (88.8% networks were significantly modular in forest edge and interior, respectively. The loss of specificity and modularity in plant-herbivore networks in forest edges may be related to the loss of important functions, such as density-dependent control of superior plant competitors, which is ultimately responsible for the maintenance of biodiversity and ecosystem functions. Our results support previous warnings that focusing on traditional community measures only (e.g. species diversity may overlook important modifications in species interactions and ecosystem functioning.

  10. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  11. On the Connectivity of Wireless Network Systems and an Application in Teacher-Student Interactive Platforms

    Directory of Open Access Journals (Sweden)

    Xun Ge

    2014-01-01

    Full Text Available A wireless network system is a pair (U;B, where B is a family of some base stations and U is a set of their users. To investigate the connectivity of wireless network systems, this paper takes covering approximation spaces as mathematical models of wireless network systems. With the help of covering approximation operators, this paper characterizes the connectivity of covering approximation spaces by their definable subsets. Furthermore, it is obtained that a wireless network system is connected if and only if the relevant covering approximation space has no nonempty definable proper subset. As an application of this result, the connectivity of a teacher-student interactive platform is discussed, which is established in the School of Mathematical Sciences of Soochow University. This application further demonstrates the usefulness of rough set theory in pedagogy and makes it possible to research education by logical methods and mathematical methods.

  12. Patterns of interactions of a large fish-parasite network in a tropical floodplain.

    Science.gov (United States)

    Lima, Dilermando P; Giacomini, Henrique C; Takemoto, Ricardo M; Agostinho, Angelo A; Bini, Luis M

    2012-07-01

    1. Describing and explaining the structure of species interaction networks is of paramount importance for community ecology. Yet much has to be learned about the mechanisms responsible for major patterns, such as nestedness and modularity in different kinds of systems, of which large and diverse networks are a still underrepresented and scarcely studied fraction. 2. We assembled information on fishes and their parasites living in a large floodplain of key ecological importance for freshwater ecosystems in the Paraná River basin in South America. The resulting fish-parasite network containing 72 and 324 species of fishes and parasites, respectively, was analysed to investigate the patterns of nestedness and modularity as related to fish and parasite features. 3. Nestedness was found in the entire network and among endoparasites, multiple-host life cycle parasites and native hosts, but not in networks of ectoparasites, single-host life cycle parasites and non-native fishes. All networks were significantly modular. Taxonomy was the major host's attribute influencing both nestedness and modularity: more closely related host species tended to be associated with more nested parasite compositions and had greater chance of belonging to the same network module. Nevertheless, host abundance had a positive relationship with nestedness when only native host species pairs of the same network module were considered for analysis. 4. These results highlight the importance of evolutionary history of hosts in linking patterns of nestedness and formation of modules in the network. They also show that functional attributes of parasites (i.e. parasitism mode and life cycle) and origin of host populations (i.e. natives versus non-natives) are crucial to define the relative contribution of these two network properties and their dependence on other ecological factors (e.g. host abundance), with potential implications for community dynamics and stability. © 2012 The Authors

  13. Network Interaction of Universities in Higher Education System of Ural Macro-Region

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    Garold Efimovich Zborovsky

    2017-06-01

    Full Text Available The subject-matter of the analysis are the characteristics and forms of cooperation between universities of Ural Federal District on the basis of their typology. The purpose of the article is to substantiate the necessity and possibility of network interaction between universities of the macro-region. We prove the importance and potential effectiveness of universities network interaction in the terms of socio-economic uncertainty of the development of Ural Federal District and its higher education. Networking interaction and multilateral cooperation are considered as a new type of inter-universities relations, which can be activated and intensified by strengthening the relations of universities with stakeholders. The authors examine certain concrete forms and formats of network interaction and cooperation between universities and discuss selected cases of new type of relations. In it, they see the real and potential innovation of higher school nonlinear development processes. The statements of the article allow to confirm the hypothesis about the reality of strengthening the network interaction in macro-region. It can transform higher education in the driver of socio-economic development of Ural Federal District; ensure the competitiveness of higher education of the macro-region in the Russian and global educational space; enhance its role in the society; become one of the most significant elements of nonlinear models of higher education development in the country. The authors’ research is based on the interdisciplinary methodology including the potential of theoretical sociology, sociology of higher education, economic sociology, management theory, regional economics. The results of the study can form the basis for the improvement of the Ural Federal District’s educational policy.

  14. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Directory of Open Access Journals (Sweden)

    Recep Colak

    2010-10-01

    Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

  15. Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.

    Science.gov (United States)

    Agarwal, Sumeet; Deane, Charlotte M; Porter, Mason A; Jones, Nick S

    2010-06-17

    The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

  16. Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Sumeet Agarwal

    2010-06-01

    Full Text Available The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

  17. Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

    LENUS (Irish Health Repository)

    Casey, Fergal

    2011-08-22

    Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

  18. Delays and user performance in human-computer-network interaction tasks.

    Science.gov (United States)

    Caldwell, Barrett S; Wang, Enlie

    2009-12-01

    This article describes a series of studies conducted to examine factors affecting user perceptions, responses, and tolerance for network-based computer delays affecting distributed human-computer-network interaction (HCNI) tasks. HCNI tasks, even with increasing computing and network bandwidth capabilities, are still affected by human perceptions of delay and appropriate waiting times for information flow latencies. Conducted were 6 laboratory studies with university participants in China (Preliminary Experiments 1 through 3) and the United States (Experiments 4 through 6) to examine users' perceptions of elapsed time, effect of perceived network task performance partners on delay tolerance, and expectations of appropriate delays based on task, situation, and network conditions. Results across the six experiments indicate that users' delay tolerance and estimated delay were affected by multiple task and expectation factors, including task complexity and importance, situation urgency and time availability, file size, and network bandwidth capacity. Results also suggest a range of user strategies for incorporating delay tolerance in task planning and performance. HCNI user experience is influenced by combinations of task requirements, constraints, and understandings of system performance; tolerance is a nonlinear function of time constraint ratios or decay. Appropriate user interface tools providing delay feedback information can help modify user expectations and delay tolerance. These tools are especially valuable when delay conditions exceed a few seconds or when task constraints and system demands are high. Interface designs for HCNI tasks should consider assistant-style presentations of delay feedback, information freshness, and network characteristics. Assistants should also gather awareness of user time constraints.

  19. Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet.

    Science.gov (United States)

    Yang, Q; Siganos, G; Faloutsos, M; Lonardi, S

    2006-01-01

    Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against man-made communication networks, and more specifically, the Internet. Our comparative study is based on a comprehensive set of graph metrics. Our results exhibit an interesting dichotomy. On the one hand, both networks share several macroscopic properties such as scale-free and small-world properties. On the other hand, the two networks exhibit significant topological differences, such as the cliqueishness of the highest degree nodes. We attribute these differences to the distinct design principles and constraints that both networks are assumed to satisfy. We speculate that the evolutionary constraints that favor the survivability and diversification are behind the building process of PPI networks, whereas the leading force in shaping the Internet topology is a decentralized optimization process geared towards efficient node communication.

  20. The role of exon shuffling in shaping protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  1. Global innovation networks and university-firm interactions: an exploratory survey analysis

    Directory of Open Access Journals (Sweden)

    Gustavo Britto

    2015-02-01

    Full Text Available The literature on Global Innovation Networks has contributed to identify changes in the innovation activities of multinational corporations. Although university-firm interactions are seen as an important factor for the emergence of GINs, their role has received limited attention. This paper aims to fill this gap in two ways. First, it carries out an exploratory analysis of an original survey dataset, of firms in three industrial sectors from nine developed and developing countries. Second, the paper analyses whether the role of universities in global innovation networks is related to national systems of innovation with varying degrees of maturity. Multiple correspondence analysis and a Probit model are used to establish the relevance of key factors in driving GINs. The results identify distinctive profiles constructed mainly according to firm characteristics, but reflecting country specific patterns of association. The Probit model confirms that internationalization processes and the existence of local interactions substantially increase the probability of interactions with international institutions.

  2. Modelling and Analysis of the Dynamics of Adaptive Temporal-Causal Network Models for Evolving Social Interactions

    NARCIS (Netherlands)

    Treur, J.

    2017-01-01

    Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes. Adaptive temporal-causal network models are based on causal relations by which the

  3. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Directory of Open Access Journals (Sweden)

    Brian R Granger

    2016-04-01

    Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  4. Exploring overlapping functional units with various structure in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Xiao-Fei Zhang

    Full Text Available Revealing functional units in protein-protein interaction (PPI networks are important for understanding cellular functional organization. Current algorithms for identifying functional units mainly focus on cohesive protein complexes which have more internal interactions than external interactions. Most of these approaches do not handle overlaps among complexes since they usually allow a protein to belong to only one complex. Moreover, recent studies have shown that other non-cohesive structural functional units beyond complexes also exist in PPI networks. Thus previous algorithms that just focus on non-overlapping cohesive complexes are not able to present the biological reality fully. Here, we develop a new regularized sparse random graph model (RSRGM to explore overlapping and various structural functional units in PPI networks. RSRGM is principally dominated by two model parameters. One is used to define the functional units as groups of proteins that have similar patterns of connections to others, which allows RSRGM to detect non-cohesive structural functional units. The other one is used to represent the degree of proteins belonging to the units, which supports a protein belonging to more than one revealed unit. We also propose a regularizer to control the smoothness between the estimators of these two parameters. Experimental results on four S. cerevisiae PPI networks show that the performance of RSRGM on detecting cohesive complexes and overlapping complexes is superior to that of previous competing algorithms. Moreover, RSRGM has the ability to discover biological significant functional units besides complexes.

  5. Global Alignment of Pairwise Protein Interaction Networks for Maximal Common Conserved Patterns

    Directory of Open Access Journals (Sweden)

    Wenhong Tian

    2013-01-01

    Full Text Available A number of tools for the alignment of protein-protein interaction (PPI networks have laid the foundation for PPI network analysis. Most of alignment tools focus on finding conserved interaction regions across the PPI networks through either local or global mapping of similar sequences. Researchers are still trying to improve the speed, scalability, and accuracy of network alignment. In view of this, we introduce a connected-components based fast algorithm, HopeMap, for network alignment. Observing that the size of true orthologs across species is small comparing to the total number of proteins in all species, we take a different approach based on a precompiled list of homologs identified by KO terms. Applying this approach to S. cerevisiae (yeast and D. melanogaster (fly, E. coli K12 and S. typhimurium, E. coli K12 and C. crescenttus, we analyze all clusters identified in the alignment. The results are evaluated through up-to-date known gene annotations, gene ontology (GO, and KEGG ortholog groups (KO. Comparing to existing tools, our approach is fast with linear computational cost, highly accurate in terms of KO and GO terms specificity and sensitivity, and can be extended to multiple alignments easily.

