WorldWideScience

Sample records for biological network structure

  1. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  2. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  3. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Deyasi Krishanu; Upadhyay Shashankaditya; Banerjee Anirban

    2016-03-01

    Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of information in a network. Here we investigate the underlying undirected topology of different biological networks which support faster spreading of information and are better in communication. We analyse the good expansion property by using the spectral gap and communicability between nodes. Different epidemic models are also used to study the transmission of information in terms of spreading of disease through individuals (nodes)in those networks. Moreover, we explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks, in a significant way, is qualitatively different from the same in other biologicalnetworks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication.

  4. Study of the structure and dynamics of complex biological networks

    Science.gov (United States)

    Samal, Areejit

    2008-12-01

    In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large scale metabolic networks inside three organisms, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the dynamics of the large scale genetic network controlling E. coli metabolism. We have tried to explain the observed system level dynamical properties of these networks in terms of their underlying structure. Our studies of the system level dynamics of these large scale biological networks provide a different perspective on their functioning compared to that obtained from purely structural studies. Our study also leads to some new insights on features such as robustness, fragility and modularity of these large scale biological networks. We also shed light on how different networks inside the cell such as metabolic networks and genetic networks are interrelated to each other.

  5. The Structure and Function of Biological Networks

    Science.gov (United States)

    Wu, Daniel Duanqing

    2010-01-01

    Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding…

  6. Similarities Between Biological and Social Networks in Their Structural Organization

    Science.gov (United States)

    Kahng, Byungnam; Lee, Deokjae; Kim, Pureun

    A branching tree is a tree that is generated through a multiplicative branching process starting from a root. A critical branching tree is a branching tree in which the mean branching number of each node is 1, so that the number of offspring neither decays to zero nor flourishes as the branching process goes on. Moreover, a scale-free branching tree is a branching tree in which the number of offspring is heterogeneous, and its distribution follows a power law. Here we examine three structures, two from biology (a phylogenetic tree and the skeletons of a yeast protein interaction network) and one from social science (a coauthorship network), and find that all these structures are scale-free critical branching trees. This suggests that evolutionary processes in such systems take place in bursts and in a self-organized manner.

  7. Networks in Cell Biology

    Science.gov (United States)

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  8. Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

    Science.gov (United States)

    Koehler, Jacob; Rawlings, Chris; Verrier, Paul; Mitchell, Rowan; Skusa, Andre; Ruegg, Alexander; Philippi, Stephan

    2005-01-01

    The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

  9. Towards Systems Biology of Heterosis: A Hypothesis about Molecular Network Structure Applied for the Arabidopsis Metabolome

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    Gärtner Tanja

    2009-01-01

    Full Text Available We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity—a known phenomenon in the literature.

  10. The Google matrix controls the stability of structured ecological and biological networks

    Science.gov (United States)

    Stone, Lewi

    2016-09-01

    May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.

  11. Visual analysis of transcriptome data in the context of anatomical structures and biological networks

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    Astrid eJunker

    2012-11-01

    Full Text Available The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

  12. Dominating biological networks.

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    Tijana Milenković

    Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

  13. The structural molecular biology network of the State of São Paulo, Brazil

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    João A.R.G. Barbosa

    2006-06-01

    Full Text Available This article describes the achievements of the Structural Molecular Biology Network (SMolBNet, a collaborative program of structural molecular biology, centered in the State of São Paulo, Brazil, and supported by São Paulo State Funding Agency (FAPESP. It gathers twenty scientific groups and is coordinated by the scientific staff of the Center of Structural Molecular Biology, at the National Laboratory of Synchrotron Light (LNLS, in Campinas. The SMolBNet program has been aimed at 1 solving the structure of proteins of interest related to the research projects of the groups. In some cases, the choice has been to select proteins of unknown function or of possible novel structure obtained from the sequenced genomes of the FAPESP genomic program; 2 providing the groups with training in all the steps of the protein structure determination: gene cloning, protein expression, protein purification, protein crystallization and structure determination. Having begun in 2001, the program has been successful in both aims. Here, four groups reveal their participation in the program and describe the structural aspects of the proteins they have selected to study.Esse artigo descreve realizações do Programa SMolBNet (Rede de Biologia Molecular Estrutural do Estado de São Paulo, apoiado pela FAPESP (Fundação de Apoio à Pesquisa do Estado de São Paulo. Ele reúne vinte grupos de pesquisa e é coordenado pelos pesquisadores do Laboratório Nacional de Luz Síncrotron (LNLS, em Campinas. O Programa SMolBNet tem como metas: Elucidar a estrutura tridimensional de proteínas de interesse aos grupos de pesquisa componentes do Programa; Prover os grupos com treinamento em todas as etapas de determinação de estrutura: clonagem gênica, expressão de proteínas, purificação de proteínas, cristalização de proteínas e elucidação de suas estruturas. Tendo começado em 2001, o Programa alcançou sucesso em ambas as metas. Neste artigo, quatro dos grupos

  14. Structural Biology Fact Sheet

    Science.gov (United States)

    ... Home > Science Education > Structural Biology Fact Sheet Structural Biology Fact Sheet Tagline (Optional) Middle/Main Content Area What is structural biology? Structural biology is a field of science focused ...

  15. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

    Science.gov (United States)

    Martin, O. C.; Krzywicki, A.; Zagorski, M.

    2016-07-01

    Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent "motifs", that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.

  16. Network systems biology for targeted cancer therapies

    Institute of Scientific and Technical Information of China (English)

    Ting-Ting Zhou

    2012-01-01

    The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

  17. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  18. Comparing biological networks via graph compression

    Directory of Open Access Journals (Sweden)

    Hayashida Morihiro

    2010-09-01

    Full Text Available Abstract Background Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges. Results This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae, and B. subtilis, and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks. Conclusions Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.

  19. Efficient, sparse biological network determination

    Directory of Open Access Journals (Sweden)

    Papachristodoulou Antonis

    2009-02-01

    Full Text Available Abstract Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational and uncertainties in the data are taken into account. It also produces a network structure that can

  20. Logical impossibilities in biological networks

    Directory of Open Access Journals (Sweden)

    Monendra Grover

    2011-10-01

    Full Text Available Biological networks are complex and involve several kinds of molecules. For proper biological function it is important for these biomolecules to act at an individual level and act at the level of interaction of these molecules. In this paper some of the logical impossibilities that may arise in the biological networks and their possible solutions are discussed. It may be important to understand these paradoxes and their possible solutions in order to develop a holistic view of biological function.

  1. Querying Large Biological Network Datasets

    Science.gov (United States)

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  2. Understanding biological functions through molecular networks

    Institute of Scientific and Technical Information of China (English)

    Jing-Dong Jackie Han

    2008-01-01

    The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.

  3. Discovering large network motifs from a complex biological network

    Energy Technology Data Exchange (ETDEWEB)

    Terada, Aika; Sese, Jun, E-mail: terada@sel.is.ocha.ac.j, E-mail: sesejun@is.ocha.ac.j [Department of Computer Science, Ochanomizu University, 2-1-1 Ohtsuka, Bunkyo-ku, Tokyo 112-8610 (Japan)

    2009-12-01

    Graph structures representing relationships between entries have been studied in statistical analysis, and the results of these studies have been applied to biological networks, whose nodes and edges represent proteins and the relationships between them, respectively. Most of the studies have focused on only graph structures such as scale-free properties and cliques, but the relationships between nodes are also important features since most of the proteins perform their functions by connecting to other proteins. In order to determine such relationships, the problem of network motif discovery has been addressed; network motifs are frequently appearing graph structures in a given graph. However, the methods for network motif discovery are highly restrictive for the application to biological network because they can only be used to find small network motifs or they do not consider noise and uncertainty in observations. In this study, we introduce a new index to measure network motifs called AR index and develop a novel algorithm called ARIANA for finding large motifs even when the network has noise. Experiments using a synthetic network verify that our method can find better network motifs than an existing algorithm. By applying ARIANA to a real complex biological network, we find network motifs associated with regulations of start time of cell functions and generation of cell energies and discover that the cell cycle proteins can be categorized into two different groups.

  4. Correlation Effects in Biological Networks

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    A.A. Bagdasaryan

    2012-06-01

    Full Text Available Review of the complex network theory is presented and classification of such networks in accordance with the main statistical characteristics is considered. For the adjacency matrix of a real neural network the shortest distances for each pair of nodes as well as the node degree distribution and cluster coefficients are calculated. Comparison of the main statistical parameters with the random network is performed, and based on this, the conclusions about the correlation phenomena in biological system are made.

  5. PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.

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    Hung-Cuong Trinh

    Full Text Available It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.

  6. Hierarchical structure of biological systems

    Science.gov (United States)

    Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

    2014-01-01

    A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961

  7. Reconstructing Causal Biological Networks through Active Learning.

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    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  8. Network dynamics and systems biology

    Science.gov (United States)

    Norrell, Johannes A.

    The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the

  9. Exploration of biological network centralities with CentiBiN

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    Schreiber Falk

    2006-04-01

    Full Text Available Abstract Background The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability. Results CentiBiN (Centralities in Biological Networks is a tool for the computation and exploration of centralities in biological networks such as protein-protein interaction networks. It computes 17 different centralities for directed or undirected networks, ranging from local measures, that is, measures that only consider the direct neighbourhood of a network element, to global measures. CentiBiN supports the exploration of the centrality distribution by visualising central elements within the network and provides several layout mechanisms for the automatic generation of graphical representations of a network. It supports different input formats, especially for biological networks, and the export of the computed centralities to other tools. Conclusion CentiBiN helps systems biology researchers to identify crucial elements of biological networks. CentiBiN including a user guide and example data sets is available free of charge at http://centibin.ipk-gatersleben.de/. CentiBiN is available in two different versions: a Java Web Start application and an installable Windows application.

  10. Novel topological descriptors for analyzing biological networks

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    Varmuza Kurt K

    2010-06-01

    Full Text Available Abstract Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.

  11. Some physics problems in biological networks

    Science.gov (United States)

    Bialek, William

    2007-03-01

    Most of the interesting things that happen in living organisms require interactions among many components, and it is convenient to think of these as a ``network'' of interactions. We use this language at the level of single molecules (the network of interactions among amino acids that determine protein structure), single cells (the network of protein-DNA interactions responsible for the regulation of gene expression) and complex multicellular organisms (the networks of neurons in our brain). In this talk I'll try to look at two very different kinds of theoretical physics problems that arise in thinking about such networks. The first problems are phenomenological: Given what our experimentalists friends can measure, can we generate a global view of network function and dynamics? I'll argue that maximum entropy methods can be useful here, and show how such methods have been used in very recent work on networks of neurons, enzymes, genes and (in disguise) amino acids. In this line of reasoning there are of course interesting connections to statistical mechanics, and we'll see that natural statistical mechanics questions about the underlying models actually teach us something about how the real biological system works, in ways that will be tested through new experiments. In the second half of the talk I'll ask if there are principles from which we might actually be able to predict the structure and dynamics of biological networks. I'll focus on optimization principles, in particular the optimization of information flow in transcriptional regulation. Even setting up these arguments forces us to think critically about our understanding of the signals, specificity and noise in these systems, all current topics of research. Although we don't know if we have the right principles, trying to work out the consequences of such optimization again suggests new experiments.

  12. Programming and engineering biological networks.

    Science.gov (United States)

    Chin, Jason W

    2006-08-01

    Synthetic biology aims to build new functions in living organisms. Recent work has addressed the creation of synthetic epigenetic switches in mammalian cells and synthetic intracellular communication. Fundamentally new, and potentially scaleable, modes of gene regulation have been created that enable expansion of the scope of synthetic circuits. Increasingly sophisticated models of gene regulation that include stochastic effects are beginning to predict the behaviour of small synthetic networks. Overall, these advances suggest that a combination of molecular engineering and systems engineering should allow the creation of living matter capable of performing many useful and novel functions.

  13. Inferring Network Structure from Cascades

    CERN Document Server

    Ghonge, Sushrut

    2016-01-01

    Many physical, biological and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we solve the dynamics of general cascade processes. We then offer three topological inversion methods to infer the structure of any directed network given a set of cascade arrival times. Our forward and inverse formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for 5 different cascade models.

  14. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  15. Discovering Network Structure Beyond Communities

    CERN Document Server

    Nishikawa, Takashi; 10.1038/srep00151

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  16. Mapping biological systems to network systems

    CERN Document Server

    Rathore, Heena

    2016-01-01

    The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully ...

  17. BioFNet: biological functional network database for analysis and synthesis of biological systems.

    Science.gov (United States)

    Kurata, Hiroyuki; Maeda, Kazuhiro; Onaka, Toshikazu; Takata, Takenori

    2014-09-01

    In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.

  18. Attentional Networks and Biological Motion

    Directory of Open Access Journals (Sweden)

    Chandramouli Chandrasekaran

    2010-03-01

    Full Text Available Our ability to see meaningful actions when presented with pointlight traces of human movement is commonly referred to as the perception of biological motion. While traditionalexplanations have emphasized the spontaneous and automatic nature of this ability, morerecent findings suggest that attention may play a larger role than is typically assumed. Intwo studies we show that the speed and accuracy of responding to point-light stimuli is highly correlated with the ability to control selective attention. In our first experiment we measured thresholds for determining the walking direction of a masked point-light figure, and performance on a range of attention-related tasks in the same set of observers. Mask-density thresholds for the direction discrimination task varied quite considerably from observer to observer and this variation was highly correlated with performance on both Stroop and flanker interference tasks. Other components of attention, such as orienting, alerting and visual search efficiency, showed no such relationship. In a second experiment, we examined the relationship between the ability to determine the orientation of unmasked point-light actions and Stroop interference, again finding a strong correlation. Our results are consistent with previous research suggesting that biological motion processing may requite attention, and specifically implicate networks of attention related to executive control and selection.

  19. Measuring the evolutionary rewiring of biological networks.

    Directory of Open Access Journals (Sweden)

    Chong Shou

    Full Text Available We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

  20. Biologically inspired self-organizing networks

    Institute of Scientific and Technical Information of China (English)

    Naoki WAKAMIYA; Kenji LEIBNITZ; Masayuki MURATA

    2009-01-01

    Information networks are becoming more and more complex to accommodate a continuously increasing amount of traffic and networked devices, as well as having to cope with a growing diversity of operating environments and applications. Therefore, it is foreseeable that future information networks will frequently face unexpected problems, some of which could lead to the complete collapse of a network. To tackle this problem, recent attempts have been made to design novel network architectures which achieve a high level of scalability, adaptability, and robustness by taking inspiration from self-organizing biological systems. The objective of this paper is to discuss biologically inspired networking technologies.

  1. Inequalities in network structures

    NARCIS (Netherlands)

    Whitmeyer, Joseph M.; Wittek, Rafael

    2010-01-01

    We use a model of continuous attachments in networks to generate propositions concerning inequalities in network structures, and test the propositions on data from organizational settings. Our network model, inspired by that of [Gould, Roger 2002. The origins of status hierarchies: A formal theory a

  2. Folksodriven Structure Network

    CERN Document Server

    Mas, Massimiliano Dal

    2011-01-01

    Nowadays folksonomy is used as a system derived from user-generated electronic tags or keywords that annotate and describe online content. But it is not a classification system as an ontology. To consider it as a classification system it would be necessary to share a representation of contexts by all the users. This paper is proposing the use of folksonomies and network theory to devise a new concept: a "Folksodriven Structure Network" to represent folksonomies. This paper proposed and analyzed the network structure of Folksodriven tags thought as folsksonomy tags suggestions for the user on a dataset built on chosen websites. It is observed that the Folksodriven Network has relative low path lengths checking it with classic networking measures (clustering coefficient). Experiment result shows it can facilitate serendipitous discovery of content among users. Neat examples and clear formulas can show how a "Folksodriven Structure Network" can be used to tackle ontology mapping challenges.

  3. Multicore-periphery structure in networks

    CERN Document Server

    Yan, Bowen

    2016-01-01

    Many real-world networked systems exhibit a multicore-periphery structure, i.e., multiple cores, each of which contains densely connected elements, surrounded by sparsely connected elements that define the periphery. Identification of the multiple-periphery structure can provide a new handle on structures and functions of various complex networks, such as cognitive and biological networks, food webs, social networks, and communication and transportation networks. However, still no quantitative method exists to identify the multicore-periphery structure embedded in networks. Prior studies on core-periphery structure focused on either dichotomous or continuous division of a network into a single core and a periphery, whereas community detection algorithms did not discern the periphery from dense cohesive communities. Herein, we introduce a method to identify the optimal partition of a network into multiple dense cores and a loosely-connected periphery, and test the method on a well-known social network and the ...