  6. Comprehension through explanation as the interaction of the brain’s coherence and cognitive control networks

    Directory of Open Access Journals (Sweden)

    Jarrod eMoss

    2015-10-01

    Full Text Available Discourse comprehension processes attempt to produce an elaborate and well-connected representation in the reader’s mind. A common network of regions including the angular gyrus, posterior cingulate, and dorsal frontal cortex appears to be involved in constructing coherent representations in a variety of tasks including social cognition tasks, narrative comprehension, and expository text comprehension. Reading strategies that require the construction of explicit inferences are used in the present research to examine how this coherence network interacts with other brain regions. A psychophysiological interaction analysis was used to examine regions showing changed functional connectivity with this coherence network when participants were engaged in either a non-inferencing reading strategy, paraphrasing, or a strategy requiring coherence-building inferences, self-explanation. Results of the analysis show that the coherence network increases in functional connectivity with a cognitive control network that may be specialized for the manipulation of semantic representations and the construction of new relations among these representations.

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

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

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

  8. Isolation and evolution of labile sulfur allotropes via kinetic encapsulation in interactive porous networks

    Directory of Open Access Journals (Sweden)

    Hakuba Kitagawa

    2016-07-01

    Full Text Available The isolation and characterization of small sulfur allotropes have long remained unachievable because of their extreme lability. This study reports the first direct observation of disulfur (S2 with X-ray crystallography. Sulfur gas was kinetically trapped and frozen into the pores of two Cu-based porous coordination networks containing interactive iodide sites. Stabilization of S2 was achieved either through physisorption or chemisorption on iodide anions. One of the networks displayed shape selectivity for linear molecules only, therefore S2 was trapped and remained stable within the material at room temperature and higher. In the second network, however, the S2 molecules reacted further to produce bent-S3 species as the temperature was increased. Following the thermal evolution of the S2 species in this network using X-ray diffraction and Raman spectroscopy unveiled the generation of a new reaction intermediate never observed before, the cyclo-trisulfur dication (cyclo-S32+. It is envisaged that kinetic guest trapping in interactive crystalline porous networks will be a promising method to investigate transient chemical species.

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

    Science.gov (United States)

    Chakraborty, Sandip; Alvarez-Ponce, David

    2016-01-01

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

  10. CONCERNING THE NETWORKING INTERACTION EXPERIENCE OF TEACHERS AND STUDENTS OF PEDAGOGICAL UNIVERSITY

    Directory of Open Access Journals (Sweden)

    E. A. Dmitrieva

    2015-01-01

    Full Text Available The purpose of the research is to identify the possibilities for the formation knowledge and practical skills related to the use of the professional activity of software and network resource of teaching communities in the pedagogical sphere.Methods. The methods involve the analysis of the literary sources, regulatory documents, Internet resources within the researched problem; an analysis of the practical experience of teachers of secondary schools, work of high school teachers and establishment of training teachers on the research problem; the experimental work and monitoring the learning process.Results. The process of teachers’ training inYaroslavl, in particular preparation of students-biologists at theYaroslavlStatePedagogicalUniversityis reflected. Activity of network pedagogical community of Yaroslavl is considered as a platform for network interaction; the analysis of such platform, use of its resources, and also conversations with subject teachers and students have shown that the given electronic and communication resources cause a great interest for practicing teachers and future experts, however, they not always possess necessary knowledge and abilities concerning its operation.Scientific novelty. The author describes in detail the process of forming a competence of networking of professional interaction in terms of its methodological support that is relevant to the educational process, both in the high school, and post-graduate education.Practical significance. The research implementations can be useful while developing specific guidelines to explain the content and methodology of the training network of professional interaction with examples of practicing teachers and students ofPedagogicalUniversity– future teachers of biology.The article is addressed to researchers, dealing with networking, specialists of teaching service centers (institutions of educational development, the practicing subject teachers and teachers of high

  11. Mass-action equilibrium and non-specific interactions in protein binding networks

    Science.gov (United States)

    Maslov, Sergei

    2009-03-01

    Large-scale protein binding networks serve as a paradigm of complex properties of living cells. These networks are naturally weighted with edges characterized by binding strength and protein-nodes -- by their concentrations. However, the state-of-the-art high-throughput experimental techniques generate just a binary (yes or no) information about individual interactions. As a result, most of the previous research concentrated just on topology of these networks. In a series of recent publications [1-4] my collaborators and I went beyond purely topological studies and calculated the mass-action equilibrium of a genome-wide binding network using experimentally determined protein concentrations, localizations, and reliable binding interactions in baker's yeast. We then studied how this equilibrium responds to large perturbations [1-2] and noise [3] in concentrations of proteins. We demonstrated that the change in the equilibrium concentration of a protein exponentially decays (and sign-alternates) with its network distance away from the perturbed node. This explains why, despite a globally connected topology, individual functional modules in such networks are able to operate fairly independently. In a separate study [4] we quantified the interplay between specific and non-specific binding interactions under crowded conditions inside living cells. We show how the need to limit the waste of resources constrains the number of types and concentrations of proteins that are present at the same time and at the same place in yeast cells. [1] S Maslov, I. Ispolatov, PNAS 104:13655 (2007). [2] S. Maslov, K. Sneppen, I. Ispolatov, New J. of Phys. 9: 273 (2007). [3] K-K. Yan, D. Walker, S. Maslov, PRL accepted (2008). [4] J. Zhang, S. Maslov, and E. I. Shakhnovich, Mol Syst Biol 4, 210 (2008).

  12. Determination of temperate bird-flower interactions as entangled mutualistic and antagonistic sub-networks: characterization at the network and species levels.

    Science.gov (United States)

    Yoshikawa, Tetsuro; Isagi, Yuji

    2014-05-01

    Most network studies on biological interactions consider only a single interaction type. However, individual species are simultaneously positioned in various types of interactions. The ways in which different network types are merged and entangled, and the variations in network structures between different sympatric networks, require full elucidation. Incorporating interaction types and disentangling complex networks is crucial, because the integration of various network architectures has the potential to alter the stability and co-evolutionary dynamics of the whole network. To reveal how different types of interaction networks are entangled, we focused on the interaction between birds and flowers of temperate plants in Japan, where flower-feeding birds are mainly generalist passerines, acting as pollinators and predators of flowers. Using long-term monitoring data, we investigated the flower-feeding episodes of birds. We constructed the whole network (WN) between birds and plants, separating the network into mutualistic and antagonistic sub-networks (MS and AS, respectively). We investigated structural properties of the three quantified networks and species-level characteristics of the main bird species. For bird species, we evaluated dietary similarity, dietary specialization and shifts of feeding behaviour relative to plant traits. Our results indicate that WN comprises entangled MS and AS, sharing considerable proportions of bird and plant assemblages. We observed distinctive differences in the network structural properties between the two sub-networks. In comparison with AS, MS had lower numbers of bird and plant species, showed lower specialization and modularity and exhibited higher nestedness. At the species level, the Japanese white-eye acted as pollinator, while the brown-eared bulbul acted as both pollinator and predator for large numbers of flowers, based on its behavioural plasticity. Overall, the pattern of avian feeding behaviour was influenced by

  13. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

    Science.gov (United States)

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2016-01-01

    To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  14. Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

    Science.gov (United States)

    McClelland, James L

    2013-01-01

    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

  15. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human--Robot Interaction

    Directory of Open Access Journals (Sweden)

    Tatsuro Yamada

    2016-07-01

    Full Text Available To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language--behavior relationships and the temporal patterns of interaction. Here, ``internal dynamics'' refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language--behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language--behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  18. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available 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

  19. Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality.

    Directory of Open Access Journals (Sweden)

    Elena Zotenko

    2008-08-01

    Full Text Available The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein-protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model.

  20. 2013 Athens-Clarke County, Georgia Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of 0.5 foot pixel resolution, four band (RGB, Near IR) orthoimages covering Athens-Clarke Georgia. An orthoimage is remotely sensed image data...

  1. 2012 Oconee County, Georgia ADS80 Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — All imagery was collected during the 2012 Spring flying season during leaf-off conditions for deciduous vegetation in the State of Georgia. The sun angle was at...

  2. The problems of nuclear security in Georgia

    International Nuclear Information System (INIS)

    Nabakhtiani, N.

    2006-01-01

    Georgia is carrying out activities to improve nuclear security according to the demands of international standards and rules. The support of developed countries and international organisations is very important to solve the associated problems. (author)

  3. Georgia Department of Transportation (GDOT) leadership academy.

    Science.gov (United States)

    2014-01-01

    Public agencies like the Georgia Department of Transportation (GDOT) are continually challenged with : reduced staff levels, reduced budgets, increased workloads and expectations for services provided. In : addition, the current economic and politica...

  4. Strait of Georgia chinook and coho fishery

    National Research Council Canada - National Science Library

    Argue, A. W

    1983-01-01

    The chinook (Oncorhynchus tshawytscha) and coho (O. kisutch) salmon fishery in the Strait of Georgia, between Vancouver Island and the mainland of British Columbia, is a valuble sport and commercial resource...

  5. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Directory of Open Access Journals (Sweden)

    Thomas Wallach

    2013-03-01

    Full Text Available Essentially all biological processes depend on protein-protein interactions (PPIs. Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc. contributing to temporal organization of cellular physiology in an unprecedented manner.

  6. A multilevel path analysis of social networks and social interaction in the neighbourhood

    Directory of Open Access Journals (Sweden)

    Pauline van den Berg

    2015-05-01

    Full Text Available The topic of neighbourhood-based social interactions has gained attention in the last decades in the light of urban policies that aim to deal with problems regarding social segregation and exclusion, quality of life and liveability in urban areas. Social interactions are expected to play an important role in dealing with these problems. However, empirical studies investigating to which extent neighbourhood characteristics can improve social contacts among residents are scarce and inconclusive. Therefore, this paper studies the role of socio-demographics and neighbourhood characteristics in the formation of social network ties and social interactions with neighbours. Based on data collected in 2011 in 70 different neighbourhoods of Eindhoven in the Netherlands in a survey among 751 respondents these relationships are analysed using a multi-level path analysis approach. The results indicate that neighbourhood-based contacts are influenced by personal and household characteristics, such as education, income, work status, ethnicity, household composition, and years at the current address. Neighbourhood characteristics are not found to affect social network size, the share of neighbours in the network or the frequency of interaction with neighbours.