  4. Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

    Science.gov (United States)

    Tkačik, Gašper

    2016-07-01

    The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to "randomized" ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

  5. Identifying communities from multiplex biological networks

    Directory of Open Access Journals (Sweden)

    Gilles Didier

    2015-12-01

    Full Text Available Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression. However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi.

  6. Structural Biology Guides Antibiotic Discovery

    Science.gov (United States)

    Polyak, Steven

    2014-01-01

    Modern drug discovery programs require the contribution of researchers in a number of specialist areas. One of these areas is structural biology. Using X-ray crystallography, the molecular basis of how a drug binds to its biological target and exerts its mode of action can be defined. For example, a drug that binds into the active site of an…

  7. Stability of biological networks as represented in Random Boolean Nets.

    Energy Technology Data Exchange (ETDEWEB)

    Slepoy, Alexander; Thompson, Marshall

    2004-09-01

    We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.

  8. Predicting biological networks from genomic data

    DEFF Research Database (Denmark)

    Harrington, Eoghan D; Jensen, Lars J; Bork, Peer

    2008-01-01

    Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks pro...... to high-throughput experimental methods....... provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate...

  9. Inference problems in structural biology

    DEFF Research Database (Denmark)

    Olsson, Simon

    The structure and dynamics of biological molecules are essential for their function. Consequently, a wealth of experimental techniques have been developed to study these features. However, while experiments yield detailed information about geometrical features of molecules, this information is of...

  10. Structural Analysis of Complex Networks

    CERN Document Server

    Dehmer, Matthias

    2011-01-01

    Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

  11. Evolving production network structures

    DEFF Research Database (Denmark)

    Grunow, Martin; Gunther, H.O.; Burdenik, H.

    2007-01-01

    When deciding about future production network configurations, the current structures have to be taken into account. Further, core issues such as the maturity of the products and the capacity requirements for test runs and ramp-ups must be incorporated. Our approach is based on optimization...... modelling and assigns products and capacity expansions to production sites under the above constraints. It also considers the production complexity at the individual sites and the flexibility of the network. Our implementation results for a large manufacturing network reveal substantial possible cost...

  12. Structure and inference in annotated networks

    Science.gov (United States)

    Newman, M. E. J.; Clauset, Aaron

    2016-06-01

    For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this `metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.

  13. Quantifying evolvability in small biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Nemenman, Ilya [Los Alamos National Laboratory; Mugler, Andrew [COLUMBIA UNIV; Ziv, Etay [COLUMBIA UNIV; Wiggins, Chris H [COLUMBIA UNIV

    2008-01-01

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.

  14. Network biology methods integrating biological data for translational science.

    Science.gov (United States)

    Bebek, Gurkan; Koyutürk, Mehmet; Price, Nathan D; Chance, Mark R

    2012-07-01

    The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

  15. Discriminative topological features reveal biological network mechanisms

    Directory of Open Access Journals (Sweden)

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  16. Systems biology of plant molecular networks: from networks to models

    NARCIS (Netherlands)

    Valentim, F.L.

    2015-01-01

    Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs

  17. Application of Graph Coloring to Biological Networks

    CERN Document Server

    Khor, Susan

    2009-01-01

    We explore the application of graph coloring to biological networks, specifically protein-protein interaction (PPI) networks. First, we find that given similar conditions (i.e. number of nodes, number of links, degree distribution and clustering), fewer colors are needed to color disassortative (high degree nodes tend to connect to low degree nodes and vice versa) than assortative networks. Fewer colors create fewer independent sets which in turn imply higher concurrency potential for a network. Since PPI networks tend to be disassortative, we suggest that in addition to functional specificity and stability proposed previously by Maslov and Sneppen (Science 296, 2002), the disassortative nature of PPI networks may promote the ability of cells to perform multiple, crucial and functionally diverse tasks concurrently. Second, since graph coloring is closely related to the presence of cliques in a graph, the significance of node coloring information to the problem of identifying protein complexes, i.e. dense subg...

  18. Biological and Environmental Research Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Balaji, V. [Princeton Univ., NJ (United States). Earth Science Grid Federation (ESGF); Boden, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cowley, Dave [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dart, Eli [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Dattoria, Vince [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Desai, Narayan [Argonne National Lab. (ANL), Argonne, IL (United States); Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Foster, Ian [Argonne National Lab. (ANL), Argonne, IL (United States); Goldstone, Robin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gregurick, Susan [U.S. Dept. of Energy, Washington, DC (United States). Biological Systems Science Division; Houghton, John [U.S. Dept. of Energy, Washington, DC (United States). Biological and Environmental Research (BER) Program; Izaurralde, Cesar [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnston, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Joseph, Renu [U.S. Dept. of Energy, Washington, DC (United States). Climate and Environmental Sciences Division; Kleese-van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lipton, Mary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Monga, Inder [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Pritchard, Matt [British Atmospheric Data Centre (BADC), Oxon (United Kingdom); Rotman, Lauren [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Strand, Gary [National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Stuart, Cory [Argonne National Lab. (ANL), Argonne, IL (United States); Tatusova, Tatiana [National Inst. of Health (NIH), Bethesda, MD (United States); Tierney, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Thomas, Brian [Univ. of California, Berkeley, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zurawski, Jason [Internet2, Washington, DC (United States)

    2013-09-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

  19. Rigidity and flexibility of biological networks

    CERN Document Server

    Gaspar, Merse E

    2012-01-01

    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.

  20. Complex network perspective on structure and function of Staphylococcus aureus metabolic network

    Indian Academy of Sciences (India)

    L Ying; D W Ding

    2013-02-01

    With remarkable advances in reconstruction of genome-scale metabolic networks, uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic network by complex network methods. We first generated a metabolite graph from the recently reconstructed high-quality S. aureus metabolic network model. Then, based on `bow tie' structure character, we explain and discuss the global structure of S. aureus metabolic network. The functional significance, global structural properties, modularity and centrality analysis of giant strong component in S. aureus metabolic networks are studied.

  1. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael;

    2017-01-01

    The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research...

  2. Linking network topology to function. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin, A. Krzywicki and M. Zagorski

    Science.gov (United States)

    di Bernardo, Diego

    2016-07-01

    The review by Martin et al. deals with a long standing problem at the interface of complex systems and molecular biology, that is the relationship between the topology of a complex network and its function. In biological terms the problem translates to relating the topology of gene regulatory networks (GRNs) to specific cellular functions. GRNs control the spatial and temporal activity of the genes encoded in the cell's genome by means of specialised proteins called Transcription Factors (TFs). A TF is able to recognise and bind specifically to a sequence (TF biding site) of variable length (order of magnitude of 10) found upstream of the sequence encoding one or more genes (at least in prokaryotes) and thus activating or repressing their transcription. TFs can thus be distinguished in activator and repressor. The picture can become more complex since some classes of TFs can form hetero-dimers consisting of a protein complex whose subunits are the individual TFs. Heterodimers can have completely different binding sites and activity compared to their individual parts. In this review the authors limit their attention to prokaryotes where the complexity of GRNs is somewhat reduced. Moreover they exploit a unique feature of living systems, i.e. evolution, to understand whether function can shape network topology. Indeed, prokaryotes such as bacteria are among the oldest living systems that have become perfectly adapted to their environment over geological scales and thus have reached an evolutionary steady-state where the fitness of the population has reached a plateau. By integrating in silico analysis and comparative evolution, the authors show that indeed function does tend to shape the structure of a GRN, however this trend is not always present and depends on the properties of the network being examined. Interestingly, the trend is more apparent for sparse networks, i.e. where the density of edges is very low. Sparsity is indeed one of the most prominent features

  3. Network biology concepts in complex disease comorbidities

    DEFF Research Database (Denmark)

    Hu, Jessica Xin; Thomas, Cecilia Engel; Brunak, Søren

    2016-01-01

    The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being...

  4. Theory of interface: category theory, directed networks and evolution of biological networks.

    Science.gov (United States)

    Haruna, Taichi

    2013-11-01

    Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.

  5. Review of biological network data and its applications.

    Science.gov (United States)

    Yu, Donghyeon; Kim, Minsoo; Xiao, Guanghua; Hwang, Tae Hyun

    2013-12-01

    Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

  6. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

    Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.

  7. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  8. Algorithmic and analytical methods in network biology.

    Science.gov (United States)

    Koyutürk, Mehmet

    2010-01-01

    During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

  9. Onion structure and network robustness

    CERN Document Server

    Wu, Zhi-Xi; 10.1103/PhysRevE.84.026106

    2011-01-01

    In a recent work [Proc. Natl. Acad. Sci. USA 108, 3838 (2011)], Schneider et al. proposed a new measure for network robustness and investigated optimal networks with respect to this quantity. For networks with a power-law degree distribution, the optimized networks have an onion structure-high-degree vertices forming a core with radially decreasing degrees and an over-representation of edges within the same radial layer. In this paper we relate the onion structure to graphs with good expander properties (another characterization of robust network) and argue that networks of skewed degree distributions with large spectral gaps (and thus good expander properties) are typically onion structured. Furthermore, we propose a generative algorithm producing synthetic scale-free networks with onion structure, circumventing the optimization procedure of Schneider et al. We validate the robustness of our generated networks against malicious attacks and random removals.

  10. Structural Biology: Practical NMR Applications

    CERN Document Server

    Teng, Quincy

    2005-01-01

    This textbook begins with an overview of NMR development and applications in biological systems. It describes recent developments in instrument hardware and methodology. Chapters highlight the scope and limitation of NMR methods. While detailed math and quantum mechanics dealing with NMR theory have been addressed in several well-known NMR volumes, chapter two of this volume illustrates the fundamental principles and concepts of NMR spectroscopy in a more descriptive manner. Topics such as instrument setup, data acquisition, and data processing using a variety of offline software are discussed. Chapters further discuss several routine stategies for preparing samples, especially for macromolecules and complexes. The target market for such a volume includes researchers in the field of biochemistry, chemistry, structural biology and biophysics.

  11. Hierarchical structure of biological systems: A bioengineering approach

    OpenAIRE

    Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M.

    2013-01-01

    A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and function...

  12. Distance, dissimilarity index, and network community structure

    CERN Document Server

    Zhou, H

    2003-01-01

    We address the question of finding the community structure of a complex network. In an earlier effort [H. Zhou, {\\em Phys. Rev. E} (2003)], the concept of network random walking is introduced and a distance measure defined. Here we calculate, based on this distance measure, the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized. Each community is characterized by an upper and a lower dissimilarity threshold. The algorithm is applied to several artificial and real-world networks, and excellent results are obtained. In the case of artificially generated random modular networks, this method outperforms the algorithm based on the concept of edge betweenness centrality. For yeast's protein-protein interaction network, we are able to identify many clusters that have well defined biological functions.

  13. Patchworking Network Structures

    DEFF Research Database (Denmark)

    Norus, Jesper

    2004-01-01

    In recent years, establishing successful collaborative arrangements and relationshipsbetween university, industry and public institutions has come to be seen as essential intransforming new scientific knowledge into new innovations and business ventures. Thefit between these very different actor...... analyzes fourdifferent managerial strategies of how to create network structures to deal with theinterfaces between industry, university and public institutions. The research-orientedstrategy, the incubator strategy, the industrial-partnering strategy, and the policyorientedstrategy. The research......-oriented strategy focuses narrowly on howbiotechnology firms transform scientific results into solid business plan or businessmodels revealing the aim of the technologies, services or products. The incubatorstrategy is concerned with localization and how to overcome specific types ofmanagerial problems...

  14. Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions

    Science.gov (United States)

    Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.

    2016-09-01

    Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving

  15. Metagovernance, network structure, and legitimacy

    DEFF Research Database (Denmark)

    Daugbjerg, Carsten; Fawcett, Paul

    2016-01-01

    This article develops a heuristic for comparative governance analysis. The heuristic depicts four network types by combining network structure with the state’s capacity to metagovern. It suggests that each network type produces a particular combination of input and output legitimacy. We illustrat...

  16. Track structure in biological models.

    Science.gov (United States)

    Curtis, S B

    1986-01-01

    High-energy heavy ions in the galactic cosmic radiation (HZE particles) may pose a special risk during long term manned space flights outside the sheltering confines of the earth's geomagnetic field. These particles are highly ionizing, and they and their nuclear secondaries can penetrate many centimeters of body tissue. The three dimensional patterns of ionizations they create as they lose energy are referred to as their track structure. Several models of biological action on mammalian cells attempt to treat track structure or related quantities in their formulation. The methods by which they do this are reviewed. The proximity function is introduced in connection with the theory of Dual Radiation Action (DRA). The ion-gamma kill (IGK) model introduces the radial energy-density distribution, which is a smooth function characterizing both the magnitude and extension of a charged particle track. The lethal, potentially lethal (LPL) model introduces lambda, the mean distance between relevant ion clusters or biochemical species along the track. Since very localized energy depositions (within approximately 10 nm) are emphasized, the proximity function as defined in the DRA model is not of utility in characterizing track structure in the LPL formulation.

  17. Hierarchical community structure in complex (social) networks

    CERN Document Server

    Massaro, Emanuele

    2014-01-01

    The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \\cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of model's parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.

  18. Relevance of Dynamic Clustering to Biological Networks

    CERN Document Server

    Kaneko, K

    1993-01-01

    Abstract Network of nonlinear dynamical elements often show clustering of synchronization by chaotic instability. Relevance of the clustering to ecological, immune, neural, and cellular networks is discussed, with the emphasis of partially ordered states with chaotic itinerancy. First, clustering with bit structures in a hypercubic lattice is studied. Spontaneous formation and destruction of relevant bits are found, which give self-organizing, and chaotic genetic algorithms. When spontaneous changes of effective couplings are introduced, chaotic itinerancy of clusterings is widely seen through a feedback mechanism, which supports dynamic stability allowing for complexity and diversity, known as homeochaos. Second, synaptic dynamics of couplings is studied in relation with neural dynamics. The clustering structure is formed with a balance between external inputs and internal dynamics. Last, an extension allowing for the growth of the number of elements is given, in connection with cell differentiation. Effecti...

  19. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard;

    2012-01-01

    a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure......Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  20. Advanced Polymer Network Structures

    Science.gov (United States)

    2016-02-01

    Std. Z39.18 Approved for public release; distribution is unlimited. iii Contents List of Figures iv List of Tables v 1. Introduction 1 2...unlimited. v black and blue lines correspond to the single network composed of the first (system 10) and second networks (system 11), respectively...aggregation also contributes significantly to the tensile behavior, where the H- and comb - polymers with long spikes have a considerably higher

  1. Reconstruction of biological networks based on life science data integration

    OpenAIRE

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-01-01

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and V...

  2. Controllability of structural brain networks.

    Science.gov (United States)

    Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K; Yu, Alfred B; Kahn, Ari E; Medaglia, John D; Vettel, Jean M; Miller, Michael B; Grafton, Scott T; Bassett, Danielle S

    2015-10-01

    Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

  3. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Adilson E Motter

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  4. Collective network for computer structures

    Energy Technology Data Exchange (ETDEWEB)

    Blumrich, Matthias A. (Ridgefield, CT); Coteus, Paul W. (Yorktown Heights, NY); Chen, Dong (Croton On Hudson, NY); Gara, Alan (Mount Kisco, NY); Giampapa, Mark E. (Irvington, NY); Heidelberger, Philip (Cortlandt Manor, NY); Hoenicke, Dirk (Ossining, NY); Takken, Todd E. (Brewster, NY); Steinmacher-Burow, Burkhard D. (Wernau, DE); Vranas, Pavlos M. (Bedford Hills, NY)

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  5. Topological implications of negative curvature for biological and social networks

    Science.gov (United States)

    Albert, Réka; DasGupta, Bhaskar; Mobasheri, Nasim

    2014-03-01

    Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.

  6. PREFACE: Complex Networks: from Biology to Information Technology

    Science.gov (United States)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  7. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  8. Identifying common components across biological network graphs using a bipartite data model.

    Science.gov (United States)

    Baker, Ej; Culpepper, C; Philips, C; Bubier, J; Langston, M; Chesler, Ej

    2014-01-01

    The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

  9. Application of random matrix theory to biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov

    2006-09-25

    We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.

  10. Collective network for computer structures

    Science.gov (United States)

    Blumrich, Matthias A; Coteus, Paul W; Chen, Dong; Gara, Alan; Giampapa, Mark E; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd E; Steinmacher-Burow, Burkhard D; Vranas, Pavlos M

    2014-01-07

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to the needs of a processing algorithm.