  7. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Explaining the gender wage gap in Georgia

    OpenAIRE

    Khitarishvili, Tamar

    2009-01-01

    This paper evaluates gender wage differentials in Georgia between 2000 and 2004. Using ordinary least squares, we find that the gender wage gap in Georgia is substantially higher than in other transition countries. Correcting for sample selection bias using the Heckman approach further increases the gender wage gap. The Blinder Oaxaca decomposition results suggest that most of the wage gap remains unexplained. The explained portion of the gap is almost entirely attributed to industrial variab...

  9. Layered vanadyl (IV) nitroprusside: Magnetic interaction through a network of hydrogen bonds

    Energy Technology Data Exchange (ETDEWEB)

    Gil, D.M. [Instituto de Química Física, Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, San Lorenzo 456, T4000CAN San Miguel de Tucumán (Argentina); Osiry, H. [Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Legaria, Instituto Politécnico Nacional, México (Mexico); Pomiro, F.; Varetti, E.L. [CEQUINOR (CONICET-UNLP), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, 47 and 115, 1900, La Plata (Argentina); Carbonio, R.E. [INFIQC – CONICET, Departamento de Físico Química, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Haya de la Torre esq, Medina Allende, Ciudad Universitaria, X5000HUA Córdoba (Argentina); Alejandro, R.R. [Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Legaria, Instituto Politécnico Nacional, México (Mexico); Ben Altabef, A. [INQUINOA-UNT-CONICET, Instituto de Química Física, Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, San Lorenzo 456, T4000CAN San Miguel de Tucumán (Argentina); and others

    2016-07-15

    The hydrogen bond and π-π stacking are two non-covalent interactions able to support cooperative magnetic ordering between paramagnetic centers. This contribution reports the crystal structure and related magnetic properties for VO[Fe(CN){sub 5}NO]·2H{sub 2}O, which has a layered structure. This solid crystallizes with an orthorhombic unit cell, in the Pna2{sub 1} space group, with cell parameters a=14.1804(2), b=10.4935(1), c=7.1722(8) Å and four molecules per unit cell (Z=4). Its crystal structure was solved and refined from powder X-ray diffraction data. Neighboring layers remain linked through a network of hydrogen bonds involving a water molecule coordinated to the axial position for the V atom and the unbridged axial NO and CN ligands. An uncoordinated water molecule is found forming a triple bridge between these last two ligands and the coordinated water molecule. The magnetic measurements, recorded down to 2 K, shows a ferromagnetic interaction between V atoms located at neighboring layers, with a Curie-Weiss constant of 3.14 K. Such ferromagnetic behavior was interpreted as resulting from a superexchange interaction through the network of strong OH····O{sub H2O}, OH····N{sub CN}, and OH····O{sub NO} hydrogen bonds that connects neighboring layers. The interaction within the layer must be of antiferromagnetic nature and it was detected close to 2 K. - Graphical abstract: Coordination environment for the metals in vanadyl (II) nitroprusside dihydrate. Display Omitted - Highlights: • Crystal structure of vanadyl nitroprusside dehydrate. • Network of hydrogen bonds. • Magnetic interactions through a network of hydrogen bonds. • Layered transition metal nitroprussides.

  10. Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs.

    Science.gov (United States)

    Bolton, Thomas A W; Tarun, Anjali; Sterpenich, Virginie; Schwartz, Sophie; Van De Ville, Dimitri

    2018-01-01

    Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.

  11. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

    Science.gov (United States)

    Freilich, Mara A; Wieters, Evie; Broitman, Bernardo R; Marquet, Pablo A; Navarrete, Sergio A

    2018-03-01

    Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction

  12. Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy.

    Science.gov (United States)

    Liu, Bing; Li, Jiuyong; Tsykin, Anna; Liu, Lin; Gaur, Arti B; Goodall, Gregory J

    2009-12-10

    microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets

  13. Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

    Directory of Open Access Journals (Sweden)

    Liu Lin

    2009-12-01

    Full Text Available Abstract Background microRNAs (miRNAs regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT. Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and

  14. Forbidden versus permitted interactions: Disentangling processes from patterns in ecological network analysis.

    Science.gov (United States)

    Strona, Giovanni; Veech, Joseph A

    2017-07-01

    Several studies have identified the tendency for species to share interacting partners as a key property to the functioning and stability of ecological networks. However, assessing this pattern has proved challenging in several regards, such as finding proper metrics to assess node overlap (sharing), and using robust null modeling to disentangle significance from randomness. Here, we bring attention to an additional, largely neglected challenge in assessing species' tendency to share interacting partners. In particular, we discuss and illustrate with two different case studies how identifying the set of "permitted" interactions for a given species (i.e. interactions that are not impeded, e.g. by lack of functional trait compatibility) is paramount to understand the ecological and co-evolutionary processes at the basis of node overlap and segregation patterns.

  15. Carnegie Mellon's STUDIO for Creative Inquiry [and] The Interdisciplinary Teaching Network (ITeN) [and] Interactive Fiction [and] The Networked Virtual Art Museum.

    Science.gov (United States)

    Holden, Lynn; And Others

    1992-01-01

    Explains the STUDIO for Creative Inquiry, an interdisciplinary center at Carnegie Mellon University that supports experimental activities in the arts, and its Interdisciplinary Teaching Network. Three STUDIO projects are described: the Ancient Egypt Prototype application of the network; an interactive fiction system based on artificial…

  16. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    Science.gov (United States)

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Predicting protein complex in protein interaction network - a supervised learning based method.

    Science.gov (United States)

    Yu, Feng; Yang, Zhi; Tang, Nan; Lin, Hong; Wang, Jian; Yang, Zhi

    2014-01-01

    Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, making it possible to predict protein complexes from protein -protein interaction networks. However, most of current methods are unsupervised learning based methods which can't utilize the information of the large amount of available known complexes. We present a supervised learning-based method for predicting protein complexes in protein - protein interaction networks. The method extracts rich features from both the unweighted and weighted networks to train a Regression model, which is then used for the cliques filtering, growth, and candidate complex filtering. The model utilizes additional "uncertainty" samples and, therefore, is more discriminative when used in the complex detection algorithm. In addition, our method uses the maximal cliques found by the Cliques algorithm as the initial cliques, which has been proven to be more effective than the method of expanding from the seeding proteins used in other methods. The experimental results on several PIN datasets show that in most cases the performance of our method are superior to comparable state-of-the-art protein complex detection techniques. The results demonstrate the several advantages of our method over other state-of-the-art techniques. Firstly, our method is a supervised learning-based method that can make full use of the information of the available known complexes instead of being only based on the topological structure of the PIN. That also means, if more training samples are provided, our method can achieve better performance than those unsupervised methods. Secondly, we design the rich feature set to describe the properties of the known complexes, which includes not only the features from the unweighted network, but also those from the weighted network built based on the Gene Ontology information. Thirdly

  18. Global product development interaction between local networks: A study of the Danish food industry

    DEFF Research Database (Denmark)

    Kristensen, Preben Sander

    A study of the Danish foods industry shows that producers of food products largely ignore home marekt demand in their product development activities. They have built up and maintain development of end-user products in interaction with customers in distant sophisticated markets. Concurrently......, the Danish agro-industrial complex has been singled out in other studies as a paradigmatic example of how crucial a sophisticated home demand is for the development of an industrial complex. The apparen contradiction arises from the complex's ability to utilize a complemntarity between companies' equidistant...... view of actors in the global end-user customer market and companies' euclidean view of actors in thelocal business-to-business market. In pr companies combine these two market views by interacting in networks: The global industrial network links various functions which again are each part of a local...

  19. INTERNET AND POWER IN COMMUNICATION NETWORK SOCIETY: INFLUENCES ON FORMS OF SOCIAL INTERACTION

    Directory of Open Access Journals (Sweden)

    Márcia Cristina Gomes Molina

    2013-12-01

    Full Text Available This article presents reflections, through literature review, about the means of communication’s use, in special the internet in the transformation of social relations. From the perspective of Manuel Castells and John Thompson, the text presents an overview of the changes arising from the information technology and communication, highlighting the potential of the power of persuasion and dissemination of social networking sites such as Facebook and YouTube in the exercise of the self-masses, which transformed mass communication through speech, autonomy and interaction of its actors. The arguments presented in the text, is to highlight the role of information technologies in social interaction and the construction of a network society.

  20. Network Regulation and Support Schemes - How Policy Interactions Affect the Integration of Distributed Generation

    DEFF Research Database (Denmark)

    Ropenus, Stephanie; Jacobsen, Henrik; Schröder, Sascha Thorsten

    2011-01-01

    This article seeks to investigate the interactions between the policy dimensions of support schemes and network regulation and how they affect distributed generation. Firstly, the incentives of distributed generators and distribution system operators are examined. Frequently there exists a trade......-off between the incentives for these two market agents to facilitate the integration of distributed generation. Secondly, the interaction of these policy dimensions is analyzed, including case studies based on five EU Member States. Aspects of operational nature and investments in grid and distributed...

  1. Understanding interactions in virtual HIV communities: a social network analysis approach.

    Science.gov (United States)

    Shi, Jingyuan; Wang, Xiaohui; Peng, Tai-Quan; Chen, Liang

    2017-02-01

    This study investigated the driving mechanism of building interaction ties among the people living with HIV/AIDS in one of the largest virtual HIV communities in China using social network analysis. Specifically, we explained the probability of forming interaction ties with homophily and popularity characteristics. The exponential random graph modeling results showed that members in this community tend to form homophilous ties in terms of shared location and interests. Moreover, we found a tendency away from popularity effect. This suggests that in this community, resources and information were not disproportionally received by a few of members, which could be beneficial to the overall community.