  11. OWL reasoning framework over big biological knowledge network.

    Science.gov (United States)

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

  12. OWL Reasoning Framework over Big Biological Knowledge Network

    Directory of Open Access Journals (Sweden)

    Huajun Chen

    2014-01-01

    Full Text Available Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM and western medicine (WM is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

  13. Confidence sets for network structure

    CERN Document Server

    Airoldi, Edoardo M; Wolfe, Patrick J

    2011-01-01

    Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific...

  14. A generic algorithm for layout of biological networks

    Directory of Open Access Journals (Sweden)

    Dwyer Tim

    2009-11-01

    Full Text Available Abstract Background Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. Results We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. Conclusion The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.

  15. Structural and dynamical properties of complex networks

    Science.gov (United States)

    Ghoshal, Gourab

    Recent years have witnessed a substantial amount of interest within the physics community in the properties of networks. Techniques from statistical physics coupled with the widespread availability of computing resources have facilitated studies ranging from large scale empirical analysis of the worldwide web, social networks, biological systems, to the development of theoretical models and tools to explore the various properties of these systems. Following these developments, in this dissertation, we present and solve for a diverse set of new problems, investigating the structural and dynamical properties of both model and real world networks. We start by defining a new metric to measure the stability of network structure to disruptions, and then using a combination of theory and simulation study its properties in detail on artificially generated networks; we then compare our results to a selection of networks from the real world and find good agreement in most cases. In the following chapter, we propose a mathematical model that mimics the structure of popular file-sharing websites such as Flickr and CiteULike and demonstrate that many of its properties can solved exactly in the limit of large network size. The remaining part of the dissertation primarily focuses on the dynamical properties of networks. We first formulate a model of a network that evolves under the addition and deletion of vertices and edges, and solve for the equilibrium degree distribution for a variety of cases of interest. We then consider networks whose structure can be manipulated by adjusting the rules by which vertices enter and leave the network. We focus in particular on degree distributions and show that, with some mild constraints, it is possible by a suitable choice of rules to arrange for the network to have any degree distribution we desire. In addition we define a simple local algorithm by which appropriate rules can be implemented in practice. Finally, we conclude our

  16. Systems biology in the context of big data and networks.

    Science.gov (United States)

    Altaf-Ul-Amin, Md; Afendi, Farit Mochamad; Kiboi, Samuel Kuria; Kanaya, Shigehiko

    2014-01-01

    Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

  17. Biological networks to the analysis of microarray data

    Institute of Scientific and Technical Information of China (English)

    FANG Zhuo; LUO Qingming; ZHANG Guoqing; LI Yixue

    2006-01-01

    Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations.Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods.

  18. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

    Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

  19. Bayesian networks: a powerful tool for systems biology study

    Institute of Scientific and Technical Information of China (English)

    Xiu-Jie WANG

    2010-01-01

    @@ Higher Education Press and Springer-Verlag Berlin Heidelberg 2010The wide application of omics research approaches caused a burst of biological data in the past decade, and also promoted the growth of systems biology, a research field that studies biological questions from a genome-wide point of view. One feature of systems biology study is to integrate and identify. Not only experiments are carried out at whole-genome scales, but also data from various resources, such as genomics, transcriptomics, proteomics,and metabolics data, need to be integrated to identify correlations among targeted entities. Therefore, plenty amounts of experimental data, robust statistical methods, and reliable network construction models are indispensable for systems biology study. Among the available network construction models, Bayesian network is considered as one of the most effective methods available so far for biological network predictions (Pe'er, 2005).

  20. Power Laws, Scale-Free Networks and Genome Biology

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

    Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

  1. Tensegrity II. How structural networks influence cellular information processing networks

    Science.gov (United States)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  2. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

    Science.gov (United States)

    Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

    2015-01-01

    With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

  3. Quantification of network structural dissimilarities

    Science.gov (United States)

    Schieber, Tiago A.; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M.; Masoller, Cristina; Ravetti, Martín G.

    2017-01-01

    Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

  4. Structurally Dynamic Spin Market Networks

    Science.gov (United States)

    Horváth, Denis; Kuscsik, Zoltán

    The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

  5. [Classification of organisms and structuralism in biology].

    Science.gov (United States)

    Vasil'eva, L I

    2001-01-01

    Structuralism in biology is the oldest trend oriented to the search for natural "laws of forms" comparable with laws of growth of crystal, was revived at the end of 20th century on the basis of structuralist thought in socio-humanitarian sciences. The development of principal ideas of the linguistic structuralism in some aspects is similar to that of biological systematics, especially concerning the relationships between "system" and "evolution". However, apart from this general similarity, biological structuralism is strongly focused on familiar problems of the origin of diversity in nature. In their striving for the renovation of existing views, biological structuralists oppose the neo-darwinism emphasizing the existence of "law of forms", that are independent on heredity and genetic "determinism". The trend to develop so-called "rational taxonomy" is also characteristic of biological structuralism but this attempt failed being connected neither with Darwin's historicism nor with Plato's typology.

  6. Duplication: a Mechanism Producing Disassortative Mixing Networks in Biology

    Institute of Scientific and Technical Information of China (English)

    ZHAO Dan; LIU Zeng-Rong; WANG Jia-Zeng

    2007-01-01

    Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks (PPI), gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks.Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.

  7. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

    Gautier, Laurent; Taboureau, Olivier; Audouze, Karine Marie Laure

    2013-01-01

    it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity. Areas covered: This review describes the strengths and limitations......Introduction: The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining...... of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity. Expert opinion: There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network...

  8. Structural prediction of dynamic Bayesian network with partial prior information.

    Science.gov (United States)

    Maiti, Aniruddha; Reddy, Ramakanth; Mukherjee, Anirban

    2015-01-01

    The prediction of the structure of a hidden dynamic Bayesian network (DBN) from a noisy dataset is an important and challenging task. This work presents a generalized framework to infer the DBN network structure with partial prior information. In the proposed framework, the partial information about the network structure is provided in the form of prior. The proposed method makes use of the prior information regarding the presence and as well as absence of some of the edges. Using the noisy dataset and partial prior information, this method is able to infer nearly accurate structure of the network. The proposed method is validated using simulated datasets. In addition, two real biological datasets are used to infer hidden biological interaction networks.

  9. Notes on a PDE system for biological network formation

    KAUST Repository

    Haskovec, Jan

    2016-01-22

    We present new analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transport networks. The model describes the pressure field using a Darcy’s type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. The analytical part extends the results of Haskovec et al. (2015) regarding the existence of weak and mild solutions to the whole range of meaningful relaxation exponents. Moreover, we prove finite time extinction or break-down of solutions in the spatially one-dimensional setting for certain ranges of the relaxation exponent. We also construct stationary solutions for the case of vanishing diffusion and critical value of the relaxation exponent, using a variational formulation and a penalty method. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on mixed finite elements and study the qualitative properties of network structures for various parameter values. Furthermore, we indicate numerically that some analytical results proved for the spatially one-dimensional setting are likely to be valid also in several space dimensions.

  10. Dissipative Structures and Biological Evolution

    Science.gov (United States)

    Brändas, E. J.

    This contribution celebrates Prof. Manuel G. Velarde's exceptional accomplishments in the science of Complex Systems and Non-Linear Dynamics in connection with his seventieth anniversary and subsequent retirement from the Instituto Pluridisciplinar de la Universidad Complutense de Madrid (UCM). In this commemoration I will discuss my provisional yet unique association with Manuel over the years. Although viewing scientific problems from differing ends of the micro-macro spectrum I believe our relationship has been mutually beneficial. In this account I will, in a few words, describe recent developments in our program to attack complicated issues on the borderline between physics and biology. Concrete overlapping interests incorporate problems related to strongly correlated systems like superconductivity as well as currently disputed issues pertaining to biological concepts and the unity of physical theory. In summary we will give examples of such wide-ranging proposals comprising potential avenues providing feasible platforms for the future.

  11. Comparing the biological coherence of network clusters identified by different detection algorithms

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighborhoods based on network topology, many network cluster identification algorithms have been developed. However, each algorithm might dissect a network from a different aspect and may provide different insight on the network partition. In order to objectively evaluate the performance of four commonly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction networks with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.

  12. SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

    Science.gov (United States)

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

  13. Controllability and observability of Boolean networks arising from biology.

    Science.gov (United States)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  14. Network benchmarking: a happy marriage between systems and synthetic biology.

    Science.gov (United States)

    Minty, Jeremy J; Varedi K, S Marjan; Nina Lin, Xiaoxia

    2009-03-27

    In their new Cell paper, Cantone et al. (2009) present exciting results on constructing and utilizing a small synthetic gene regulatory network in yeast that draws from two rapidly developing fields of systems and synthetic biology.

  15. From network structure to network reorganization: implications for adult neurogenesis

    Science.gov (United States)

    Schneider-Mizell, Casey M.; Parent, Jack M.; Ben-Jacob, Eshel; Zochowski, Michal R.; Sander, Leonard M.

    2010-12-01

    Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

  16. Toward Network Biology in E. coli Cell.

    Science.gov (United States)

    Mori, Hirotada; Takeuchi, Rikiya; Otsuka, Yuta; Bowden, Steven; Yokoyama, Katsushi; Muto, Ai; Libourel, Igor; Wanner, Barry L

    2015-01-01

    E. coli has been a critically important model research organism for more than 50 years, particularly in molecular biology. In 1997, the E. coli draft genome sequence was published. Post-genomic techniques and resources were then developed that allowed E. coli to become a model organism for systems biology. Progress made since publication of the E. coli genome sequence will be summarized.

  17. Epigenetics and Why Biological Networks are More Controllable than Expected

    Science.gov (United States)

    Motter, Adilson

    2013-03-01

    A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.

  18. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    Science.gov (United States)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

  19. Structural Transitions in Dense Networks

    CERN Document Server

    Lambiotte, R; Bhat, U; Redner, S

    2016-01-01

    We introduce an evolving network model in which a new node attaches to a randomly selected target node and also to each of its neighbors with probability $p$. The resulting network is sparse for $p<\\frac{1}{2}$ and dense (average degree increasing with number of nodes $N$) for $p\\geq \\frac{1}{2}$. In the dense regime, individual networks realizations built by this copying mechanism are disparate and not self-averaging. Further, there is an infinite sequence of structural anomalies at $p=\\frac{2}{3}$, $\\frac{3}{4}$, $\\frac{4}{5}$, etc., where the dependences on $N$ of the number of triangles (3-cliques), 4-cliques, undergo phase transitions. When linking to second neighbors of the target can occur, the probability that the resulting graph is complete---where all nodes are connected---is non-zero as $N\\to\\infty$.

  20. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    OpenAIRE

    Jim Harkin; Fearghal Morgan; Liam McDaid; Steve Hall; Brian McGinley; Seamus Cawley

    2009-01-01

    FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable...

  1. Structural Biology for A-Level Students

    Science.gov (United States)

    Philip, Judith

    2013-01-01

    The relationship between the structure and function of proteins is an important area in biochemistry. Pupils studying A-level Biology are introduced to the four levels of protein structure (primary, secondary, tertiary and quaternary) and how these can be used to describe the progressive folding of a chain of amino acid residues to a final,…

  2. Visualizing ensembles in structural biology.

    Science.gov (United States)

    Melvin, Ryan L; Salsbury, Freddie R

    2016-06-01

    Displaying a single representative conformation of a biopolymer rather than an ensemble of states mistakenly conveys a static nature rather than the actual dynamic personality of biopolymers. However, there are few apparent options due to the fixed nature of print media. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of ensembles of states with little loss of the visual simplicity of displaying a single representative conformation. Of particular note is that the visualization method employed clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We provide several examples in the context of nucleic acids and proteins with particular insights gained via this method. Such examples include investigating a therapeutic polymer of FdUMP (5-fluoro-2-deoxyuridine-5-O-monophosphate) - a topoisomerase-1 (Top1), apoptosis-inducing poison - and nucleotide-binding proteins responsible for ATP hydrolysis from Bacillus subtilis. We also discuss how these methods can be extended to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures.

  3. The complex channel networks of bone structure

    CERN Document Server

    Costa, Luciano da Fontoura; Beletti, Marcelo E

    2006-01-01

    Bone structure in mammals involves a complex network of channels (Havers and Volkmann channels) required to nourish the bone marrow cells. This work describes how three-dimensional reconstructions of such systems can be obtained and represented in terms of complex networks. Three important findings are reported: (i) the fact that the channel branching density resembles a power law implies the existence of distribution hubs; (ii) the conditional node degree density indicates a clear tendency of connection between nodes with degrees 2 and 4; and (iii) the application of the recently introduced concept of hierarchical clustering coefficient allows the identification of typical scales of channel redistribution. A series of important biological insights is drawn and discussed

  4. Walk modularity and community structure in networks

    CERN Document Server

    Mehrle, David; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the difference between the actual and expected number of walks within clusters, which we call walk-modularity. Walk-modularity can be expressed in matrix form, and community detection can be performed by finding leading eigenvectors of the walk-modularity matrix. We demonstrate community detection on both synthetic and real-world networks and find that walk-modularity maximization returns significantly improved results compared to traditional modularity maximization.

  5. Quantum Processes and Dynamic Networks in Physical and Biological Systems.

    Science.gov (United States)

    Dudziak, Martin Joseph

    Quantum theory since its earliest formulations in the Copenhagen Interpretation has been difficult to integrate with general relativity and with classical Newtonian physics. There has been traditionally a regard for quantum phenomena as being a limiting case for a natural order that is fundamentally classical except for microscopic extrema where quantum mechanics must be applied, more as a mathematical reconciliation rather than as a description and explanation. Macroscopic sciences including the study of biological neural networks, cellular energy transports and the broad field of non-linear and chaotic systems point to a quantum dimension extending across all scales of measurement and encompassing all of Nature as a fundamentally quantum universe. Theory and observation lead to a number of hypotheses all of which point to dynamic, evolving networks of fundamental or elementary processes as the underlying logico-physical structure (manifestation) in Nature and a strongly quantized dimension to macroscalar processes such as are found in biological, ecological and social systems. The fundamental thesis advanced and presented herein is that quantum phenomena may be the direct consequence of a universe built not from objects and substance but from interacting, interdependent processes collectively operating as sets and networks, giving rise to systems that on microcosmic or macroscopic scales function wholistically and organically, exhibiting non-locality and other non -classical phenomena. The argument is made that such effects as non-locality are not aberrations or departures from the norm but ordinary consequences of the process-network dynamics of Nature. Quantum processes are taken to be the fundamental action-events within Nature; rather than being the exception quantum theory is the rule. The argument is also presented that the study of quantum physics could benefit from the study of selective higher-scale complex systems, such as neural processes in the brain

  6. Generalized Double Ring Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Patel, A.; Knudsen, Thomas Phillip;

    2004-01-01

    ) structures. Results on the Generalized Petersen Graphs provide necessary and sufficient conditions for a N2R(p; q) structure to be node or edge symmetric, and a table free routing scheme always determining a shortest path between any pair of nodes is presented. Next, the performance in terms of average...... of these structures are shown, indicating that they forma suitable base for future access network infrastructures. The first result is that every N2R(p; q; r) structure is isomorphic to a N2R(p; q) structure N2R(p; q'), and it is shown how q' is determined. Consequently, the rest of the paper focuses on the N2R(p; q...

  7. Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.

    Science.gov (United States)

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.

  8. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  9. 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

  10. Two classes of bipartite networks: nested biological and social systems.

    Science.gov (United States)

    Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

    2008-10-01

    Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

  11. Human diseases through the lens of network biology.

    Science.gov (United States)

    Furlong, Laura I

    2013-03-01

    One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.

  12. Computing the structural influence matrix for biological systems.

    Science.gov (United States)

    Giordano, Giulia; Cuba Samaniego, Christian; Franco, Elisa; Blanchini, Franco

    2016-06-01

    We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.

  13. Structured population models in biology and epidemiology

    CERN Document Server

    Ruan, Shigui

    2008-01-01

    This book consists of six chapters written by leading researchers in mathematical biology. These chapters present recent and important developments in the study of structured population models in biology and epidemiology. Topics include population models structured by age, size, and spatial position; size-structured models for metapopulations, macroparasitc diseases, and prion proliferation; models for transmission of microparasites between host populations living on non-coincident spatial domains; spatiotemporal patterns of disease spread; method of aggregation of variables in population dynamics; and biofilm models. It is suitable as a textbook for a mathematical biology course or a summer school at the advanced undergraduate and graduate level. It can also serve as a reference book for researchers looking for either interesting and specific problems to work on or useful techniques and discussions of some particular problems.

  14. Structure and function in flow networks

    CERN Document Server

    Rubido, Nicolás; Baptista, Murilo S

    2013-01-01

    This Letter presents a unified approach for the fundamental relationship between structure and function in flow networks by solving analytically the voltages in a resistor network, transforming the network structure to an effective all-to-all topology, and then measuring the resultant flows. Moreover, it defines a way to study the structural resilience of the graph and to detect possible communities.

  15. Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

    Institute of Scientific and Technical Information of China (English)

    Gui-xia Liu; Wei Feng; Han Wang; Lei Liu; Chun-guang Zhou

    2009-01-01

    In the post-genomic biology era, the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system, and it has been a challenging task in bioinformatics. The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages, but how to determine the network structure and parameters is still important to be explored. This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network .The new algorithm is evaluated with the use of both simulated and yeast cell cycle data. The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

  16. Communities, modules and large-scale structure in networks

    Science.gov (United States)

    Newman, M. E. J.

    2012-01-01

    Networks, also called graphs by mathematicians, provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and the state spaces of physical systems. In the past decade there have been significant advances in experiments to determine the topological structure of networked systems, but there remain substantial challenges in extracting scientific understanding from the large quantities of data produced by the experiments. A variety of basic measures and metrics are available that can tell us about small-scale structure in networks, such as correlations, connections and recurrent patterns, but it is considerably more difficult to quantify structure on medium and large scales, to understand the `big picture'. Important progress has been made, however, within the past few years, a selection of which is reviewed here.

  17. Computation of the effective mechanical response of biological networks accounting for large configuration changes.

    Science.gov (United States)

    El Nady, K; Ganghoffer, J F

    2016-05-01

    The asymptotic homogenization technique is involved to derive the effective elastic response of biological membranes viewed as repetitive beam networks. Thereby, a systematic methodology is established, allowing the prediction of the overall mechanical properties of biological membranes in the nonlinear regime, reflecting the influence of the geometrical and mechanical micro-parameters of the network structure on the overall response of the equivalent continuum. Biomembranes networks are classified based on nodal connectivity, so that we analyze in this work 3, 4 and 6-connectivity networks, which are representative of most biological networks. The individual filaments of the network are described as undulated beams prone to entropic elasticity, with tensile moduli determined from their persistence length. The effective micropolar continuum evaluated as a continuum substitute of the biological network has a kinematics reflecting the discrete network deformation modes, involving a nodal displacement and a microrotation. The statics involves the classical Cauchy stress and internal moments encapsulated into couple stresses, which develop internal work in duality to microcurvatures reflecting local network undulations. The relative ratio of the characteristic bending length of the effective micropolar continuum to the unit cell size determines the relevant choice of the equivalent medium. In most cases, the Cauchy continuum is sufficient to model biomembranes. The peptidoglycan network may exhibit a re-entrant hexagonal configuration due to thermal or pressure fluctuations, for which micropolar effects become important. The homogenized responses are in good agreement with FE simulations performed over the whole network. The predictive nature of the employed homogenization technique allows the identification of a strain energy density of a hyperelastic model, for the purpose of performing structural calculations of the shape evolutions of biomembranes.

  18. Optimizing Dynamical Network Structure for Pinning Control

    Science.gov (United States)

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-04-01

    Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

  19. Distributed Structure-Searchable Toxicity Database Network

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable,...

  20. Tensegrity I. Cell structure and hierarchical systems biology

    Science.gov (United States)

    Ingber, Donald E.

    2003-01-01

    In 1993, a Commentary in this journal described how a simple mechanical model of cell structure based on tensegrity architecture can help to explain how cell shape, movement and cytoskeletal mechanics are controlled, as well as how cells sense and respond to mechanical forces (J. Cell Sci. 104, 613-627). The cellular tensegrity model can now be revisited and placed in context of new advances in our understanding of cell structure, biological networks and mechanoregulation that have been made over the past decade. Recent work provides strong evidence to support the use of tensegrity by cells, and mathematical formulations of the model predict many aspects of cell behavior. In addition, development of the tensegrity theory and its translation into mathematical terms are beginning to allow us to define the relationship between mechanics and biochemistry at the molecular level and to attack the larger problem of biological complexity. Part I of this two-part article covers the evidence for cellular tensegrity at the molecular level and describes how this building system may provide a structural basis for the hierarchical organization of living systems--from molecule to organism. Part II, which focuses on how these structural networks influence information processing networks, appears in the next issue.

  1. Using biological networks to improve our understanding of infectious diseases

    Directory of Open Access Journals (Sweden)

    Nicola J. Mulder

    2014-08-01

    Full Text Available Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.

  2. Protein Structure Network-based Drug Design.

    Science.gov (United States)

    Liang, Zhongjie; Hu, Guang

    2016-01-01

    Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

  3. Biological impacts and context of network theory

    Energy Technology Data Exchange (ETDEWEB)

    Almaas, E

    2007-01-05

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  4. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    Science.gov (United States)

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

  5. Spectral algorithms for heterogeneous biological networks.

    Science.gov (United States)

    McDonald, Martin; Higham, Desmond J; Vass, J Keith

    2012-11-01

    Spectral methods, which use information relating to eigenvectors, singular vectors and generalized singular vectors, help us to visualize and summarize sets of pairwise interactions. In this work, we motivate and discuss the use of spectral methods by taking a matrix computation view and applying concepts from applied linear algebra. We show that this unified approach is sufficiently flexible to allow multiple sources of network information to be combined. We illustrate the methods on microarray data arising from a large population-based study in human adipose tissue, combined with related information concerning metabolic pathways.

  6. Oscillatory Activities in Regulatory Biological Networks and Hopf Bifurcation

    Institute of Scientific and Technical Information of China (English)

    YAN Shi-Wei; WANG Qi; XIE Bai-Song; ZHANG Feng-Shou

    2007-01-01

    Exploiting the nonlinear dynamics in the negative feedback loop, we propose a statistical signal-response model to describe the different oscillatory behaviour in a biological network motif. By choosing the delay as a bifurcation parameter, we discuss the existence of Hopf bifurcation and the stability of the periodic solutions of model equations with the centre manifold theorem and the normal form theory. It is shown that a periodic solution is born in a Hopf bifurcation beyond a critical time delay, and thus the bifurcation phenomenon may be important to elucidate the mechanism of oscillatory activities in regulatory biological networks.

  7. MAPPING OF NATURAL KAPOSI SARCOMA INHIBITOR USING NETWORK BIOLOGY APPROACH

    Directory of Open Access Journals (Sweden)

    Jayadeepa R. M.

    2012-03-01

    Full Text Available Identification of protein-ligand interaction networks on a proteome scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics. In this study we have developed a novel computational strategy to identify ligand binding profiles of proteins across gene families and applied it to predicting protein functions, elucidating molecular mechanisms of drug adverse effects, and repositioning safe pharmaceuticals to treat different diseases The resultant network is then extrapolated to proteomics level to sort out the genes only expressed in the specific cancer types. The network is statistically analyzed and represented by the graphical interpretation to encounter the hub nodes. The objective of developing a biological networking is for the evaluation and validation of cancer drugs and their targets. In the field of cancer biology, the drug and their targets holds a role of paramount importance. With the work conducted here it shows the study of relation between drug target networks. Kaposi’s sarcoma (KS is a systemic disease which can present with cutaneous lesions with or without internal involvement. Genes belonging to the group of proto-oncogenes and tumor suppressors are best targeted for cancer studies. Biological networks like gene regulatory networks, protein interaction network is usually created to simplify the studies. In the present study, 26 proteins as receptor were selected for the study; all the receptors were responsible for the cause of Kaposi’s sarcoma. Also, 121 natural anti-Kaposi Sarcoma compounds were selected from different sources the natural components were the best component for blocking of abnormal activity.

  8. Network structure exploration in networks with node attributes

    Science.gov (United States)

    Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

    2016-05-01

    Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

  9. Structural Holes in Directed Fuzzy Social Networks

    OpenAIRE

    Renjie Hu; Guangyu Zhang

    2014-01-01

    The structural holes have been a key issue in fuzzy social network analysis. For undirected fuzzy social networks where edges are just present or absent undirected fuzzy relation and have no more information attached, many structural holes measures have been presented, such as key fuzzy structural holes, general fuzzy structural holes, strong fuzzy structural holes, and weak fuzzy structural holes. There has been a growing need to design structural holes measures for directed fuzzy social net...

  10. Sensitive dependence of network dynamics on network structure

    CERN Document Server

    Nishikawa, Takashi; Motter, Adilson E

    2016-01-01

    The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in act...

  11. True Nature of Supply Network Communication Structure

    Directory of Open Access Journals (Sweden)

    Lokhman Hakim bin Osman

    2016-04-01

    Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.

  12. Control of Boolean networks: hardness results and algorithms for tree structured networks.

    Science.gov (United States)

    Akutsu, Tatsuya; Hayashida, Morihiro; Ching, Wai-Ki; Ng, Michael K

    2007-02-21

    Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.

  13. Syntactic structures in languages and biology.

    Science.gov (United States)

    Horn, David

    2008-08-01

    Both natural languages and cell biology make use of one-dimensional encryption. Their investigation calls for syntactic deciphering of the text and semantic understanding of the resulting structures. Here we discuss recently published algorithms that allow for such searches: automatic distillation of structure (ADIOS) that is successful in discovering syntactic structures in linguistic texts and its motif extraction (MEX) component that can be used for uncovering motifs in DNA and protein sequences. The underlying principles of these syntactic algorithms and some of their results will be described.

  14. Towards the understanding of network information processing in biology

    Science.gov (United States)

    Singh, Vijay

    Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

  15. Decentralized control of ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-09-01

    Full Text Available Evolutionary Network Control (ENC has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data. In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets.

  16. Mesoscopic Structures Reveal the Network Between the Layers of Multiplex Datasets

    CERN Document Server

    Iacovacci, Jacopo; Bianconi, Ginestra

    2015-01-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex datasets, forming a "network of networks". We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collab...

  17. Prediction and testing of biological networks underlying intestinal cancer.

    Directory of Open Access Journals (Sweden)

    Vishal N Patel

    Full Text Available Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called "driver" genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections--both precedented and novel--between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21, known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc(1638N+/- or Cdkn1a (Cdkn1a(-/-, followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional, then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.

  18. Structural Approaches to Sequence Evolution Molecules, Networks, Populations

    CERN Document Server

    Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele

    2007-01-01

    Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.

  19. The Structure and Dynamics of Networks

    CERN Document Server

    Newman, Mark; Watts, Duncan J

    2011-01-01

    From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new "science of networks." This book brings together for the first time a set of seminal articles representing research from across

  20. Graphical methods for analysing feedback in biological networks - A survey

    Science.gov (United States)

    Radde, Nicole; Bar, Nadav S.; Banaji, Murad

    2010-01-01

    Observed phenotypes usually arise from complex networks of interacting cell components. Qualitative information about the structure of these networks is often available, while quantitative information may be partial or absent. It is natural then to ask what, if anything, we can learn about the behaviour of the system solely from its qualitative structure. In this article we review some techniques which can be applied to answer this question, focussing in particular on approaches involving graphical representations of model structure. By applying these techniques to various cellular network examples, we discuss their strengths and limitations, and point to future research directions.

  1. Taxonomies of networks from community structure.

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J; Reid, Stephen; Porter, Mason A; Mucha, Peter J; Fricker, Mark D; Jones, Nick S

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  2. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  3. An new representation for interconnection network structures

    Institute of Scientific and Technical Information of China (English)

    刘丽华; 陈建二; 陈松乔; 贾维嘉

    2002-01-01

    An important theoretic interest is to study the relations between different interconnection networks, and to compare the capability and performance of the network structures. The most popular way to do the investigation is network emulation. Based on the classical voltage graph theory, the authors develop a new representation scheme for interconnection network structures. The new approach is a combination of algebraic methods and combinatorial methods. The results demonstrate that the voltage graph theory is a powerful tool for representing well-known interconnection networks and in implementing optimal network emulation algorithms, and in particular, show that all popular interconnection networks have very simple and intuitive representations under the new scheme. The new representation scheme also offers powerful tools for the study of network routings and emulations. For example, we present very simple constructions for optimal network emulations from the cube-connected cycles networks to the butterfly networks, and from the butterfly networks to the hypercube networks. Compared with the most popular way of network emulation, this new scheme is intuitive and easy to realize, and easy to apply to other network structures.

  4. Neural network definitions of highly predictable protein secondary structure classes

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  5. Discriminating different classes of biological networks by analyzing the graphs spectra distribution

    CERN Document Server

    Takahashi, Daniel Yasumasa; Ferreira, Carlos Eduardo; Fujita, André

    2012-01-01

    The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibl...

  6. Glycosides from Marine Sponges (Porifera, Demospongiae: Structures, Taxonomical Distribution, Biological Activities and Biological Roles

    Directory of Open Access Journals (Sweden)

    Valentin A. Stonik

    2012-08-01

    Full Text Available Literature data about glycosides from sponges (Porifera, Demospongiae are reviewed. Structural diversity, biological activities, taxonomic distribution and biological functions of these natural products are discussed.

  7. Glycosides from marine sponges (Porifera, Demospongiae): structures, taxonomical distribution, biological activities and biological roles.

    Science.gov (United States)

    Kalinin, Vladimir I; Ivanchina, Natalia V; Krasokhin, Vladimir B; Makarieva, Tatyana N; Stonik, Valentin A

    2012-08-01

    Literature data about glycosides from sponges (Porifera, Demospongiae) are reviewed. Structural diversity, biological activities, taxonomic distribution and biological functions of these natural products are discussed.

  8. Exploitation of complex network topology for link prediction in biological interactomes

    KAUST Repository

    Alanis Lobato, Gregorio

    2014-06-01

    The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.

  9. Discovering networks of perturbed biological processes in hepatocyte cultures.

    Directory of Open Access Journals (Sweden)

    Christopher D Lasher

    Full Text Available The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs and collagen sandwiches (CS. Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs. CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior.

  10. The Deep Structure of Organizational Online Networking

    DEFF Research Database (Denmark)

    Trier, Matthias; Richter, Alexander

    2015-01-01

    of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... interaction structures. We finally discuss how the deep structure framework can contribute to future research on organizational networks.......While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...

  11. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  12. Biologically Inspired Optimization of Building District Heating Networks

    Directory of Open Access Journals (Sweden)

    Leiming Shang

    2013-07-01

    Full Text Available In this paper we show that a biologically inspired model can be successfully applied to problems of building optimal district heating network. The model is based on physiological observations of the true slime mold Physarumpolycephalum, but can also be used for path-finding in the complicated networks of mazes and road maps. A strategy of optimally building heating distribution network was guided by the model and a well-tuned ant colony algorithm and genetic algorithm. The results indicate that although there are not large-scale efficiency savings to be made, the biologically inspired amoeboid movement model is capable of finding results of equal or better optimality than a comparable ant colony algorithm and genetic algorithm.

  13. 2004 Reversible Associations in Structure & Molecular Biology

    Energy Technology Data Exchange (ETDEWEB)

    Edward Eisenstein Nancy Ryan Gray

    2005-03-23

    The Gordon Research Conference (GRC) on 2004 Gordon Research Conference on Reversible Associations in Structure & Molecular Biology was held at Four Points Sheraton, CA, 1/25-30/2004. The Conference was well attended with 82 participants (attendees list attached). The attendees represented the spectrum of endeavor in this field coming from academia, industry, and government laboratories, both U.S. and foreign scientists, senior researchers, young investigators, and students.

  14. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    Science.gov (United States)

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.

  15. Structural simplification of chemical reaction networks in partial steady states.

    Science.gov (United States)

    Madelaine, Guillaume; Lhoussaine, Cédric; Niehren, Joachim; Tonello, Elisa

    2016-11-01

    We study the structural simplification of chemical reaction networks with partial steady state semantics assuming that the concentrations of some but not all species are constant. We present a simplification rule that can eliminate intermediate species that are in partial steady state, while preserving the dynamics of all other species. Our simplification rule can be applied to general reaction networks with some but few restrictions on the possible kinetic laws. We can also simplify reaction networks subject to conservation laws. We prove that our simplification rule is correct when applied to a module of a reaction network, as long as the partial steady state is assumed with respect to the complete network. Michaelis-Menten's simplification rule for enzymatic reactions falls out as a special case. We have implemented an algorithm that applies our simplification rules repeatedly and applied it to reaction networks from systems biology.