  2. Real beards and real networks: a spin-glass model for interacting individuals

    Science.gov (United States)

    O'Neale, Dion

    ''I want to be different, just like all the other different people'' sang the band King Missile. Whether they are the Beatniks of the 1950s, the punks of the 1970s, or the hipsters of today, non-conformists often tend to look the same, seemingly at odds with their goal of non-conformity. The spin-glass model, originally developed to describe the interaction of magnetic spins, and since applied to situations as diverse as the electrical activity of networks of neurons, to trades on a financial market, has recently been used in social science to study populations of interacting individuals comprised of a mix of both conformists and anti-conformists - or hipsters. Including delay effects for the interactions between individuals has been shown to give a system with non-trivial dynamics with a phase transition from stable behaviour to periodic switching between two states (let's call them bushy bearded and clean shaven). Analytic solutions to such a model are possible, but only for particular assumptions about the interaction and delay matrices. In this work we will show what happens when the interactions in the model are based on real-world networks with ''small-world'' effects and clustering.

  3. STRING v9.1: protein-protein interaction networks, with increased coverage and integration.

    Science.gov (United States)

    Franceschini, Andrea; Szklarczyk, Damian; Frankild, Sune; Kuhn, Michael; Simonovic, Milan; Roth, Alexander; Lin, Jianyi; Minguez, Pablo; Bork, Peer; von Mering, Christian; Jensen, Lars J

    2013-01-01

    Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

  4. Network topology of stable isotope interactions in a sub-arctic raptor guild.

    Science.gov (United States)

    Dalerum, F; Hellström, P; Miranda, M; Nyström, J; Ekenstedt, J; Angerbjörn, A

    2016-10-01

    Predation is an ecologically important process, and intra-guild interactions may substantially influence the ecological effects of predator species. Despite a rapid expansion in the use of mathematical graph theory to describe trophic relations, network approaches have rarely been used to study interactions within predator assemblages. Assemblages of diurnal raptors are subject to substantial intra- and interspecific competition. Here we used the novel approach of applying analyzes based on network topology to species-specific data on the stable isotopes (13)C and (15)N in feathers to evaluate patterns of relative resource utilization within a guild of diurnal raptors in northern Sweden. Our guild consisted of the golden eagle (Aquila chrysaetos), the gyrfalcon (Falco rusticolus), the peregrine falcon (Falco peregrinus) and the rough-legged buzzard (Buteo lagopus). We found a modular trophic interaction structure within the guild, but the interactions were less nested than expected by chance. These results suggest low redundancy and hence a strong ecological importance of individual species. Our data also suggested that species were less connected through intra-guild interactions than expected by chance. We interpret our results as a convergence on specific isotope niches, and that body size and different hunting behaviour may mediate competition within these niches. We finally highlight that generalist predators could be ecologically important by linking specialist predator species with disparate dietary niches.

  5. Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism.

    Science.gov (United States)

    Haghiri, Saeed; Ahmadi, Arash; Saif, Mehrdad

    2017-02-01

    Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

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

    Science.gov (United States)

    Quinn, T M; Morel, V

    2007-01-01

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

  7. Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis.

    Science.gov (United States)

    Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

    2015-06-19

    Social media is becoming a new battlefield for tobacco "wars". Evaluating the current situation is very crucial for the advocacy of tobacco control in the age of social media. To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches. Our empirical studies demonstrate that the exploding pro-tobacco content has long-lasting effects with more active users and broader influence, and reveal the shortage of social media resources in global tobacco control. It is found that the user interaction in the pro-tobacco group is more active, and user-generated content for tobacco promotion is more successful in obtaining user attention. Furthermore, we construct three tobacco-related social networks and investigate the topological patterns of these tobacco-related social networks. We find that the size of the pro-tobacco network overwhelms the others, which suggests a huge number of users are exposed to the pro-tobacco content. These results indicate that the gap between tobacco promotion and tobacco control is widening and tobacco control may be losing ground to tobacco promotion in social media.

  8. The function of communities in protein interaction networks at multiple scales

    Directory of Open Access Journals (Sweden)

    Jones Nick S

    2010-07-01

    Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

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

    Directory of Open Access Journals (Sweden)

    Daniel Boloc

    Full Text Available 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.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.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.

  10. The Effects of Day-to-Day Interaction via Social Network Sites on Interpersonal Relationships

    OpenAIRE

    Houghton, David J

    2012-01-01

    The current research identifies the impact of sharing day-to-day information insocial network sites (SNS) on the relationships we hold within and outside of them. Stemming from the literature on self-disclosure, uncertainty reduction, personal relationships, privacy and computer-mediated communication (CMC), a concurrent triangulation research strategy is adopted to identify the patterns of relationship development and interaction in SNS. Using a mixed methods approach, five studies were cond...

  11. Neuroplasticity pathways and protein-interaction networks are modulated by vortioxetine in rodents

    DEFF Research Database (Denmark)

    Waller, Jessica A.; Nygaard, Sara Holm; Li, Yan

    2017-01-01

    and rat in response to distinct treatment regimens and in different brain regions. Furthermore, analysis of complexes of physically-interacting proteins reveal that biomarkers involved in transcriptional regulation, neurodevelopment, neuroplasticity, and endocytosis are modulated by vortioxetine....... A subsequent qPCR study examining the expression of targets in the protein-protein interactome space in response to chronic vortioxetine treatment over a range of doses provides further biological validation that vortioxetine engages neuroplasticity networks. Thus, the same biology is regulated in different...

  12. Sub-Synchronous Interaction Analysis between DFIG Based Wind Farm and Series Compensated Network

    OpenAIRE

    Wang, Yun; Wu, Qiuwei; Kang, Shaoli

    2016-01-01

    This paper analyzes the sub-synchronous interaction (SSI) phenomenon between the doubly fed induction generator (DFIG) based wind farm (WF) and the series capacitor compensated network. The possible types of SSI in the DFIG based WF are studied. The factors influencing the SSI of DFIG based WF are investigated. The large signal stability and small signal stability of the DFIG based WF with different series compensation (SC) level and wind speed are simulated and compared.

  13. Sub-Synchronous Interaction Analysis between DFIG Based Wind Farm and Series Compensated Network

    DEFF Research Database (Denmark)

    Wang, Yun; Wu, Qiuwei; Kang, Shaoli

    2016-01-01

    This paper analyzes the sub-synchronous interaction (SSI) phenomenon between the doubly fed induction generator (DFIG) based wind farm (WF) and the series capacitor compensated network. The possible types of SSI in the DFIG based WF are studied. The factors influencing the SSI of DFIG based WF...... are investigated. The large signal stability and small signal stability of the DFIG based WF with different series compensation (SC) level and wind speed are simulated and compared....

  14. Instability of interaction network for interstellar gas and interstellar diffusive energy in the shear field

    International Nuclear Information System (INIS)

    Fujimoto, Mitsuaki; Mizuno, Takao.

    1987-01-01

    A model network for interaction between interstellar gas and interstellar diffusive energy is considered in the shear field. Local linearized equations are derived around the equilibrium states which are realized when no shear field exists. A wavy perturbation is followed by employing the WKB method. It is concluded that the shear field brings about various unstable waves depending on their configuration. A great variety of observed dark and luminous pattern in spiral galaxies could be understood as related to these waves. (author)

  15. Coupling root architecture and pore network modeling - an attempt towards better understanding root-soil interactions

    Science.gov (United States)

    Leitner, Daniel; Bodner, Gernot; Raoof, Amir

    2013-04-01

    Understanding root-soil interactions is of high importance for environmental and agricultural management. Root uptake is an essential component in water and solute transport modeling. The amount of groundwater recharge and solute leaching significantly depends on the demand based plant extraction via its root system. Plant uptake however not only responds to the potential demand, but in most situations is limited by supply form the soil. The ability of the plant to access water and solutes in the soil is governed mainly by root distribution. Particularly under conditions of heterogeneous distribution of water and solutes in the soil, it is essential to capture the interaction between soil and roots. Root architecture models allow studying plant uptake from soil by describing growth and branching of root axes in the soil. Currently root architecture models are able to respond dynamically to water and nutrient distribution in the soil by directed growth (tropism), modified branching and enhanced exudation. The porous soil medium as rooting environment in these models is generally described by classical macroscopic water retention and sorption models, average over the pore scale. In our opinion this simplified description of the root growth medium implies several shortcomings for better understanding root-soil interactions: (i) It is well known that roots grow preferentially in preexisting pores, particularly in more rigid/dry soil. Thus the pore network contributes to the architectural form of the root system; (ii) roots themselves can influence the pore network by creating preferential flow paths (biopores) which are an essential element of structural porosity with strong impact on transport processes; (iii) plant uptake depend on both the spatial location of water/solutes in the pore network as well as the spatial distribution of roots. We therefore consider that for advancing our understanding in root-soil interactions, we need not only to extend our root models

  16. Demonstration Exercise of a Validated Sample Collection Method for Powders Suspected of Being Biological Agents in Georgia 2006

    International Nuclear Information System (INIS)

    Marsh, B.

    2007-01-01

    August 7, 2006 the state of Georgia conducted a collaborative sampling exercise between the Georgia National Guard 4th Civil Support Team Weapons of Mass Destruction (CST-WMD) and the Georgia Department of Human Resources Division of Public Health demonstrating a recently validated bulk powder sampling method. The exercise was hosted at the Federal Law Enforcement Training Center (FLETC) at Glynn County, Georgia and involved the participation of the Georgia Emergency Management Agency (GEMA), Georgia National Guard, Georgia Public Health Laboratories, the Federal Bureau of Investigation Atlanta Office, Georgia Coastal Health District, and the Glynn County Fire Department. The purpose of the exercise was to demonstrate a recently validated national sampling standard developed by the American Standards and Test Measures (ASTM) International; ASTM E2458 S tandard Practice for Bulk Sample Collection and Swab Sample Collection of Visible Powders Suspected of Being Biological Agents from Nonporous Surfaces . The intent of the exercise was not to endorse the sampling method, but to develop a model for exercising new sampling methods in the context of existing standard operating procedures (SOPs) while strengthening operational relationships between response teams and analytical laboratories. The exercise required a sampling team to respond real-time to an incident cross state involving a clandestine bio-terrorism production lab found within a recreational vehicle (RV). Sample targets consisted of non-viable gamma irradiated B. anthracis Sterne spores prepared by Dugway Proving Ground. Various spore concentration levels were collected by the ASTM method, followed by on- and off-scene analysis utilizing the Center for Disease Control (CDC) Laboratory Response Network (LRN) and National Guard Bureau (NGB) CST mobile Analytical Laboratory Suite (ALS) protocols. Analytical results were compared and detailed surveys of participant evaluation comments were examined. I will

  17. Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2016-10-06

    Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

  18. Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach

    Directory of Open Access Journals (Sweden)

    Linda Ponta

    2018-01-01

    Full Text Available An information-based multiasset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented and studied so as to determine the influences of agents’ networks on the market’s structure. Agents are organized in networks that are responsible for the formation of the sentiments of the agents. In the market, agents trade risky assets in exchange for cash and share their sentiments by means of interactions that are determined by sparsely connected graphs. A central market maker (clearing house mechanism determines the price process for each stock at the intersection of the demand and the supply curves. A set of market’s structure indicators based on the main single-assets and multiassets stylized facts have been defined, in order to study the effects of the agents’ networks. Results point out an intrinsic structural resilience of the stock market. In fact, the network is necessary in order to archive the ability to reproduce the main stylized facts, but also the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth.