  16. Composite nanowire networks for biological sensor platforms

    Science.gov (United States)

    Jabal, Jamie Marie Francisco

    The main goal of this research is to design, fabricate, and test a nanomaterial-based platform adequate for the measurement of physiological changes in living cells. The two primary objectives toward this end are (1) the synthesis and selection of a suitable nanomaterial and (2) the demonstration of cellular response to a direct stimulus. Determining a useful nanomaterial morphology and behavior within a sensor configuration presented challenges based on cellular integration and access to electrochemical characterization. The prospect for feasible optimization and eventual scale-up in technology were also significant. Constraining criteria are that the nanomaterial detector must (a) be cheap and relatively easy to fabricate controllably, (b) encourage cell attachment, (c) exhibit consistent wettability over time, and (d) facilitate electrochemical processes. The ultimate goal would be to transfer a proof-of-principle and proof-of-design for a whole-cell sensor technology that is cost effective and has a potential for hand-held packaging. Initial tasks were to determine an effective and highly-functional nanomaterial for biosensors by assessing wettability, morphology and conductivity behavior of several candidate materials: gallium nitride nanowires, silicon dioxide nanosprings and nanowires, and titania nanofibers. Electrospinning poly(vinyl pyrrolidone)-coated titania nano- and microfibers (O20 nm--2 microm) into a pseudo-random network is controllable to a uniformity of 1--2° in contact angle. The final electrode can be prepared with a precise wettability ranging from partial wetting to ultrahydrophobic (170°) on a variety of substrates: glass, indium tin oxide, silicon, and aluminum. Fiber mats exhibit excellent mechanical stability against rinsing, and support the incubation of epithelial (skin) and pancreatic cells. Impedance spectroscopy on the whole-cell sensor shows resistive changes attributed to cell growth as well as complex frequency

  17. Structure Biology of Membrane Bound Enzymes

    Energy Technology Data Exchange (ETDEWEB)

    Fu, Dax [Johns Hopkins Univ., Baltimore, MD (United States). School of Medicine. Dept. of Physiology

    2016-11-30

    The overall goal of the proposed research is to understand the membrane-associated active processes catalyzed by an alkane $\\square$-hydroxylase (AlkB) from eubacterium Pseudomonase oleovorans. AlkB performs oxygenation of unactivated hydrocarbons found in crude oils. The enzymatic reaction involves energy-demanding steps in the membrane with the uses of structurally unknown metal active sites featuring a diiron [FeFe] center. At present, a critical barrier to understanding the membrane-associated reaction mechanism is the lack of structural information. The structural biology efforts have been challenged by technical difficulties commonly encountered in crystallization and structural determination of membrane proteins. The specific aims of the current budget cycle are to crystalize AlkB and initiate X-ray analysis to set the stage for structural determination. The long-term goals of our structural biology efforts are to provide an atomic description of AlkB structure, and to uncover the mechanisms of selective modification of hydrocarbons. The structural information will help elucidating how the unactivated C-H bonds of saturated hydrocarbons are oxidized to initiate biodegradation and biotransformation processes. The knowledge gained will be fundamental to biotechnological applications to biofuel transformation of non-edible oil feedstock. Renewable biodiesel is a promising energy carry that can be used to reduce fossil fuel dependency. The proposed research capitalizes on prior BES-supported efforts on over-expression and purification of AlkB to explore the inner workings of a bioenergy-relevant membrane-bound enzyme.

  18. Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

    Science.gov (United States)

    Wu, Ming; Chan, Christina

    2012-01-01

    Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. PMID:24957377

  19. Consensus between pipelines in structural brain networks.

    Directory of Open Access Journals (Sweden)

    Christopher S Parker

    Full Text Available Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.

  20. Capacitive Structures for Gas and Biological Sensing

    KAUST Repository

    Sapsanis, Christos

    2015-04-01

    The semiconductor industry was benefited by the advances in technology in the last decades. This fact has an impact on the sensors field, where the simple transducer was evolved into smart miniaturized multi-functional microsystems. However, commercially available gas and biological sensors are mostly bulky, expensive, and power-hungry, which act as obstacles to mass use. The aim of this work is gas and biological sensing using capacitive structures. Capacitive sensors were selected due to its design simplicity, low fabrication cost, and no DC power consumption. In the first part, the dominant structure among interdigitated electrodes (IDEs), fractal curves (Peano and Hilbert) and Archimedean spiral was investigated from capacitance density perspective. The investigation consists of geometrical formula calculations, COMSOL Multiphysics simulations and cleanroom fabrication of the capacitors on a silicon substrate. Moreover, low-cost fabrication on flexible plastic PET substrate was conducted outside cleanroom with rapid prototyping using a maskless laser etching. The second part contains the humidity, Volatile Organic compounds (VOCs) and Ammonia sensing of polymers, Polyimide and Nafion, and metal-organic framework (MOF), Cu(bdc)2.xH2O using IDEs and tested in an automated gas setup for experiment control and data extraction. The last part includes the biological sensing of C - reactive protein (CRP) quantification, which is considered as a biomarker of being prone to cardiac diseases and Bovine serum albumin (BSA) protein quantification, which is used as a reference for quantifying unknown proteins.

  1. Imposing early stability to ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2015-03-01

    Full Text Available The stability analysis of the dynamical networks is a well-studied topic, both in ecology and in biology. In this work, I adopt a different perspective: instead of analysing the stability of an arbitrary ecological network, I seek here to impose such stability as soon as possible (or, contrariwise, as late as possible during network dynamics. Evolutionary Network Control (ENC is a theoretical and methodological framework aimed to the control of ecological and biological networks by coupling network dynamics and evolutionary modelling. ENC covers several topics of network control, for instance a the global control from inside and b from outside, c the local (step-by-step control, and the computation of: d control success, e feasibility, and f degree of uncertainty. In this work, I demonstrate that ENC can also be employed to impose early (but, also, late stability to arbitrary ecological and biological networks, and provide an applicative example based on the nonlinear, widely-used, Lotka-Volterra model.

  2. Structural Dissection for Controlling Complex Networks

    CERN Document Server

    Wang, Wen-Xu; Zhao, Chen; Liu, Yang-Yu; Lai, Ying-Cheng

    2015-01-01

    Controlling complex networked systems has been a central goal in different fields and understanding controllability of complex networks has been at the forefront of contemporary science. Despite the recent progress in the development of controllability theories for complex networks, we continue to lack efficient tools to fully understand the effect of network topology and interaction strengths among nodes on controllability. Here we establish a framework to discern the significance of links and nodes for controlling general complex networks in a simple way based on local information. A dissection process is offered by the framework to probe and classify nodes and links completely, giving rise to a criterion for strong structural controllability. Analytical results indicate phase transitions associated with link and node categories, and strong structural controllability. Applying the tools to real networks demonstrate that real technological networks are strong structurally controllable, whereas most of real s...

  3. Matching Community Structure Across Online Social Networks

    CERN Document Server

    Li, Lin

    2016-01-01

    The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecting the community structure. In this paper, we explore several approaches for community detection across online social networks with limited knowledge of username alignment across the networks. We refer to the known alignment of usernames as seeds. We investigate strategies for seed selection and its impact on networks with a different fraction of overlapping vertices. The goal is to study the interplay between network topologies and seed selection strategies, and to understand how it affects the detected community structu...

  4. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  5. Structural Interfaces and Attachments in Biology

    CERN Document Server

    Birman, Victor; Genin, Guy

    2013-01-01

    Attachment of dissimilar materials in engineering and surgical practice is a perennial challenge. Bimaterial attachment sites are common locations for injury, repeated injury, and mechanical failure. Nature presents several highly effective solutions to the challenge of bimaterial attachment that differ from those found in engineering practice. Structural Interfaces and Attachments in Biology describes the attachment of dissimilar materials from multiple perspectives. The text will simultaneously elucidate natural bimaterial attachments and outline engineering principles underlying successful attachments to the communities of tissue engineers and surgeons. Included an in-depth analysis of the biology of attachments in the body and mechanisms by which robust attachments are formed, a review of current concepts of attaching dissimilar materials in surgical practice and a discussion of bioengineering approaches that are currently being developed. This book also: Provides the first comprehensive treatment of phys...

  6. Classification of biological and non-biological fluvial particles using image processing and artificial neural network

    Science.gov (United States)

    Shrestha, Bim Prasad; Shrestha, Nabin Kumar; Poudel, Laxman

    2009-04-01

    Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.

  7. Ice breaking in GPCR structural biology

    Institute of Scientific and Technical Information of China (English)

    Qiang ZHAO; Bei-li WU

    2012-01-01

    G-protein-coupled receptors (GPCRs) are one of the most challenging targets in structural biology.To successfully solve a high-resolution GPCR structure,several experimental obstacles must be overcome,including expression,extraction,purification,and crystallization.As a result,there are only a handful of unique structures reported from this protein superfamily,which consists of over 800 members.In the past few years,however,there has been an increase in the amount of solved GPCR structures,and a few high-impact structures have been determined:the peptide receptor CXCR4,the agonist bound receptors,and the GPCR-G protein complex.The dramatic progress in GPCR structural studies is not due to the development of any single technique,buta combination of new techniques,new tools and new concepts.Here,we summarize the progress made for GPCR expression,purification,and crystalliza-tion,and we highlight the technical advances that will facilitate the future determination of GPCR structures.

  8. Passing messages between biological networks to refine predicted interactions.

    Directory of Open Access Journals (Sweden)

    Kimberly Glass

    Full Text Available Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation, a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  9. Passing messages between biological networks to refine predicted interactions.

    Science.gov (United States)

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  10. Integrated Network Analysis and Effective Tools in Plant Systems Biology

    Directory of Open Access Journals (Sweden)

    Atsushi eFukushima

    2014-11-01

    Full Text Available One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1 network visualization tools, (2 pathway analyses, (3 genome-scale metabolic reconstruction, and (4 the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

  11. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  12. An automated approach to network features of protein structure ensembles.

    Science.gov (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-10-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

  13. Matching Community Structure Across Online Social Networks

    OpenAIRE

    Lin LI; Campbell, W. M.

    2016-01-01

    The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecti...

  14. Network structure of inter-industry flows

    CERN Document Server

    McNerney, James; Silverberg, Gerald

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  15. Estrogenic flavonoids: structural requirements for biological activity.

    Science.gov (United States)

    Miksicek, R J

    1995-01-01

    A systematic survey of polycyclic phenols has been performed to identify members of this chemical group with estrogenic activity. Twelve compounds were found to be able to stimulate the transcriptional activity of the human estrogen receptor expressed in cultured cells by transient transfection. These natural estrogens belong to several distinct, but chemically related classes including chalcones, flavanones, flavones, flavonols, and isoflavones. Selected examples of estrogenic flavonoids were further analyzed to determine their biological potencies and their relative affinities for binding to the estrogen receptor. These data are interpreted with respect to the molecular structure of polycyclic phenols required for hormonal activity as nonsteroidal estrogens.

  16. Structure Properties of Koch Networks Based on Networks Dynamical Systems

    CERN Document Server

    Zhai, Yinhu; Wang, Shaohui

    2016-01-01

    We introduce an informative labeling algorithm for the vertices of a family of Koch networks. Each of the labels is consisted of two parts, the precise position and the time adding to Koch networks. The shortest path routing between any two vertices is determined only on the basis of their labels, and the routing is calculated only by few computations. The rigorous solutions of betweenness centrality for every node and edge are also derived by the help of their labels. Furthermore, the community structure in Koch networks is studied by the current and voltage characteristics of its resistor networks.

  17. Network news: innovations in 21st century systems biology.

    Science.gov (United States)

    Arkin, Adam P; Schaffer, David V

    2011-03-18

    A decade ago, seminal perspectives and papers set a strong vision for the field of systems biology, and a number of these themes have flourished. Here, we describe key technologies and insights that have elucidated the evolution, architecture, and function of cellular networks, ultimately leading to the first predictive genome-scale regulatory and metabolic models of organisms. Can systems approaches bridge the gap between correlative analysis and mechanistic insights?

  18. Analysis of complex networks from biology to linguistics

    CERN Document Server

    Dehmer, Matthias

    2009-01-01

    Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.

  19. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

    Science.gov (United States)

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

    2016-06-02

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

  20. Cross-linked structure of network evolution

    Energy Technology Data Exchange (ETDEWEB)

    Bassett, Danielle S., E-mail: dsb@seas.upenn.edu [Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Physics, University of California, Santa Barbara, California 93106 (United States); Sage Center for the Study of the Mind, University of California, Santa Barbara, California 93106 (United States); Wymbs, Nicholas F.; Grafton, Scott T. [Department of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, California 93106 (United States); Porter, Mason A. [Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); CABDyN Complexity Centre, University of Oxford, Oxford, OX1 1HP (United Kingdom); Mucha, Peter J. [Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599 (United States); Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599 (United States)

    2014-03-15

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  1. The structural biology of patellamide biosynthesis.

    Science.gov (United States)

    Koehnke, Jesko; Bent, Andrew F; Houssen, Wael E; Mann, Greg; Jaspars, Marcel; Naismith, James H

    2014-12-01

    The biosynthetic pathways for patellamide and related natural products have recently been studied by structural biology. These pathways produce molecules that have a complex framework and exhibit a diverse array of activity due to the variability of the amino acids that are found in them. As these molecules are difficult to synthesize chemically, exploitation of their properties has been modest. The patellamide pathway involves amino acid heterocyclization, peptide cleavage, peptide macrocyclization, heterocycle oxidation and epimerization; closely related products are also prenylated. Enzyme activities have been identified for all these transformations except epimerization, which may be spontaneous. This review highlights the recent structural and mechanistic work on amino acid heterocyclization, peptide cleavage and peptide macrocyclization. This work should help in using the enzymes to produce novel analogs of the natural products enabling an exploitation of their properties.

  2. 2010 Diffraction Methods in Structural Biology

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Ana Gonzalez

    2011-03-10

    Advances in basic methodologies have played a major role in the dramatic progress in macromolecular crystallography over the past decade, both in terms of overall productivity and in the increasing complexity of the systems being successfully tackled. The 2010 Gordon Research Conference on Diffraction Methods in Structural Biology will, as in the past, focus on the most recent developments in methodology, covering all aspects of the process from crystallization to model building and refinement, complemented by examples of structural highlights and complementary methods. Extensive discussion will be encouraged and it is hoped that all attendees will participate by giving oral or poster presentations, the latter using the excellent poster display area available at Bates College. The relatively small size and informal atmosphere of the meeting provides an excellent opportunity for all participants, especially younger scientists, to meet and exchange ideas with leading methods developers.

  3. Dynamic Analysis of Structures Using Neural Networks

    Directory of Open Access Journals (Sweden)

    N. Ahmadi

    2008-01-01

    Full Text Available In the recent years, neural networks are considered as the best candidate for fast approximation with arbitrary accuracy in the time consuming problems. Dynamic analysis of structures against earthquake has the time consuming process. We employed two kinds of neural networks: Generalized Regression neural network (GR and Back-Propagation Wavenet neural network (BPW, for approximating of dynamic time history response of frame structures. GR is a traditional radial basis function neural network while BPW categorized as a wavelet neural network. In BPW, sigmoid activation functions of hidden layer neurons are substituted with wavelets and weights training are achieved using Scaled Conjugate Gradient (SCG algorithm. Comparison the results of BPW with those of GR in the dynamic analysis of eight story steel frame indicates that accuracy of the properly trained BPW was better than that of GR and therefore, BPW can be efficiently used for approximate dynamic analysis of structures.

  4. Network structure and travel time perception.

    Science.gov (United States)

    Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

    2013-01-01

    The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

  5. Temporal network structures controlling disease spreading

    CERN Document Server

    Holme, Petter

    2016-01-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks and a fully connected topology. We notice that the difference between the static and fully-connected networks -- with respect to time to extinction and average outbreak size -- is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concur that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

  6. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    2012-01-01

    With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological...... environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases...... and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical...

  7. Importance of randomness in biological networks: A random matrix analysis

    Indian Academy of Sciences (India)

    Sarika Jalan

    2015-02-01

    Random matrix theory, initially proposed to understand the complex interactions in nuclear spectra, has demonstrated its success in diverse domains of science ranging from quantum chaos to galaxies. We demonstrate the applicability of random matrix theory for networks by providing a new dimension to complex systems research. We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate the importance of randomness in interactions for deducing crucial properties of the underlying system. This paper provides an overview of the importance of random matrix framework in complex systems research with biological systems as examples.

  8. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Kiran M Kolwankar; Quansheng Ren; Areejit Samal; Jürgen Jost

    2011-11-01

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of . elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

  9. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  10. 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

  11. Compressive-Sensing-Based Structure Identification for Multilayer Networks.

    Science.gov (United States)

    Mei, Guofeng; Wu, Xiaoqun; Wang, Yingfei; Hu, Mi; Lu, Jun-An; Chen, Guanrong

    2017-02-13

    The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed. Specifically, if the interested layer is sparsely connected and the node behaviors of the other layer are observable at a few time points, then a theoretical framework is established based on compressive sensing and regularization. Some numerical examples illustrate the effectiveness of the identification scheme, its requirement of a relatively small number of observations, as well as its robustness against small noise. It is noteworthy that the framework can be straightforwardly extended to multilayer networks, thus applicable to a variety of real-world complex systems.

  12. Bit by bit control of nonlinear ecological and biological networks using Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-06-01

    Full Text Available Evolutionary Network Control (ENC has been first introduced in 2013 to effectively subdue network-like systems. ENC opposes the idea, very common in the scientific literature, that controllability of networks should be based on the identification of the set of driver nodes that can guide the system's dynamics, in other words on the choice of a subset of nodes that should be selected to be permanently controlled. ENC has proven to be effective in the global control (i.e. the focus is on mastery of the final state of network dynamics of linear and nonlinear networks, and in the local (i.e. the focus is on the step-by-step ascendancy of network dynamics control of linear networks. In this work, ENC is applied to the local control of nonlinear networks. Using the Lotka-Volterra model as a case study, I show here that ENC is capable of locally driving nonlinear networks as well, so that also intermediate steps (not only the final state are under our strict control. ENC can be readily applied to any kind of ecological, biological, economic and network-like system.

  13. Discriminating different classes of biological networks by analyzing the graphs spectra distribution.

    Directory of Open Access Journals (Sweden)

    Daniel Yasumasa Takahashi

    Full Text Available The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1 protein-protein interaction networks of different species and (2 on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length failed.

  14. Biological instability in a chlorinated drinking water distribution network.

    Science.gov (United States)

    Nescerecka, Alina; Rubulis, Janis; Vital, Marius; Juhna, Talis; Hammes, Frederik

    2014-01-01

    The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia) was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM) intact cell concentrations, intracellular adenosine tri-phosphate (ATP), heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×10(3) cells mL(-1) to 4.66×10(5) cells mL(-1) in the network. While this parameter did not exceed 2.1×10(4) cells mL(-1) in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×10(5) cells mL(-1). This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability.

  15. Biological instability in a chlorinated drinking water distribution network.

    Directory of Open Access Journals (Sweden)

    Alina Nescerecka

    Full Text Available The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM intact cell concentrations, intracellular adenosine tri-phosphate (ATP, heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×10(3 cells mL(-1 to 4.66×10(5 cells mL(-1 in the network. While this parameter did not exceed 2.1×10(4 cells mL(-1 in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×10(5 cells mL(-1. This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability.

  16. A network-theoretic approach for decompositional translation across Open Biological Ontologies.

    Science.gov (United States)

    Patel, Chintan O; Cimino, James J

    2010-08-01

    Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, composite terms in these ontologies need to be translated into atomic or primitive terms in other, orthogonal ontologies, for example, gluconeogenesis (biological process term) to glucose (chemical ontology term). Identifying such decompositional ontology translations is a challenging problem. In this paper, we propose a network-theoretic approach based on the structure of the integrated OBO relationship graph. We use a network-theoretic measure, called the clustering coefficient, to find relevant atomic terms in the neighborhood of a composite term. By eliminating the existing GO to ChEBI Ontology mappings from OBO, we evaluate whether the proposed approach can re-identify the corresponding relationships. The results indicate that the network structure provides strong cues for decompositional ontology translation and the existing relationships can be used to identify new translations.

  17. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  18. Information transfer in community structured multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer ...

  19. Mathematical Analysis of a PDE System for Biological Network Formation

    KAUST Repository

    Haskovec, Jan

    2015-02-04

    Motivated by recent physics papers describing rules for natural network formation, we study an elliptic-parabolic system of partial differential equations proposed by Hu and Cai [13, 15]. The model describes the pressure field thanks to Darcy\\'s type equation and the dynamics of the conductance network under pressure force effects with a diffusion rate D >= 0 representing randomness in the material structure. We prove the existence of global weak solutions and of local mild solutions and study their long term behavior. It turns out that, by energy dissipation, steady states play a central role to understand the network formation capacity of the system. We show that for a large diffusion coefficient D, the zero steady state is stable, while network formation occurs for small values of D due to the instability of the zero steady state, and the borderline case D = 0 exhibits a large class of dynamically stable (in the linearized sense) steady states.

  20. Network structure of subway passenger flows

    CERN Document Server

    Xu, Qi; Bai, Yun

    2016-01-01

    The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial doma...

  1. Temporal network structures controlling disease spreading

    Science.gov (United States)

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks—with respect to time to extinction and average outbreak size—is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

  2. Structural Biology of Nuclear Auxin Action.

    Science.gov (United States)

    Dinesh, Dhurvas Chandrasekaran; Villalobos, Luz Irina A Calderón; Abel, Steffen

    2016-04-01

    Auxin coordinates plant development largely via hierarchical control of gene expression. During the past decades, the study of early auxin genes paired with the power of Arabidopsis genetics have unraveled key nuclear components and molecular interactions that perceive the hormone and activate primary response genes. Recent research in the realm of structural biology allowed unprecedented insight into: (i) the recognition of auxin-responsive DNA elements by auxin transcription factors; (ii) the inactivation of those auxin response factors by early auxin-inducible repressors; and (iii) the activation of target genes by auxin-triggered repressor degradation. The biophysical studies reviewed here provide an impetus for elucidating the molecular determinants of the intricate interactions between core components of the nuclear auxin response module.

  3. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  4. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    Network research has yet to determine whether bonding ties or bridging ties are more beneficial for individual creativity, but the debate has mostly overlooked the organizational context in which such ties are formed. In particular, the causal chain connecting network structures and individual ou...

  5. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  6. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  7. Motif structure and cooperation in real-world complex networks

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R.; Jalili, Mahdi

    2010-12-01

    Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.

  8. Structurally dynamic spin market networks

    CERN Document Server

    Horváth, D

    2007-01-01

    The agent-based model of price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The resulting stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. For some properly selected parametric combination the network displays small-world phenomenon with high mean clustering coefficient and power-law node degree distribution. The mechanism of repeated random walk through network combined with a fitness recognition is proposed and tested to generate modular multi-leader market. The simulations suggest that dynamics of fitness is the slowest process that manifests itself in the volatility clustering of the log-price returns.

  9. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  10. CellNet: network biology applied to stem cell engineering.

    Science.gov (United States)

    Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J

    2014-08-14

    Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

  11. Inference of asynchronous Boolean network from biological pathways.

    Science.gov (United States)

    Das, Haimabati; Layek, Ritwik Kumar

    2015-01-01

    Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.

  12. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  13. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  14. Methods of information theory and algorithmic complexity for network biology.

    Science.gov (United States)

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

  15. Structural Connectivity Networks of Transgender People

    OpenAIRE

    Hahn, Andreas; Kranz, Georg S.; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F.; Lanzenberger, Rupert

    2015-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) transgender patients before hormone therapy as compared with 25 female and 25 male healthy controls. Graph theoretical analysis of whole-brain probabilistic tractography networks (adjusted for diffe...

  16. Structural Connectivity Networks of Transgender People

    OpenAIRE

    Hahn, Andreas; Kranz, Georg S.; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F.; Lanzenberger, Rupert

    2014-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) transgender patients before hormone therapy as compared with 25 female and 25 male healthy controls. Graph theoretical analysis of whole-brain probabilistic tractography networks (adjusted for diffe...

  17. Molecular codes in biological and chemical reaction networks.

    Science.gov (United States)

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

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

  18. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  19. Correlations between community structure and link formation in complex networks.

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    Full Text Available BACKGROUND: Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. METHODOLOGY/PRINCIPAL FINDINGS: Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. CONCLUSIONS/SIGNIFICANCE: Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  20. Structure-based control of complex networks with nonlinear dynamics

    CERN Document Server

    Zañudo, Jorge G T; Albert, Réka

    2016-01-01

    Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system. We use this framework on several real networks, compar...

  1. Bayesian inference of structural brain networks.

    Science.gov (United States)

    Hinne, Max; Heskes, Tom; Beckmann, Christian F; van Gerven, Marcel A J

    2013-02-01

    Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. State-of-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.

  2. A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Xin Lai

    2013-01-01

    Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

  3. Structural biology computing: Lessons for the biomedical research sciences.

    Science.gov (United States)

    Morin, Andrew; Sliz, Piotr

    2013-11-01

    The field of structural biology, whose aim is to elucidate the molecular and atomic structures of biological macromolecules, has long been at the forefront of biomedical sciences in adopting and developing computational research methods. Operating at the intersection between biophysics, biochemistry, and molecular biology, structural biology's growth into a foundational framework on which many concepts and findings of molecular biology are interpreted1 has depended largely on parallel advancements in computational tools and techniques. Without these computing advances, modern structural biology would likely have remained an exclusive pursuit practiced by few, and not become the widely practiced, foundational field it is today. As other areas of biomedical research increasingly embrace research computing techniques, the successes, failures and lessons of structural biology computing can serve as a useful guide to progress in other biomedically related research fields.

  4. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    Energy Technology Data Exchange (ETDEWEB)

    Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  5. Optimal pinning controllability of complex networks: dependence on network structure.

    Science.gov (United States)

    Jalili, Mahdi; Askari Sichani, Omid; Yu, Xinghuo

    2015-01-01

    Controlling networked structures has many applications in science and engineering. In this paper, we consider the problem of pinning control (pinning the dynamics into the reference state), and optimally placing the driver nodes, i.e., the nodes to which the control signal is fed. Considering the local controllability concept, a metric based on the eigenvalues of the Laplacian matrix is taken into account as a measure of controllability. We show that the proposed optimal placement strategy considerably outperforms heuristic methods including choosing hub nodes with high degree or betweenness centrality as drivers. We also study properties of optimal drivers in terms of various centrality measures including degree, betweenness, closeness, and clustering coefficient. The profile of these centrality values depends on the network structure. For homogeneous networks such as random small-world networks, the optimal driver nodes have almost the mean centrality value of the population (much lower than the centrality value of hub nodes), whereas the centrality value of optimal drivers in heterogeneous networks such as scale-free ones is much higher than the average and close to that of hub nodes. However, as the degree of heterogeneity decreases in such networks, the profile of centrality approaches the population mean.

  6. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  7. Practical use of BiNoM: a biological network manager software.

    Science.gov (United States)

    Bonnet, Eric; Calzone, Laurence; Rovera, Daniel; Stoll, Gautier; Barillot, Emmanuel; Zinovyev, Andrei

    2013-01-01

    The Biological Network Manager (BiNoM) is a software tool for the manipulation and analysis of biological networks. It facilitates the import and conversion of a set of well-established systems biology file formats. It also provides a large set of graph-based algorithms that allow users to analyze and extract relevant subnetworks from large molecular maps. It has been successfully used in several projects related to the analysis of large and complex biological data, or networks from databases. In this tutorial, we present a detailed and practical case study of how to use BiNoM to analyze biological networks.

  8. Chapter 5: Network biology approach to complex diseases.

    Directory of Open Access Journals (Sweden)

    Dong-Yeon Cho

    Full Text Available Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.

  9. Structural Biology in the context of EGEE

    CERN Document Server

    García, D; Carazo, J M; Valverde, J R; Moscicki, J; Muraru, A

    2007-01-01

    Electron microscopy (EM) is a crucial technique, which allows Structural Biology researchers to characterize macromolecular assemblies in distinct functional states. Image processing in three dimensional EM (3D-EM) is used by a flourishing community (exemplarized by the EU funded 3D-EM NoE) and is characterized by voluminous data and large computing requirements, making this a problem well suited for Grid computing and the EGEE infrastructure. There are various steps in the 3D-EM refinement process that may benefit from Grid computing. To start with, large numbers of experimental images need to be averaged. Nowadays, typically tens of thousands of images are used, while future studies may routinely employ millions of images. Our group has been developing Xmipp, a package for single-particle 3D-EM image processing. Using Xmipp, the classification of 91,000 ribosome projections into 4 classes took more than 2500 CPU hours using the resources of the MareNostrum supercomputer at the Barcelona Supercomputing Centr...

  10. Integrative biology identifies shared transcriptional networks in CKD.

    Science.gov (United States)

    Martini, Sebastian; Nair, Viji; Keller, Benjamin J; Eichinger, Felix; Hawkins, Jennifer J; Randolph, Ann; Böger, Carsten A; Gadegbeku, Crystal A; Fox, Caroline S; Cohen, Clemens D; Kretzler, Matthias

    2014-11-01

    A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

  11. A Network Biology Approach to Denitrification in Pseudomonas aeruginosa

    Science.gov (United States)

    Arat, Seda; Bullerjahn, George S.; Laubenbacher, Reinhard

    2015-01-01

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO2), nitric oxide (NO) and nitrous oxide (N2O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O2), nitrate (NO3), and phosphate (PO4) suggests that PO4 concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO4 on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N2O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide. PMID:25706405

  12. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure...

  13. Structural Connectivity Networks of Transgender People

    NARCIS (Netherlands)

    Hahn, Andreas; Kranz, Georg S; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F; Lanzenberger, Rupert

    2015-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) t

  14. Interconnectivity structure of a general interdependent network.

    Science.gov (United States)

    Van Mieghem, P

    2016-04-01

    A general two-layer network consists of two networks G_{1} and G_{2}, whose interconnection pattern is specified by the interconnectivity matrix B. We deduce desirable properties of B from a dynamic process point of view. Many dynamic processes are described by the Laplacian matrix Q. A regular topological structure of the interconnectivity matrix B (constant row and column sum) enables the computation of a nontrivial eigenmode (eigenvector and eigenvalue) of Q. The latter eigenmode is independent from G_{1} and G_{2}. Such a regularity in B, associated to equitable partitions, suggests design rules for the construction of interconnected networks and is deemed crucial for the interconnected network to show intriguing behavior, as discovered earlier for the special case where B=wI refers to an individual node to node interconnection with interconnection strength w. Extensions to a general m-layer network are also discussed.

  15. Stochastic blockmodels and community structure in networks

    CERN Document Server

    Karrer, Brian

    2010-01-01

    Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.

  16. Cognitive network structure: an experimental study

    CERN Document Server

    Guazzini, Andrea; Bagnoli, Franco; Carletti, Timoteo; Grotto, Rosapia Lauro

    2012-01-01

    In this paper we present first experimental results about a small group of people exchanging private and public messages in a virtual community. Our goal is the study of the cognitive network that emerges during a chat seance. We used the Derrida coefficient and the triangle structure under the working assumption that moods and perceived mutual affinity can produce results complementary to a full semantic analysis. The most outstanding outcome is the difference between the network obtained considering publicly exchanged messages and the one considering only privately exchanged messages: in the former case, the network is very homogeneous, in the sense that each individual interacts in the same way with all the participants, whilst in the latter the interactions among different agents are very heterogeneous, and are based on "the enemy of my enemy is my friend" strategy. Finally a recent characterization of the triangular cliques has been considered in order to describe the intimate structure of the network. E...

  17. A New Computationally Efficient Measure of Topological Redundancy of Biological and Social Networks

    CERN Document Server

    Albert, Reka; Gitter, Anthony; Gursoy, Gamze; Hegde, Rashmi; Paul, Pradyut; Sivanathan, Gowri Sangeetha; Sontag, Eduardo

    2011-01-01

    It is well-known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient and applicable to a variety of directed networks such as cellular signaling, metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively...

  18. Computationally efficient measure of topological redundancy of biological and social networks

    Science.gov (United States)

    Albert, Réka; Dasgupta, Bhaskar; Hegde, Rashmi; Sivanathan, Gowri Sangeetha; Gitter, Anthony; Gürsoy, Gamze; Paul, Pradyut; Sontag, Eduardo

    2011-09-01

    It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

  19. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    Science.gov (United States)

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

  20. Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

    Science.gov (United States)

    Larjo, Antti; Lähdesmäki, Harri

    2015-12-01

    Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.