  19. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. The Role of Human Relations and Interactions in Designing Memory-Related Models for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Basim MAHMOOD

    2016-04-01

    Full Text Available Recently, the use of Wireless Sensor Networks has become substantial in most of our life aspects. These networks have many issues and challenges at the design phase (e.g., memory and power consumption. There exists a huge amount of works and studies that offer and provide solutions for many of these challenges. However, the issues of predicting memory requirements and memory management have not received enough attention in sensor networks literature. Yet, most of the studies in this field focus on issues related to power consumption and connectivity of sensor nodes. This paper has two main purposes: first, we propose a metric for measuring the strength of a relation between two sensors. In the proposed metric, we involve three important characteristics of human relations and interactions: encounter frequencies, duration of encounters, and regularities of encounters. We then exploit this metric in predicting memory requirements in a sensor network. Second, based on the estimated memory size, we propose an approach for memory management in a sensor network. The proposed approach is based on two concepts: social capital in sociology and preferential return in human mobility. The results show that our approach is effective in managing sensor memories comparing to other approaches in the literature.

  1. Dynamics of person-to-person interactions from distributed RFID sensor networks.

    Directory of Open Access Journals (Sweden)

    Ciro Cattuto

    Full Text Available BACKGROUND: Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. METHODS AND FINDINGS: We use active Radio Frequency Identification (RFID devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. CONCLUSIONS: Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

  2. Transmission mode predicts specificity and interaction patterns in coral-Symbiodinium networks.

    Science.gov (United States)

    Fabina, Nicholas S; Putnam, Hollie M; Franklin, Erik C; Stat, Michael; Gates, Ruth D

    2012-01-01

    Most reef-building corals in the order Scleractinia depend on endosymbiotic algae in the genus Symbiodinium for energy and survival. Significant levels of taxonomic diversity in both partners result in numerous possible combinations of coral-Symbiodinium associations with unique functional characteristics. We created and analyzed the first coral-Symbiodinium networks utilizing a global dataset of interaction records from coral reefs in the tropical Indo-Pacific and Atlantic Oceans for 1991 to 2010. Our meta-analysis reveals that the majority of coral species and Symbiodinium types are specialists, but failed to detect any one-to-one obligate relationships. Symbiont specificity is correlated with a host's transmission mode, with horizontally transmitting corals being more likely to interact with generalist symbionts. Globally, Symbiodinium types tend to interact with only vertically or horizontally transmitting corals, and only a few generalist types are found with both. Our results demonstrate a strong correlation between symbiont specificity, symbiont transmission mode, and community partitioning. The structure and dynamics of these network interactions underlie the fundamental biological partnership that determines the condition and resilience of coral reef ecosystems.

  3. Transmission mode predicts specificity and interaction patterns in coral-Symbiodinium networks.

    Directory of Open Access Journals (Sweden)

    Nicholas S Fabina

    Full Text Available Most reef-building corals in the order Scleractinia depend on endosymbiotic algae in the genus Symbiodinium for energy and survival. Significant levels of taxonomic diversity in both partners result in numerous possible combinations of coral-Symbiodinium associations with unique functional characteristics. We created and analyzed the first coral-Symbiodinium networks utilizing a global dataset of interaction records from coral reefs in the tropical Indo-Pacific and Atlantic Oceans for 1991 to 2010. Our meta-analysis reveals that the majority of coral species and Symbiodinium types are specialists, but failed to detect any one-to-one obligate relationships. Symbiont specificity is correlated with a host's transmission mode, with horizontally transmitting corals being more likely to interact with generalist symbionts. Globally, Symbiodinium types tend to interact with only vertically or horizontally transmitting corals, and only a few generalist types are found with both. Our results demonstrate a strong correlation between symbiont specificity, symbiont transmission mode, and community partitioning. The structure and dynamics of these network interactions underlie the fundamental biological partnership that determines the condition and resilience of coral reef ecosystems.

  4. CellWhere: graphical display of interaction networks organized on subcellular localizations.

    Science.gov (United States)

    Zhu, Lu; Malatras, Apostolos; Thorley, Matthew; Aghoghogbe, Idonnya; Mer, Arvind; Duguez, Stéphanie; Butler-Browne, Gillian; Voit, Thomas; Duddy, William

    2015-07-01

    Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein-protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt-together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein-protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Exploring Homophobia in Tbilisi, Georgia.

    Science.gov (United States)

    Mestvirishvili, Maia; Zurabishvili, Tinatin; Iakobidze, Tamar; Mestvirishvili, Natia

    2017-01-01

    The purpose of this study is to determine statistical predictors of homophobic attitudes among the residents of Tbilisi, Georgia. We analyze 2013 survey data from a representative sample of the Tbilisi adult population. Residents were asked about their attitudes, beliefs, and political and social values in the context of the May 17, 2013 attack on LGBT activists on the International Day Against Homophobia and Transphobia (IDAHOT). Findings show that homophobia is significantly predicted by male gender, lower levels of education, acceptance of social inequality, nonliberal attitudes, and perceiving homosexuals as a "threat to national security." However, psychological perceptions and personal experiences also indirectly influence homophobic attitudes: the findings suggest that males report homophobic attitudes more often than females do and tend to be even more homophobic when they believe that homosexuality is inborn rather than acquired. The study also found that people without liberal attitudes tend to be more homophobic when they have personal contacts with homosexuals. This article highlights the need for a more comprehensive approach to education and the promotion of liberal values as well as legal equality for LGBTQ individuals to decrease the level of homophobia in Georgian society and, specifically, in Tbilisi.

  6. Interactive granular computations in networks and systems engineering a practical perspective

    CERN Document Server

    Jankowski, Andrzej

    2017-01-01

    The book outlines selected projects conducted under the supervision of the author. Moreover, it discusses significant relations between Interactive Granular Computing (IGrC) and numerous dynamically developing scientific domains worldwide, along with features characteristic of the author’s approach to IGrC. The results presented are a continuation and elaboration of various aspects of Wisdom Technology, initiated and developed in cooperation with Professor Andrzej Skowron. Based on the empirical findings from these projects, the author explores the following areas: (a) understanding the causes of the theory and practice gap problem (TPGP) in complex systems engineering (CSE);(b) generalizing computing models of complex adaptive systems (CAS) (in particular, natural computing models) by constructing an interactive granular computing (IGrC) model of networks of interrelated interacting complex granules (c-granules), belonging to a single agent and/or to a group of agents; (c) developing methodologies based ...

  7. An intracellular interaction network regulates conformational transitions in the dopamine transporter

    DEFF Research Database (Denmark)

    Kniazeff, Julie; Shi, Lei; Løland, Claus Juul

    2008-01-01

    Neurotransmitter:sodium symporters (NSS)(1) mediate sodium-dependent reuptake of neurotransmitters from the synaptic cleft and are targets for many psychoactive drugs. The crystal structure of the prokaryotic NSS protein, LeuT, was recently solved at high resolution; however, the mechanistic...... details of regulation of the permeation pathway in this class of proteins remain unknown. Here we combine computational modeling and experimental probing in the dopamine transporter (DAT) to demonstrate the functional importance of a conserved intracellular interaction network. Our data suggest...... that a salt bridge between Arg-60 in the N terminus close to the cytoplasmic end of transmembrane segment (TM) 1 and Asp-436 at the cytoplasmic end of TM8 is stabilized by a cation-pi interaction between Arg-60 and Tyr-335 at the cytoplasmic end of TM6. Computational probing illustrates how the interactions...

  8. Qualities of Peer Relations on Social Networking Websites: Predictions from Negative Mother-Teen Interactions

    Science.gov (United States)

    Szwedo, David E.; Mikami, Amori Yee; Allen, Joseph P.

    2010-01-01

    This study examined associations between characteristics of teenagers’ relationships with their mothers and their later socializing behavior and peer relationship quality online. At age 13, teenagers and their mothers participated in an interaction in which mothers’ and adolescents’ behavior undermining autonomy and relatedness was observed, and indicators of teens’ depressive symptoms and social anxiety were assessed. At age 20, youth self-reported on their online behaviors, youths’ social networking webpages were observationally coded to assess peer relationship quality online, and symptoms of depression and social anxiety were reassessed. Results suggested that problematic mother-teen relationships were predictive of youths’ later preference for online communication and greater likelihood of forming a friendship with someone met online, yet poorer quality in online relationships. Findings are discussed within a developmental framework suggesting the importance of considering youths’ family interactions during early adolescence as predictors of future online socializing behavior and online interactions with peers. PMID:21860584

  9. Trophic level, successional age and trait matching determine specialization of deadwood-based interaction networks of saproxylic beetles.

    Science.gov (United States)

    Wende, Beate; Gossner, Martin M; Grass, Ingo; Arnstadt, Tobias; Hofrichter, Martin; Floren, Andreas; Linsenmair, Karl Eduard; Weisser, Wolfgang W; Steffan-Dewenter, Ingolf

    2017-05-17

    The specialization of ecological networks provides important insights into possible consequences of biodiversity loss for ecosystem functioning. However, mostly mutualistic and antagonistic interactions of living organisms have been studied, whereas detritivore networks and their successional changes are largely unexplored. We studied the interactions of saproxylic (deadwood-dependent) beetles with their dead host trees. In a large-scale experiment, 764 logs of 13 tree species were exposed to analyse network structure of three trophic groups of saproxylic beetles over 3 successional years. We found remarkably high specialization of deadwood-feeding xylophages and lower specialization of fungivorous and predatory species. During deadwood succession, community composition, network specialization and network robustness changed differently for the functional groups. To reveal potential drivers of network specialization, we linked species' functional traits to their network roles, and tested for trait matching between plant (i.e. chemical compounds) and beetle (i.e. body size) traits. We found that both plant and animal traits are major drivers of species specialization, and that trait matching can be more important in explaining interactions than neutral processes reflecting species abundance distributions. High network specialization in the early successional stage and decreasing network robustness during succession indicate vulnerability of detritivore networks to reduced tree species diversity and beetle extinctions, with unknown consequences for wood decomposition and nutrient cycling. © 2017 The Author(s).

  10. Interaction of multiple networks modulated by the working memory training based on real-time fMRI

    Science.gov (United States)

    Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

    2015-03-01

    Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

  11. Emergence of modular structure in a large-scale brain network with interactions between dynamics and connectivity

    Directory of Open Access Journals (Sweden)

    Cornelis Jan Stam

    2010-09-01

    Full Text Available A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an EEG / MEG like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i synchronization dependent plasticity (SDP and (ii growth dependent plasticity (GDP. In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.

  12. Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network

    Science.gov (United States)

    Patil, Ashwini; Kinoshita, Kengo; Nakamura, Haruki

    2010-01-01

    Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non-hubs in protein–protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non-hubs. We found that proteins with a more diverse domain composition were over-represented in hubs when compared with non-hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non-hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non-hubs had more DNA-binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non-hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains. PMID:20509167

  13. Domain distribution and intrinsic disorder in hubs in the human protein-protein interaction network.

    Science.gov (United States)

    Patil, Ashwini; Kinoshita, Kengo; Nakamura, Haruki

    2010-08-01

    Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non-hubs in protein-protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non-hubs. We found that proteins with a more diverse domain composition were over-represented in hubs when compared with non-hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non-hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non-hubs had more DNA-binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non-hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains.

  14. Let's Face(book) It: Analyzing Interactions in Social Network Groups for Chemistry Learning

    Science.gov (United States)

    Rap, Shelley; Blonder, Ron

    2016-02-01

    We examined how social network (SN) groups contribute to the learning of chemistry. The main goal was to determine whether chemistry learning could occur in the group discourse. The emphasis was on groups of students in the 11th and 12th grades who learn chemistry in preparation for their final external examination. A total of 1118 discourse events were tallied in the different groups. We analyzed the different events that were found in chemistry learning Facebook groups (CLFGs). The analysis revealed that seven types of interactions were observed in the CLFGs: The most common interaction (47 %) dealt with organizing learning (e.g., announcements regarding homework, the location of the next class); learning interactions were observed in 22 % of the posts, and links to learning materials and social interactions constituted about 20 % each. The learning events that were ascertained underwent a deeper examination and three different types of chemistry learning interactions were identified. This examination was based on the theoretical framework of the commognitive approach to learning (Sfard in Thinking as communicating. Cambridge University Press, Cambridge, 2008), which will be explained. The identified learning interactions that were observed in the Facebook groups illustrate the potential of SNs to serve as an additional tool for teachers to advance their students' learning of chemistry.

  15. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.

    Science.gov (United States)

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.

  16. Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.

    Directory of Open Access Journals (Sweden)

    Darcy A Davis

    Full Text Available The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.

  17. Emotion perception and executive control interact in the salience network during emotionally charged working memory processing.

    Science.gov (United States)

    Luo, Yu; Qin, Shaozheng; Fernández, Guillén; Zhang, Yu; Klumpers, Floris; Li, Hong

    2014-11-01

    Processing of emotional stimuli can either hinder or facilitate ongoing working memory (WM); however, the neural basis of these effects remains largely unknown. Here we examined the neural mechanisms of these paradoxical effects by implementing a novel emotional WM task in an fMRI study. Twenty-five young healthy participants performed an N-back task with fearful and neutral faces as stimuli. Participants made more errors when performing 0-back task with fearful versus neutral faces, whereas they made fewer errors when performing 2-back task with fearful versus neutral faces. These emotional impairment and enhancement on behavioral performance paralleled significant interactions in distributed regions in the salience network including anterior insula (AI) and dorsal cingulate cortex (dACC), as well as in emotion perception network including amygdala and temporal-occipital association cortex (TOC). The dorsal AI (dAI) and dACC were more activated when comparing fearful with neutral faces in 0-back task. Contrarily, dAI showed reduced activation, while TOC and amygdala showed stronger responses to fearful as compared to neutral faces in the 2-back task. These findings provide direct neural evidence to the emerging dual competition model suggesting that the salience network plays a critical role in mediating interaction between emotion perception and executive control when facing ever-changing behavioral demands. Copyright © 2014 Wiley Periodicals, Inc.

  18. A Failed Experiment: Georgia's Tax Credit Scholarships for Private Schools

    Science.gov (United States)

    Southern Education Foundation, 2011

    2011-01-01

    Georgia is one of seven states that currently allow tax credits for scholarships to private schools. Georgia's law was enacted in May 2008 in order to assist low income students to transfer out of low performing public schools. Operations under the new act began in late 2008. The law permits taxpayers in Georgia to reduce their annual state taxes…

  19. An interaction network perspective on the relation between patterns of sea surface temperature variability and global mean surface temperature

    NARCIS (Netherlands)

    Tantet, A.J.J.; Dijkstra, H.A.

    2014-01-01

    On interannual- to multidecadal timescales variability in sea surface temperature appears to be organized in large-scale spatiotemporal patterns. In this paper, we investigate these patterns by studying the community structure of interaction networks constructed from sea surface temperature

  20. Train-Network Interactions and Stability Evaluation in High-Speed Railways--Part I: Phenomena and Modeling

    DEFF Research Database (Denmark)

    Hu, Haitao; Tao, Haidong; Blaabjerg, Frede

    2018-01-01

    This paper presents an impedance-based model to systematically investigate the interaction performance of multiple trains and traction network interaction system, aiming to evaluate the serious phenomena, including low-frequency oscillation (LFO), harmonic resonance and resonance instability....... The train-network interaction mechanism is therefore studied and one presents a detailed coupling model for investigating the three interactive phenomena and their characteristics, influential factors, analysis methods and possible mitigation schemes. In the Part I of the two-part paper, the measured...

  1. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks.

    Science.gov (United States)

    Li, Fei; Li, Peng; Xu, Wenjian; Peng, Yuxing; Bo, Xiaochen; Wang, Shengqi

    2010-01-15

    The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. Supplementary data are available at Bioinformatics online.

  2. Modification of gene duplicability during the evolution of protein interaction network.

    Directory of Open Access Journals (Sweden)

    Matteo D'Antonio

    2011-04-01

    Full Text Available Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.

  3. Analysis of Two-Worm Interaction Model in Heterogeneous M2M Network

    Directory of Open Access Journals (Sweden)

    Jinhua Ma

    2015-10-01

    Full Text Available With the rapid development of M2M (Machine-to-Machine networks, the damages caused by malicious worms are getting more and more serious. By considering the influences of the network heterogeneity on worm spreading, we are the first to study the complex interaction dynamics between benign worms and malicious worms in heterogeneous M2M network. We analyze and compare three worm propagation models based on different immunization schemes. By investigating the local stability of the worm-free equilibrium, we obtain the basic reproduction number R0 . Besides, by using suitable Lyapunov functions, we prove that the worm-free equilibrium is globally asymptotically stable if R0 ≤ 1 , otherwise unstable. The dynamics of worm models is completely determined by R0 . In the absence of birth, death and users’ treatment, we obtain the final size formula of worms. This study shows that the nodes with higher node degree are more susceptible to be infected than those with lower node degree. In addition, the effects of various immunization schemes are studied. Numerical simulations verify our theoretical results. The research results are meaningful for us to further understand the spread of worms in heterogeneous M2M network, and enact effectual control tactics.

  4. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  5. Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization.

    Science.gov (United States)

    Warner, Jeremy L; Denny, Joshua C; Kreda, David A; Alterovitz, Gil

    2015-03-01

    Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome. Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Deciphering the protein-protein interaction network regulating hepatocellular carcinoma metastasis.

    Science.gov (United States)

    Qin, Guoxuan; Dang, Mengjiao; Gao, Huajun; Wang, Hao; Luo, Fengting; Chen, Ruibing

    2017-09-01

    Hepatocellular carcinoma (HCC) is one of the leading causes of mortality related to cancer all over the world. To better understand the molecular mechanisms of HCC metastasis, we analyzed the proteome of three HCC cell lines with different metastasis potentials by quantitative proteomics and bioinformatics analysis. As a result, we identified 378 cellular proteins potentially associated to HCC metastasis, and constructed a highly connected protein-protein interaction (PPI) network. Functional annotation of the network uncovered prominent pathways and key roles of these proteins, suggesting that the metabolism and cytoskeleton biological processes are greatly involved with HCC metastasis. Furthermore, the integrative network analysis revealed a rich-club organization within the PPI network, indicating a hub center of connections. The rich-club nodes include several well-known cancer-related proteins, such as proto-oncogene non-receptor tyrosine kinase (SRC) and pyruvate kinase M2 (PKM2). Moreover, the differential expressions of two identified proteins, including PKM2 and actin-related protein 2/3 complex subunit 4 (ARPC4), were validated using Western blotting. These two proteins were revealed as potential prognostic markers for HCC as shown by survival rate analysis. Copyright © 2017. Published by Elsevier B.V.

  7. Modeling interacting dynamic networks: II. Systematic study of the statistical properties of cross-links between two networks with preferred degrees

    International Nuclear Information System (INIS)

    Liu, Wenjia; Schmittmann, B; Zia, R K P

    2014-01-01

    In a recent work (Liu et al, 2013 J. Stat. Mech. P08001), we introduced dynamic networks with preferred degrees and presented simulation and analytic studies of a single, homogeneous system as well as two interacting networks. Here, we extend these studies to a wider range of parameter space, in a more systematic fashion. Though the interaction we introduced seems simple and intuitive, it produced dramatically different behavior in the single- and two-network systems. Specifically, partitioning the single network into two identical sectors, we find the cross-link distribution to be a sharply peaked Gaussian. In stark contrast, we find a very broad and flat plateau in the case of two interacting identical networks. A sound understanding of this phenomenon remains elusive. Exploring more asymmetric interacting networks, we discover a kind of ‘universal behavior’ for systems in which the ‘introverts’ (nodes with smaller preferred degree) are far outnumbered. Remarkably, an approximation scheme for their degree distribution can be formulated, leading to very successful predictions. (paper)

  8. The Geographic Reference Atlas of Georgia

    DEFF Research Database (Denmark)

    Liparteliani, Guliza; Kurtubadze, Manana; Sologhashvili, Nato

    2017-01-01

    Developing the national atlases as a main goal of geography has been determined by the 18th International Geographical Congress in Rio-de-Janeiro in 1965. The interest to create thematic complex atlases was increased. The first and the second National Atlases of Georgia show a high level of the e......Developing the national atlases as a main goal of geography has been determined by the 18th International Geographical Congress in Rio-de-Janeiro in 1965. The interest to create thematic complex atlases was increased. The first and the second National Atlases of Georgia show a high level...... of the exploration of the nature, population, economy and history of Georgia. Both, the first and the second atlases are scientific by the content, the purpose and the design. Nowadays, Georgian society requires to understand country not only from maps, but also from photos, related text annotations and graphic...