  1. Genetic and logic networks with the signal-inhibitor-activator structure are dynamically robust

    Institute of Scientific and Technical Information of China (English)

    LI Fangting; TAN Ning

    2006-01-01

    The proteins, DNA and RNA interaction networks govern various biological functions in living cells, these networks should be dynamically robust in the intracellular and environmental fluctuations. Here, we use Boolean network to study the robust structure of both genetic and logic networks. First, SOS network in bacteria E. coli, which regulates cell survival and repair after DNA damage, is shown to be dynamically robust. Comparing with cell cycle network in budding yeast and flagella network in E. coli, we find the signal-inhibitor-activator (SIA) structure in transcription regulatory networks. Second, under the dynamical rule that inhibition is much stronger than activation, we have searched 3-node non-self-loop logical networks that are dynamically robust, and that if the attractive basin of a final attractor is as large as seven, and the final attractor has only one active node, then the active node acts as inhibitor, and the SIA and signal-inhibitor (SI) structures are fundamental architectures of robust networks. SIA and SI networks with dynamic robustness against environment uncertainties may be selected and maintained over the course of evolution, rather than blind trial-error testing and be ing an accidental consequence of particular evolutionary history. SIA network can perform a more complex process than SI network, andSIA might be used to design robust artificial genetic network. Our results provide dynamical support for why the inhibitors and SIA/SI structures are frequently employed in cellular regulatory networks.

  2. Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities.

    Science.gov (United States)

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2016-06-06

    In this brief, we consider the problem of finding a probabilistic Boolean network (PBN) based on a network structure and desired steady-state properties. In systems biology and synthetic biology, such problems are important as an inverse problem. Using a matrix-based representation of PBNs, a solution method for this problem is proposed. The problem of finding a BN has been studied so far. In the problem of finding a PBN, we must calculate not only the Boolean functions, but also the probabilities of selecting a Boolean function and the number of candidates of the Boolean functions. Hence, the problem of finding a PBN is more difficult than that of finding a BN. The effectiveness of the proposed method is presented by numerical examples.

  3. MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology.

    Science.gov (United States)

    Vera, Julio; Lai, Xin; Schmitz, Ulf; Wolkenhauer, Olaf

    2013-01-01

    MicroRNAs (miRNAs) are involved in many regulatory pathways some of which are complex networks enriched in regulatory motifs like positive or negative feedback loops or coherent and incoherent feedforward loops. Their complexity makes the understanding of their regulation difficult and the interpretation of experimental data cumbersome. In this book chapter we claim that systems biology is the appropriate approach to investigate the regulation of these miRNA-regulated networks. Systems biology is an interdisciplinary approach by which biomedical questions on biochemical networks are addressed by integrating experiments with mathematical modelling and simulation. We here introduce the foundations of the systems biology approach, the basic theoretical and computational tools used to perform model-based analyses of miRNA-regulated networks and review the scientific literature in systems biology of miRNA regulation, with a focus on cancer.

  4. Data acquisition and analysis at the Structural Biology Center

    Energy Technology Data Exchange (ETDEWEB)

    Westbrook, M.L.; Coleman, T.A.; Daly, R.T. [Argonne National Lab., IL (United States); Pflugrath, J.W. [Molecular Structure Corp., The Woodlands, TX (United States)

    1996-12-31

    The Structural Biology Center (SBC), a national user facility for macromolecular crystallography located at Argonne National Laboratory`s Advanced Photon Source, is currently being built and commissioned. SBC facilities include a bending-magnet beamline, an insertion-device beamline, laboratory and office space adjacent to the beamlines, and associated instrumentation, experimental apparatus, and facilities. SBC technical facilities will support anomalous dispersion phasing experiments, data collection from microcrystals, data collection from crystals with large molecular structures and rapid data collection from multiple related crystal structures for protein engineering and drug design. The SBC Computing Systems and Software Engineering Group is tasked with developing the SBC Control System, which includes computing systems, network, and software. The emphasis of SBC Control System development has been to provide efficient and convenient beamline control, data acquisition, and data analysis for maximal facility and experimenter productivity. This paper describes the SBC Control System development, specifically data acquisition and analysis at the SBC, and the development methods used to meet this goal.

  5. Hydrological structure and biological productivity of the tropical Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Muraleedharan, U.D.; Muraleedharan, P.M.

    Hydrological structure analyses of regions in the tropical Atlantic Ocean have consistently revealed the existence of a typical tropical structure characterized by a nitrate-depleted mixed layer above the thermocline. The important biological...

  6. Topological Structure of Urban Street Networks from the Perspective of Degree Correlations

    CERN Document Server

    Jiang, Bin; Lu, Feng; Yang, Tinghong; Zhao, Jing

    2013-01-01

    Many complex networks demonstrate a phenomenon of striking degree correlations, i.e., a node tends to link to other nodes with similar (or dissimilar) degrees. From the perspective of degree correlations, this paper attempts to characterize topological structures of urban street networks. We adopted six urban street networks (three European and three North American), and converted them into network topologies in which nodes and edges respectively represent individual streets and street intersections, and compared the network topologies to three reference network topologies (biological, technological, and social). The urban street network topologies (with the exception of Manhattan) showed a consistent pattern that distinctly differs from the three reference networks. The topologies of urban street networks lack striking degree correlations in general. Through reshuffling the network topologies towards for example maximum or minimum degree correlations while retaining the initial degree distributions, we found...

  7. Structural Biology and Molecular Applications Research

    Science.gov (United States)

    Part of NCI's Division of Cancer Biology's research portfolio, research and development in this area focuses on enabling technologies, models, and methodologies to support basic and applied cancer research.

  8. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks

    Science.gov (United States)

    Wiencko, Heather L.; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J.

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat). PMID:27853512

  9. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

    Science.gov (United States)

    Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest.

  10. Double network bacterial cellulose hydrogel to build a biology-device interface.

    Science.gov (United States)

    Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

    2014-01-21

    Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

  11. Community detection for networks with unipartite and bipartite structure

    OpenAIRE

    Chang, Chang; Tang, Chao

    2013-01-01

    Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite net...

  12. Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp

    Directory of Open Access Journals (Sweden)

    networkbiology@iaees.org

    Full Text Available Network Biology ISSN 2220-8879 URL: http://www.iaees.org/publications/journals/nb/online-version.asp RSS: http://www.iaees.org/publications/journals/nb/rss.xml E-mail: networkbiology@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS is an open access, peer-reviewed international journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology. It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: •Theories, algorithms and programs of network analysis •Innovations and applications of biological networks •Ecological networks, food webs and natural equilibrium •Co-evolution, co-extinction, biodiversity conservation •Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs •Physiological networksNetwork regulation of metabolic processes, human diseases and ecological systems •Social networks, epidemiological networks •System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal

  13. CSMB | Center For Structural Molecular Biology

    Data.gov (United States)

    Federal Laboratory Consortium — The Center for Structural Molecular Biologyat ORNL is dedicated to developing instrumentation and methods for determining the 3-dimensional structures of proteins,...

  14. Identifying Community Structures in Dynamic Networks

    CERN Document Server

    Alvari, Hamidreza; Sukthankar, Gita; Lakkaraju, Kiran

    2016-01-01

    Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

  15. Bayesian blind source separation for data with network structure.

    Science.gov (United States)

    Illner, Katrin; Fuchs, Christiane; Theis, Fabian J

    2014-11-01

    In biology, more and more information about the interactions in regulatory systems becomes accessible, and this often leads to prior knowledge for recent data interpretations. In this work we focus on multivariate signaling data, where the structure of the data is induced by a known regulatory network. To extract signals of interest we assume a blind source separation (BSS) model, and we capture the structure of the source signals in terms of a Bayesian network. To keep the parameter space small, we consider stationary signals, and we introduce the new algorithm emGrade, where model parameters and source signals are estimated using expectation maximization. For network data, we find an improved estimation performance compared to other BSS algorithms, and the flexible Bayesian modeling enables us to deal with repeated and missing observation values. The main advantage of our method is the statistically interpretable likelihood, and we can use model selection criteria to determine the (in general unknown) number of source signals or decide between different given networks. In simulations we demonstrate the recovery of the source signals dependent on the graph structure and the dimensionality of the data.

  16. Uncovering the spatial structure of mobility networks

    CERN Document Server

    Louail, Thomas; Picornell, Miguel; Cantú, Oliva García; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J; Barthelemy, Marc

    2015-01-01

    The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method which extracts a coarse-grained signature of mobility networks, under the form of a $2\\times 2$ matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in thirty-one Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally the method allows to determine categories of networks, and in the mobility case to classify cities according to their commuting structure.

  17. Managing biological networks by using text mining and computer-aided curation

    Science.gov (United States)

    Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

    2015-11-01

    In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

  18. Probabilistic Inference of Biological Networks via Data Integration

    Directory of Open Access Journals (Sweden)

    Mark F. Rogers

    2015-01-01

    Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

  19. Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

    Science.gov (United States)

    Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

    2014-12-06

    Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

  20. Network Structures for Distributed Situation Assessment

    Science.gov (United States)

    1980-08-01

    14 Communication Pathways ...................................... 15 IV. MACHINE STRUCTURES FOR DISTRIBUTED...complexity-reducing effect of the abstraction pro- cess. INTEGRATION Integration denotes the degree and type of internode coupling required to solve a...effective solution than the development of intelligent, cooperative software. COMMUNICATION PATHWAYS The conclusions discussed earlier concerning network

  1. Bayesian inference of structural brain networks

    NARCIS (Netherlands)

    Hinne, M.; Heskes, T.; Beckmann, C.F.; Gerven, van M.A.J.

    2013-01-01

    Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unf

  2. Social Network Structures among Groundnut Farmers

    Science.gov (United States)

    Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

    2013-01-01

    Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

  3. Structural network efficiency predicts conversion to dementia

    NARCIS (Netherlands)

    Tuladhar, A.M.; Uden, I.W.M. van; Rutten-Jacobs, L.C.A.; Lawrence, A.; Holst, H. van der; Norden, A.G.W. van; Laat, K.F. de; Dijk, E.J. van; Claassen, J.A.H.R.; Kessels, R.P.C.; Markus, H.S.; Norris, D.G.; Leeuw, H.F. de

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen Dif

  4. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  5. Spatial-Frequency Azimuthally Stable Cartography of Biological Polycrystalline Networks

    Directory of Open Access Journals (Sweden)

    V. A. Ushenko

    2013-01-01

    Full Text Available A new azimuthally stable polarimetric technique processing microscopic images of optically anisotropic structures of biological tissues histological sections is proposed. It has been used as a generalized model of phase anisotropy definition of biological tissues by using superposition of Mueller matrices of linear birefringence and optical activity. The matrix element M44 has been chosen as the main information parameter, whose value is independent of the rotation angle of both sample and probing beam polarization plane. For the first time, the technique of concerted spatial-frequency filtration has been used in order to separate the manifestation of linear birefringence and optical activity. Thereupon, the method of azimuthally stable spatial-frequency cartography of biological tissues histological sections has been elaborated. As the analyzing tool, complex statistic, correlation, and fractal analysis of coordinate distributions of M44 element has been performed. The possibility of using the biopsy of the uterine wall tissue in order to differentiate benign (fibromyoma and malignant (adenocarcinoma conditions has been estimated.

  6. Image processing and computing in structural biology

    NARCIS (Netherlands)

    Jiang, Linhua

    2009-01-01

    With the help of modern techniques of imaging processing and computing, image data obtained by electron cryo-microscopy of biomolecules can be reconstructed to three-dimensional biological models at sub-nanometer resolution. These models allow answering urgent problems in life science, for instance,

  7. Information diffusion in structured online social networks

    Science.gov (United States)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  8. Fundamental structures of dynamic social networks.

    Science.gov (United States)

    Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

    2016-09-06

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

  9. Mesoscopic structures reveal the network between the layers of multiplex data sets

    Science.gov (United States)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  10. Mesoscopic structures reveal the network between the layers of multiplex data sets.

    Science.gov (United States)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  11. 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

  12. ModuLand plug-in for Cytoscape: extensively overlapping modules, community centrality and their use in biological networks

    CERN Document Server

    Szalay-Beko, Mate; Szappanos, Balazs; Kovacs, Istvan A; Papp, Balazs; Csermely, Peter

    2011-01-01

    Summary: The extensively overlapping structure of network modules is an increasingly recognized feature of biological networks. Here we introduce a user-friendly implementation of our previous network module determination method, ModuLand, as a plug-in of the widely used Cytoscape program. We show the utility of this approach a.) to identify an extensively overlapping modular structure; b.) to define a modular core and hierarchy allowing an easier functional annotation; c.) to identify key nodes of high community centrality, modular overlap or bridgeness in protein structure, protein-protein interaction and metabolic networks. Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a JAVA-based graphical interface, can be installed as a single plug-in and can run on Windows, Linux, or Mac OS. The plug-in and its user guide can be downloaded from: http://www.linkgroup.hu/modules_bioinfo_download.php

  13. 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.

  14. The fundamental structures of dynamic social networks

    CERN Document Server

    Sekara, Vedran; Lehmann, Sune

    2015-01-01

    Networks provide a powerful mathematical framework for analyzing the structure and dynamics of complex systems (1-3). The study of group behavior has deep roots in the social science literature (4,5) and community detection is a central part of modern network science. Network communities have been found to be highly overlapping and organized in a hierarchical structure (6-9). Recent technological advances have provided a toolset for measuring the detailed social dynamics at scale (10,11). In spite of great progress, a quantitative description of the complex temporal behavior of social groups-with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years-is still absent. Here we uncover a class of fundamental structures embedded within highly dynamic social networks. On the shortest time-scale, we find that social gatherings are fluid, with members coming and going, but organized via a stable core of individuals. We show that cores represent social contexts (9), with recur...

  15. An examination of a reciprocal relationship between network governance and network structure

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten; Goduscheit, René Chester

    2011-01-01

    In the present article, we examine the network structure and governance of inter-organisational innovation networks over time. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals...... with the overall structural relations between the actors in the network. These streams of research do contain references to each other but they mainly rely on a static conception of the relationship between network structure and the applied network governance. Based on a case study of a loosely coupled Danish...... inter-organisational innovation network, the proposition is that a reciprocal relationship between network governance and network structure can be identified. Such a reciprocal relationship involves theoretical and practical implications for how to govern an inter-organisational network....

  16. The redox biology network in cancer pathophysiology and therapeutics

    Directory of Open Access Journals (Sweden)

    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  17. Design principles for the analysis and construction of robustly homeostatic biological networks.

    Science.gov (United States)

    Tang, Zhe F; McMillen, David R

    2016-11-07

    Homeostatic biological systems resist external disturbances, allowing cells and organisms to maintain a constant internal state despite perturbations from their surroundings. Many biological regulatory networks are known to act homeostatically, with examples including thermal adaptation, osmoregulation, and chemotaxis. Understanding the network topologies (sets of regulatory interactions) and biological parameter regimes that can yield homeostasis in a biological system is of interest both for the study of natural biological system, and in the context of designing new biological control schemes for use in synthetic biology. Here, we examine the mathematical properties of a function that maps a biological system's inputs to its outputs, we have formulated a novel criterion (the "cofactor condition") that compactly describes the conditions for homeostasis. We further analyze the problem of robust homeostasis, wherein the system is required to maintain homeostatic behavior when its parameter values are slightly altered. We use the cofactor condition to examine previously reported examples of robust homeostasis, showing that it is a useful way to unify a number of seemingly different analyses into a single framework. Based on the observation that all previous robustly homeostatic examples fall into one of three classes, we propose a "strong cofactor condition" and use it to provide an algorithm for designing new robustly homeostatic biological networks, giving both their topologies and constraints on their parameter values. Applying the design algorithm to a three-node biological network, we construct several robustly homeostatic genetic networks, uncovering network topologies not previously identified as candidates for exhibiting robust homeostasis.

  18. Characteristics of Molecular-biological Systems and Process-network Synthesis

    CERN Document Server

    Papp, L; Friedler, F; Fan, L T

    2002-01-01

    Graph Theoretic Process Network Synthesis is described as an introduction to biological networks. Genetic, protein and metabolic systems are considered. The theoretical work of Kauffman is discussed and amplified by critical property excursions. The scaling apparent in biological systems is shown. Applications to evolution and reverse engineering are construed. The use of several programs, such as the Synprops, Design of molecules, Therm and Knapsack are suggested as instruments to study biological process network synthesis. The properties of robust self-assembly and Self-Organizing synthesis are important contributors to the discussion. The bar code of life and intelligent design is reviewed. The need for better data in biological systems is emphasized.