  9. Prediction of Narcissism, Perception of Social Interactions and Marital Conflicts Based on the Use of Social Networks

    Directory of Open Access Journals (Sweden)

    رویا رضاپور

    2017-09-01

    Full Text Available The prevalence of social networks and the excessive use of them by couples have had a significant impact on various aspects of their lives. The aim of this study was to investigate the role of social networks in the formation of narcissism, perception of social interaction and marital conflicts in couples who use these social networks. The study design was correlational and the statistical population included couples of Zanjan city who use social networks. 120 couples which widely used social networks were selected by random sampling. The questionnaires of Internet Addiction (Young, 1998, Narcissistic Personality (Ames and et al, 2006, Perception of Social Interaction (Glass, 1994 and Marital Conflict (Sanaei, 2000 were used. Pearson correlation coefficient and Regression were used for data analysis. This study showed that there is a significant negative relationship between the use of social networks with perception of social interaction, and a significant positive relationship between the use of social networks with narcissism and marital conflicts (P<0/01. Also narcissism has a significant positive relationship with marital conflicts, and a significant negative relationship with perception of social interaction (P<0/01. Social networks have a negative effect on couple's relationship and their feelings towards each other, as well as strengthening narcissism, which can cause communication problems, decreased positive feelings of couples towards each other and marital conflicts.

  10. Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources

    OpenAIRE

    Griffiths, E C; Pedersen, A B; Fenton, A; Petchey, O L

    2014-01-01

    Simultaneous infection by multiple parasite species (viruses, bacteria, helminths, protozoa or fungi) is commonplace. Most reports show co-infected humans to have worse health than those with single infections. However, we have little understanding of how co-infecting parasites interact within human hosts. We used data from over 300 published studies to construct a network that offers the first broad indications of how groups of co-infecting parasites tend to interact. The network had three l...

  11. SLIDER: a generic metaheuristic for the discovery of correlated motifs in protein-protein interaction networks.

    Science.gov (United States)

    Boyen, Peter; Van Dyck, Dries; Neven, Frank; van Ham, Roeland C H J; van Dijk, Aalt D J

    2011-01-01

    Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.

  12. Geometric universality of currents in an open network of interacting particles

    International Nuclear Information System (INIS)

    Sinitsyn, Nikolai A.; Chernyak, Vladimir Y.; Chertkov, Michael

    2010-01-01

    We discuss a non-equilibrium statistical system on a graph or network. Identical particles are injected, interact with each other, traverse, and leave the graph in a stochastic manner described in terms of Poisson rates, possibly dependent on time and instantaneous occupation numbers at the nodes of the graph. We show that under the assumption of the relative rates constancy, the system demonstrates a profound statistical symmetry, resulting in geometric universality of the particle currents statistics. The phenomenon applies broadly to many man-made and natural open stochastic systems, such as queuing of packages over internet, transport of electrons and quasi-particles in mesoscopic systems, and chains of reactions in bio-chemical networks. We illustrate the utility of the general approach using two enabling examples from the two latter disciplines.

  13. Patterns of cooperation: fairness and coordination in networks of interacting agents

    Science.gov (United States)

    Do, Anne-Ly; Rudolf, Lars; Gross, Thilo

    2010-06-01

    We study the self-assembly of a complex network of collaborations among self-interested agents. The agents can maintain different levels of cooperation with different partners. Further, they continuously, selectively and independently adapt the amount of resources allocated to each of their collaborations in order to maximize the obtained payoff. We show analytically that the system approaches a state in which the agents make identical investments, and links produce identical benefits. Despite this high degree of social coordination, some agents manage to secure privileged topological positions in the network, enabling them to extract high payoffs. Our analytical investigations provide a rationale for the emergence of unidirectional non-reciprocal collaborations and different responses to the withdrawal of a partner from an interaction that have been reported in the psychological literature.

  14. Network analysis applications in hydrology

    Science.gov (United States)

    Price, Katie

    2017-04-01

    Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

  15. Interaction patterns of nurturant support exchanged in online health social networking.

    Science.gov (United States)

    Chuang, Katherine Y; Yang, Christopher C

    2012-05-03

    Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. The computer-mediated communication format may have the greatest influence on the supportive interactions

  16. Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-01-01

    Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with

  17. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

    Science.gov (United States)

    Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

    2017-06-01

    Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  18. Gastric microbiome of Indian patients with Helicobacter pylori infection, and their interaction networks.

    Science.gov (United States)

    Das, Anubhav; Pereira, Verima; Saxena, Shruti; Ghosh, Tarini Shankar; Anbumani, D; Bag, Satyabrata; Das, Bhabatosh; Nair, G Balakrish; Abraham, Philip; Mande, Sharmila S

    2017-11-13

    The gastric microbiome is suspected to have a role in the causation of diseases by Helicobacter pylori. Reports on their relative abundance vis-à-vis H. pylori are available from various ethnic and geographic groups, but little is known about their interaction patterns. Endoscopic mucosal biopsy samples from the gastric antrum and corpus of 39 patients with suspected H. pylori infection were collected and microbiomes were analyzed by 16S rDNA profiling. Four groups of samples were identified, which harbored Helicobacter as well as a diverse group of bacteria including Lactobacillus, Halomonas and Prevotella. There was a negative association between the microbiome diversity and Helicobacter abundance. Network analyses showed that Helicobacter had negative interactions with members of the gastric microbiome, while other microbes interacted positively with each other, showing a higher tendency towards intra-cluster co-occurrence/co-operation. Cross-geographic comparisons suggested the presence of region-specific microbial abundance profiles. We report the microbial diversity, abundance variation and interaction patterns of the gastric microbiota of Indian patients with H. pylori infection and present a comparison of the same with the gastric microbial ecology in samples from different geographic regions. Such microbial abundance profiles and microbial interactions can help in understanding the pathophysiology of gastric ailments and can thus help in development of new strategies to curb it.

  19. Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

    Science.gov (United States)

    Song, Xian-Dong; Song, Xian-Xu; Liu, Gui-Bo; Ren, Chun-Hui; Sun, Yuan-Bo; Liu, Ke-Xin; Liu, Bo; Liang, Shuang; Zhu, Zhu

    2018-03-01

    The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

  20. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.

    Science.gov (United States)

    Zhang, Wen; Chen, Yanlin; Liu, Feng; Luo, Fei; Tian, Gang; Li, Xiaohong

    2017-01-05

    Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ .

  1. Species interactions in an Andean bird-flowering plant network: phenology is more important than abundance or morphology.

    Science.gov (United States)

    Gonzalez, Oscar; Loiselle, Bette A

    2016-01-01

    Biological constraints and neutral processes have been proposed to explain the properties of plant-pollinator networks. Using interactions between nectarivorous birds (hummingbirds and flowerpiercers) and flowering plants in high elevation forests (i.e., "elfin" forests) of the Andes, we explore the importance of biological constraints and neutral processes (random interactions) to explain the observed species interactions and network metrics, such as connectance, specialization, nestedness and asymmetry. In cold environments of elfin forests, which are located at the top of the tropical montane forest zone, many plants are adapted for pollination by birds, making this an ideal system to study plant-pollinator networks. To build the network of interactions between birds and plants, we used direct field observations. We measured abundance of birds using mist-nets and flower abundance using transects, and phenology by scoring presence of birds and flowers over time. We compared the length of birds' bills to flower length to identify "forbidden interactions"-those interactions that could not result in legitimate floral visits based on mis-match in morphology. Diglossa flowerpiercers, which are characterized as "illegitimate" flower visitors, were relatively abundant. We found that the elfin forest network was nested with phenology being the factor that best explained interaction frequencies and nestedness, providing support for biological constraints hypothesis. We did not find morphological constraints to be important in explaining observed interaction frequencies and network metrics. Other network metrics (connectance, evenness and asymmetry), however, were better predicted by abundance (neutral process) models. Flowerpiercers, which cut holes and access flowers at their base and, consequently, facilitate nectar access for other hummingbirds, explain why morphological mis-matches were relatively unimportant in this system. Future work should focus on how changes in

  2. Interacting opinion and disease dynamics in multiplex networks: Discontinuous phase transition and nonmonotonic consensus times.

    Science.gov (United States)

    Velásquez-Rojas, Fátima; Vazquez, Federico

    2017-05-01

    Opinion formation and disease spreading are among the most studied dynamical processes on complex networks. In real societies, it is expected that these two processes depend on and affect each other. However, little is known about the effects of opinion dynamics over disease dynamics and vice versa, since most studies treat them separately. In this work we study the dynamics of the voter model for opinion formation intertwined with that of the contact process for disease spreading, in a population of agents that interact via two types of connections, social and contact. These two interacting dynamics take place on two layers of networks, coupled through a fraction q of links present in both networks. The probability that an agent updates its state depends on both the opinion and disease states of the interacting partner. We find that the opinion dynamics has striking consequences on the statistical properties of disease spreading. The most important is that the smooth (continuous) transition from a healthy to an endemic phase observed in the contact process, as the infection probability increases beyond a threshold, becomes abrupt (discontinuous) in the two-layer system. Therefore, disregarding the effects of social dynamics on epidemics propagation may lead to a misestimation of the real magnitude of the spreading. Also, an endemic-healthy discontinuous transition is found when the coupling q overcomes a threshold value. Furthermore, we show that the disease dynamics delays the opinion consensus, leading to a consensus time that varies nonmonotonically with q in a large range of the model's parameters. A mean-field approach reveals that the coupled dynamics of opinions and disease can be approximately described by the dynamics of the voter model decoupled from that of the contact process, with effective probabilities of opinion and disease transmission.

  3. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  4. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  5. Mental health and social networks in early adolescence: a dynamic study of objectively-measured social interaction behaviors.

    Science.gov (United States)

    Pachucki, Mark C; Ozer, Emily J; Barrat, Alain; Cattuto, Ciro

    2015-01-01

    How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment--such as physical activity and food choice--may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early

  6. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    BACKGROUND: Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different......-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs...

  7. Signaling by Small GTPases at Cell-Cell junctions: Protein Interactions Building Control and Networks.

    Science.gov (United States)

    Braga, Vania

    2017-09-11

    A number of interesting reports highlight the intricate network of signaling proteins that coordinate formation and maintenance of cell-cell contacts. We have much yet to learn about how the in vitro binding data is translated into protein association inside the cells and whether such interaction modulates the signaling properties of the protein. What emerges from recent studies is the importance to carefully consider small GTPase activation in the context of where its activation occurs, which upstream regulators are involved in the activation/inactivation cycle and the GTPase interacting partners that determine the intracellular niche and extent of signaling. Data discussed here unravel unparalleled cooperation and coordination of functions among GTPases and their regulators in supporting strong adhesion between cells. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

  8. Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

    Science.gov (United States)

    Handcock, Rebecca N.; Swain, Dave L.; Bishop-Hurley, Greg J.; Patison, Kym P.; Wark, Tim; Valencia, Philip; Corke, Peter; O'Neill, Christopher J.

    2009-01-01

    Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle. PMID:22412327

  9. Integrating Micro-level Interactions with Social Network Analysis in Tie Strength Research

    DEFF Research Database (Denmark)

    Torre, Osku; Gupta, Jayesh Prakash; Kärkkäinen, Hannu

    2017-01-01

    strength measurements may not reflect the true social connections of individuals accurately enough, and 2) many different methods to gather data from social media are not applicable anymore due to different data openness issues. In addition, we have only little empirical knowledge of the actual tie...... of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages......, relationships) with large-scale social network analysis (SNA). This study provides a way to find relevant actors from publicly available data in the context of tie strengthening process, and answers how to take this stream of research closer to computational social science....

  10. Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

    Directory of Open Access Journals (Sweden)

    Peter Corke

    2009-05-01

    Full Text Available Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs. We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

  11. Species interactions in an Andean bird–flowering plant network: phenology is more important than abundance or morphology

    Directory of Open Access Journals (Sweden)

    Oscar Gonzalez

    2016-12-01

    Full Text Available Biological constraints and neutral processes have been proposed to explain the properties of plant–pollinator networks. Using interactions between nectarivorous birds (hummingbirds and flowerpiercers and flowering plants in high elevation forests (i.e., “elfin” forests of the Andes, we explore the importance of biological constraints and neutral processes (random interactions to explain the observed species interactions and network metrics, such as connectance, specialization, nestedness and asymmetry. In cold environments of elfin forests, which are located at the top of the tropical montane forest zone, many plants are adapted for pollination by birds, making this an ideal system to study plant–pollinator networks. To build the network of interactions between birds and plants, we used direct field observations. We measured abundance of birds using mist-nets and flower abundance using transects, and phenology by scoring presence of birds and flowers over time. We compared the length of birds’ bills to flower length to identify “forbidden interactions”—those interactions that could not result in legitimate floral visits based on mis-match in morphology. Diglossa flowerpiercers, which are characterized as “illegitimate” flower visitors, were relatively abundant. We found that the elfin forest network was nested with phenology being the factor that best explained interaction frequencies and nestedness, providing support for biological constraints hypothesis. We did not find morphological constraints to be important in explaining observed interaction frequencies and network metrics. Other network metrics (connectance, evenness and asymmetry, however, were better predicted by abundance (neutral process models. Flowerpiercers, which cut holes and access flowers at their base and, consequently, facilitate nectar access for other hummingbirds, explain why morphological mis-matches were relatively unimportant in this system. Future

  12. Determination of the Interaction Position of Gamma Photons in Monolithic Scintillators Using Neural Network Fitting

    Science.gov (United States)

    Conde, P.; Iborra, A.; González, A. J.; Hernández, L.; Bellido, P.; Moliner, L.; Rigla, J. P.; Rodríguez-Álvarez, M. J.; Sánchez, F.; Seimetz, M.; Soriano, A.; Vidal, L. F.; Benlloch, J. M.

    2016-02-01

    In Positron Emission Tomography (PET) detectors based on monolithic scintillators, the photon interaction position needs to be estimated from the light distribution (LD) on the photodetector pixels. Due to the finite size of the scintillator volume, the symmetry of the LD is truncated everywhere except for the crystal center. This effect produces a poor estimation of the interaction positions towards the edges, an especially critical situation when linear algorithms, such as Center of Gravity (CoG), are used. When all the crystal faces are painted black, except the one in contact with the photodetector, the LD can be assumed to behave as the inverse square law, providing a simple theoretical model. Using this LD model, the interaction coordinates can be determined by means of fitting each event to a theoretical distribution. In that sense, the use of neural networks (NNs) has been shown to be an effective alternative to more traditional fitting techniques as nonlinear least squares (LS). The multilayer perceptron is one type of NN which can model non-linear functions well and can be trained to accurately generalize when presented with new data. In this work we have shown the capability of NNs to approximate the LD and provide the interaction coordinates of γ-photons with two different photodetector setups. One experimental setup was based on analog Silicon Photomultipliers (SiPMs) and a charge division diode network, whereas the second setup was based on digital SiPMs (dSiPMs). In both experiments NNs minimized border effects. Average spatial resolutions of 1.9 ±0.2 mm and 1.7 ±0.2 mm for the entire crystal surface were obtained for the analog and dSiPMs approaches, respectively.

  13. Prior fear conditioning and reward learning interact in fear and reward networks

    Directory of Open Access Journals (Sweden)

    Lisa eBulganin

    2014-03-01

    Full Text Available The ability to flexibly adapt responses to changes in the environment is important for survival. Previous research in humans separately examined the mechanisms underlying acquisition and extinction of aversive and appetitive conditioned responses. It is yet unclear how aversive and appetitive learning interact on a neural level during counterconditioning in humans. This functional magnetic resonance imaging (fMRI study investigated the interaction of fear conditioning and subsequent reward learning. In the first phase (fear acquisition, images predicted aversive electric shocks or no aversive outcome. In the second phase (counterconditioning, half of the CS+ and CS- were associated with monetary reward in the absence of electric stimulation. The third phase initiated reinstatement of fear through presentation of electric shocks, followed by CS presentation in the absence of shock or reward. Results indicate that participants were impaired at learning the reward contingencies for stimuli previously associated with shock. In the counterconditioning phase, prior fear association interacted with reward representation in the amygdala, where activation was decreased for rewarded compared to unrewarded CS- trials, while there was no reward-related difference in CS+ trials. In the reinstatement phase, an interaction of previous fear association and previous reward status was observed in a reward network consisting of substantia nigra / ventral tegmental area (SN/VTA, striatum and orbitofrontal cortex (OFC, where activation was increased by previous reward association only for CS- but not for CS+ trials. These findings suggest that during counterconditioning, prior fear conditioning interferes with reward learning, subsequently leading to lower activation of the reward network.

  14. Salt-bridge networks within globular and disordered proteins: characterizing trends for designable interactions.

    Science.gov (United States)

    Basu, Sankar; Mukharjee, Debasish

    2017-07-01

    There has been considerable debate about the contribution of salt bridges to the stabilization of protein folds, in spite of their participation in crucial protein functions. Salt bridges appear to contribute to the activity-stability trade-off within proteins by bringing high-entropy charged amino acids into close contacts during the course of their functions. The current study analyzes the modes of association of salt bridges (in terms of networks) within globular proteins and at protein-protein interfaces. While the most common and trivial type of salt bridge is the isolated salt bridge, bifurcated salt bridge appears to be a distinct salt-bridge motif having a special topology and geometry. Bifurcated salt bridges are found ubiquitously in proteins and interprotein complexes. Interesting and attractive examples presenting different modes of interaction are highlighted. Bifurcated salt bridges appear to function as molecular clips that are used to stitch together large surface contours at interacting protein interfaces. The present work also emphasizes the key role of salt-bridge-mediated interactions in the partial folding of proteins containing long stretches of disordered regions. Salt-bridge-mediated interactions seem to be pivotal to the promotion of "disorder-to-order" transitions in small disordered protein fragments and their stabilization upon binding. The results obtained in this work should help to guide efforts to elucidate the modus operandi of these partially disordered proteins, and to conceptualize how these proteins manage to maintain the required amount of disorder even in their bound forms. This work could also potentially facilitate explorations of geometrically specific designable salt bridges through the characterization of composite salt-bridge networks. Graphical abstract ᅟ.

  15. Interactive algorithms for teaching and learning acute medicine in the network of medical faculties MEFANET.

    Science.gov (United States)

    Schwarz, Daniel; Štourač, Petr; Komenda, Martin; Harazim, Hana; Kosinová, Martina; Gregor, Jakub; Hůlek, Richard; Smékalová, Olga; Křikava, Ivo; Štoudek, Roman; Dušek, Ladislav

    2013-07-08

    Medical Faculties Network (MEFANET) has established itself as the authority for setting standards for medical educators in the Czech Republic and Slovakia, 2 independent countries with similar languages that once comprised a federation and that still retain the same curricular structure for medical education. One of the basic goals of the network is to advance medical teaching and learning with the use of modern information and communication technologies. We present the education portal AKUTNE.CZ as an important part of the MEFANET's content. Our focus is primarily on simulation-based tools for teaching and learning acute medicine issues. Three fundamental elements of the MEFANET e-publishing system are described: (1) medical disciplines linker, (2) authentication/authorization framework, and (3) multidimensional quality assessment. A new set of tools for technology-enhanced learning have been introduced recently: Sandbox (works in progress), WikiLectures (collaborative content authoring), Moodle-MEFANET (central learning management system), and Serious Games (virtual casuistics and interactive algorithms). The latest development in MEFANET is designed for indexing metadata about simulation-based learning objects, also known as electronic virtual patients or virtual clinical cases. The simulations assume the form of interactive algorithms for teaching and learning acute medicine. An anonymous questionnaire of 10 items was used to explore students' attitudes and interests in using the interactive algorithms as part of their medical or health care studies. Data collection was conducted over 10 days in February 2013. In total, 25 interactive algorithms in the Czech and English languages have been developed and published on the AKUTNE.CZ education portal to allow the users to test and improve their knowledge an