  19. Reconstruct the Hierarchical Structure in a Complex Network

    CERN Document Server

    Yang, H; Zhou, T; Ang, B; Zhao, F; Yang, Huijie; Wang, Wenxu; Zhou, Tao; ang, Binghong; Zhao, Fangcui

    2005-01-01

    A number of recent works have concentrated on a few statistical properties of complex networks, such as the clustering, the right-skewed degree distribution and the community, which are common to many real world networks. In this paper, we address the hierarchy property sharing among a large amount of networks. Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known. We also apply it to a Mathematicians' collaboration network and the protein interaction network of Yeast. The method can detect significantly hierarchical structures in these networks.

  20. Improving resource utilization in hierarchy network by optimizing topological structure

    Science.gov (United States)

    Liu, G. L.; Peng, H. P.; Li, L. X.; Sun, F.; Yang, Y. X.

    2012-02-01

    We study the performance of peer-to-peer (P2P) network built on the top of hierarchy topological structure of local area networks (LAN). We find that the topological structure of the underlying physical network has significant impacts on the resource utilization of the P2P overlay network. The larger size of the physical network is, the lower resource utilization of the overlay network is. Through optimizing the topological structure of physical network, we propose two novel schemes to improve the resource utilization. The experimental results show that in any case the resource utilization of P2P network can always achieve 100% by these two schemes.

  1. Fundamentals of complex networks models, structures and dynamics

    CERN Document Server

    Chen, Guanrong; Li, Xiang

    2014-01-01

    Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F

  2. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designe...... is better than most secondary structure prediction methods based on single sequences even though this model contains much fewer parameters...

  3. Collaboration Networks in the Brazilian Scientific Output in Evolutionary Biology: 2000-2012.

    Science.gov (United States)

    Santin, Dirce M; Vanz, Samile A S; Stumpf, Ida R C

    2016-03-01

    This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community.

  4. Collaboration Networks in the Brazilian Scientific Output in Evolutionary Biology: 2000-2012

    Directory of Open Access Journals (Sweden)

    Dirce M. Santin

    2016-03-01

    Full Text Available This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community.

  5. Neutron scattering for the analysis of biological structures. Brookhaven symposia in biology. Number 27

    Energy Technology Data Exchange (ETDEWEB)

    Schoenborn, B P [ed.

    1976-01-01

    Sessions were included on neutron scattering and biological structure analysis, protein crystallography, neutron scattering from oriented systems, solution scattering, preparation of deuterated specimens, inelastic scattering, data analysis, experimental techniques, and instrumentation. Separate entries were made for the individual papers.

  6. On the Concept "Microscope": Biology Student Teachers' Cognitive Structure

    Science.gov (United States)

    Kurt, Hakan; Ekici, Gulay; Aktas, Murat; Aksu, Ozlem

    2013-01-01

    The purpose of the current study is to determine biology student teachers' cognitive structures on the concept of microscope. Qualitative research methodology has been applied in the study. The data were collected from biology student teachers. Free word association test and drawing-writing test were used to collect data. The data collected were…

  7. Identifying and visualizing macromolecular flexibility in structural biology

    Directory of Open Access Journals (Sweden)

    Martina Palamini

    2016-09-01

    Full Text Available Structural biology comprises a variety of tools to obtain atomic resolution data for the investigation of macromolecules. Conventional structural methodologies including crystallography, NMR and electron microscopy often do not provide sufficient details concerning flexibility and dynamics, even though these aspects are critical for the physiological functions of the systems under investigation. However, the increasing complexity of the molecules studied by structural biology (including large macromolecular assemblies, integral membrane proteins, intrinsically disordered systems, and folding intermediates continuously demands in-depth analyses of the roles of flexibility and conformational specificity involved in interactions with ligands and inhibitors. The intrinsic difficulties in capturing often subtle but critical molecular motions in biological systems have restrained the investigation of flexible molecules into a small niche of structural biology. Introduction of massive technological developments over the recent years, which include time-resolved studies, solution X-ray scattering, and new detectors for cryo-electron microscopy, have pushed the limits of structural investigation of flexible systems far beyond traditional approaches of NMR analysis. By integrating these modern methods with powerful biophysical and computational approaches such as generation of ensembles of molecular models and selective particle picking in electron microscopy, more feasible investigations of dynamic systems are now possible. Using some prominent examples from recent literature, we review how current structural biology methods can contribute useful data to accurately visualize flexibility in macromolecular structures and understand its important roles in regulation of biological processes.

  8. The defining features of the modern entrepreneurship network structures

    Directory of Open Access Journals (Sweden)

    R.V. Kochubey

    2011-03-01

    Full Text Available In the article the various forms of network enterprises are analyzed, the key characteristics of network structures are identified. The relationship between network types and forms of business organization is shown. The studies of different types of network structures are systematized.

  9. Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

    Science.gov (United States)

    Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

    2008-10-01

    Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

  10. An examination of a reciprocal relationship between network governance and network structure

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten; Goduscheit, René Chester

    The present article examines the network structure and governance of inter-organisational innovation networks. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals with the overall...... structural relations between the actors in the network. These streams of research do contain references to each other but mostly rely on a static conception of the relationship between network structure and the applied network governance. The paper is based on a primarily qualitative case study of a loosely...... coupled Danish inter-organisational innovation network. The proposition is that a reciprocal relation between network governance and network structure can be identified....

  11. Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

    Directory of Open Access Journals (Sweden)

    Theis Fabian J

    2010-10-01

    Full Text Available Abstract Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. Results Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. Conclusions In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning

  12. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. Hayes; Pelletier, Dale A.; Schmoyer, Denise D.; Cannon, William R.

    2007-12-01

    One of the core tasks of the emerging discipline of systems biology is the reconstruction of the various biological networks in an organism. The importance of understanding such regulatory, interaction, and signaling networks has fueled the development by bioinformatics researchers of many inference algorithms for determining their structure. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, testing, and improvement of algorithms used to reconstruct the structures of regulatory and interaction networks from high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, a software package for exploratory data analysis that allows basic integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. Thus, the combined SEBINI–CABIN platform aids in the more accurate determination of biological networks, in less time, with less effort. In this paper, we present a case study demonstrating the use of the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein-protein interaction networks from sets of mass spectrometry bait-prey experiment data. To the best of our knowledge the pipeline so designed is the first to be publicly available for such use. A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/NIT/NIT.html. Source code and PostgreSQL database schema are available under open source license. Contact: ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. The

  13. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  14. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  15. Structure and dynamics of core-periphery networks

    CERN Document Server

    Csermely, Peter; Wu, Ling-Yun; Uzzi, Brian

    2013-01-01

    Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways e...

  16. Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

    Science.gov (United States)

    Yang, Gang; Campbell, Colin; Albert, Réka

    2016-12-01

    Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

  17. Structural biology applications of solid state MAS DNP NMR

    Science.gov (United States)

    Akbey, Ümit; Oschkinat, Hartmut

    2016-08-01

    Dynamic Nuclear Polarization (DNP) has long been an aim for increasing sensitivity of nuclear magnetic resonance (NMR) spectroscopy, delivering spectra in shorter experiment times or of smaller sample amounts. In recent years, it has been applied in magic angle spinning (MAS) solid-state NMR to a large range of samples, including biological macromolecules and functional materials. New research directions in structural biology can be envisaged by DNP, facilitating investigations on very large complexes or very heterogeneous samples. Here we present a summary of state of the art DNP MAS NMR spectroscopy and its applications to structural biology, discussing the technical challenges and factors affecting DNP performance.

  18. C-GRAAL: common-neighbors-based global GRAph ALignment of biological networks.

    Science.gov (United States)

    Memišević, Vesna; Pržulj, Nataša

    2012-07-01

    Networks are an invaluable framework for modeling biological systems. Analyzing protein-protein interaction (PPI) networks can provide insight into underlying cellular processes. It is expected that comparison and alignment of biological networks will have a similar impact on our understanding of evolution, biological function, and disease as did sequence comparison and alignment. Here, we introduce a novel pairwise global alignment algorithm called Common-neighbors based GRAph ALigner (C-GRAAL) that uses heuristics for maximizing the number of aligned edges between two networks and is based solely on network topology. As such, it can be applied to any type of network, such as social, transportation, or electrical networks. We apply C-GRAAL to align PPI networks of eukaryotic and prokaryotic species, as well as inter-species PPI networks, and we demonstrate that the resulting alignments expose large connected and functionally topologically aligned regions. We use the resulting alignments to transfer biological knowledge across species, successfully validating many of the predictions. Moreover, we show that C-GRAAL can be used to align human-pathogen inter-species PPI networks and that it can identify patterns of pathogen interactions with host proteins solely from network topology.

  19. Networks and their applications to biological systems: From ecological dynamics to gene regulation

    Science.gov (United States)

    Sevim, Volkan

    In this dissertation, we study three biological applications of networks. The first one is a biological coevolution model, in which a species is defined by a genome in the form of a finite bitstring and the interactions between species are given by a fixed matrix with randomly distributed elements. Here we study a version of the model, in which the matrix elements are correlated to a controllable degree by means of an averaging scheme. This method allows creation of mutants resembling their ancestors (wildtype). We compare long kinetic Monte Carlo simulations of models with uncorrelated and correlated interactions. We find that while there are quantitative differences, most qualitative features, such as 1/f behavior in power spectral densities for the diversity indices and the power-law distribution of species lifetimes, are not significantly affected by the correlations in the interaction matrix. The second application is the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. This is a new preferential-attachment scheme, in which a new node attaches to an existing node i with probability pi(k i, k'i ) ∝ ( k'i /ki)gamma, where ki and k'i are the number of outgoing and incoming links at i, respectively, and gamma is a constant. First, we calculate the degree distribution for the outgoing links for a simplified form of this function, pi( ki) ∝ k-1i , both analytically and by Monte Carlo simulations. The distribution decays like kmuk/Gamma(k) for large k, where mu is a constant. We relate this mechanism to simple food-web models by implementing it in the cascade model. We also study the generalized case, pi(ki, k'i ) ∝ ( k'i /ki)gamma, by simulations. The third application is the evolution of robustness to mutations and noise in gene regulatory networks. It has been shown that robustness to mutations and noise can evolve through stabilizing selection for optimal phenotypes in model gene regulatory

  20. Research on Multi-Layer Distributed HF Radio Network Structure

    Institute of Scientific and Technical Information of China (English)

    Hui Dai; Chun-Jiang Wang; Quan Yu

    2008-01-01

    High frequency (HF) transmission is an important communication techniques. However, conventional point-to-point transmission can be easily destroyed, which limits its utilization in practice. HF networking communication has the capability against demolishment. The network structure is one of the key factors for HF networking communication. In this paper, a novel analysis method of the network connectedness based on the eigenvalue is derived, and a multi-layer distributed HF radio network structure is proposed. Both the theore tical analysis and the computer simulation results verify that the application of the proposed network structure in the HF radio communication can improve the anti demolishment ability of the HF network efficiently.

  1. Effect of edge pruning on structural controllability and observability of complex networks.

    Science.gov (United States)

    Mengiste, Simachew Abebe; Aertsen, Ad; Kumar, Arvind

    2015-12-17

    Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex systems show dynamic changes in their network connectivity, it is important to understand how perturbations in the connectivity affect the controllability of the system. To this end, we studied the control structure of different types of artificial, social and biological neuronal networks (BNN) as their connections were progressively pruned using four different pruning strategies. We show that the BNNs are more similar to scale-free networks than to small-world networks, when comparing the robustness of their control structure to structural perturbations. We introduce a new graph descriptor, 'the cardinality curve', to quantify the robustness of the control structure of a network to progressive edge pruning. Knowing the susceptibility of control structures to different pruning methods could help design strategies to destroy the control structures of dangerous networks such as epidemic networks. On the other hand, it could help make useful networks more resistant to edge attacks.

  2. Learning Bayesian network structure with immune algorithm

    Institute of Scientific and Technical Information of China (English)

    Zhiqiang Cai; Shubin Si; Shudong Sun; Hongyan Dui

    2015-01-01

    Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This pa-per proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Further-more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Final y, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

  3. Mass Spec Studio for Integrative Structural Biology

    NARCIS (Netherlands)

    Rey, Martial; Sarpe, Vladimir; Burns, Kyle M.; Buse, Joshua; Baker, Charles A. H.; van Dijk, Marc; Wordeman, Linda; Bonvin, Alexandre M. J. J.; Schriemer, David C.

    2014-01-01

    The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and

  4. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

    Science.gov (United States)

    Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

    2017-02-01

    Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

  5. Higher-order structure and epidemic dynamics in clustered networks

    CERN Document Server

    Ritchie, Martin; House, Thomas; Kiss, Istvan Z

    2013-01-01

    Clustering is typically measured by the ratio of triangles to all triples, open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks \\cite{vmclust, karrerclust2010}, e.g., networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network's topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous netwo...

  6. Structural biology of bacterial RNA polymerase.

    Science.gov (United States)

    Murakami, Katsuhiko S

    2015-05-11

    Since its discovery and characterization in the early 1960s (Hurwitz, J. The discovery of RNA polymerase. J. Biol. Chem. 2005, 280, 42477-42485), an enormous amount of biochemical, biophysical and genetic data has been collected on bacterial RNA polymerase (RNAP). In the late 1990s, structural information pertaining to bacterial RNAP has emerged that provided unprecedented insights into the function and mechanism of RNA transcription. In this review, I list all structures related to bacterial RNAP (as determined by X-ray crystallography and NMR methods available from the Protein Data Bank), describe their contributions to bacterial transcription research and discuss the role that small molecules play in inhibiting bacterial RNA transcription.

  7. Wham: Identifying Structural Variants of Biological Consequence.

    Directory of Open Access Journals (Sweden)

    Zev N Kronenberg

    2015-12-01

    Full Text Available Existing methods for identifying structural variants (SVs from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools-Lumpy, Delly and SoftSearch-and demonstrate Wham's ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham, with documentation on a wiki (http://zeeev.github.io/wham/. For community support please post questions to https://www.biostars.org/.

  8. Wham: Identifying Structural Variants of Biological Consequence.

    Science.gov (United States)

    Kronenberg, Zev N; Osborne, Edward J; Cone, Kelsey R; Kennedy, Brett J; Domyan, Eric T; Shapiro, Michael D; Elde, Nels C; Yandell, Mark

    2015-12-01

    Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools-Lumpy, Delly and SoftSearch-and demonstrate Wham's ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.

  9. Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

    Energy Technology Data Exchange (ETDEWEB)

    Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

    1996-12-31

    This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

  10. Adaptation of coordination mechanisms to network structures

    Directory of Open Access Journals (Sweden)

    Herwig Mittermayer

    2008-12-01

    Full Text Available The coordination efficiency of Supply Chain Management is determined by two opposite poles: benefit from improved planning results and associated coordination cost. The centralization grade, applied coordination mechanisms and IT support have influence on both categories. Therefore three reference types are developed and subsequently detailed in business process models for different network structures. In a simulation study the performance of these organization forms are compared in a process plant network. Coordination benefit is observed if the planning mode is altered by means of a demand planning IT tool. Coordination cost is divided into structural and activity-dependent cost. The activity level rises when reactive planning iterations become necessary as a consequence of inconsistencies among planning levels. Some characteristic influence factors are considered to be a reason for uninfeasible planning. In this study the effect of capacity availability and stochastic machine downtimes is investigated in an uncertain demand situation. Results that if the network runs with high overcapacity, central planning is less likely to increase benefit enough to outweigh associated cost. Otherwise, if capacity constraints are crucial, a central planning mode is recommendable. When also unforeseen machine downtimes are low, the use of sophisticated IT tools is most profitable.

  11. The prescribed output pattern regulates the modular structure of flow networks

    Science.gov (United States)

    Emanuel Beber, Moritz; Armbruster, Dieter; Hütt, Marc-Thorsten

    2013-11-01

    Modules are common functional and structural properties of many social, technical and biological networks. Especially for biological systems it is important to understand how modularity is related to function and how modularity evolves. It is known that time-varying or spatially organized goals can lead to modularity in a simulated evolution of signaling networks. Here, we study a minimal model of material flow in networks. We discuss the relation between the shared use of nodes, i.e., the cooperativity of modules, and the orthogonality of a prescribed output pattern. We study the persistence of cooperativity through an evolution of robustness against local damages. We expect the results to be valid for a large class of flow-based biological and technical networks. Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2013-40672-3

  12. Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

    DEFF Research Database (Denmark)

    Chang, Roger L.; Andrews, Kathleen; Kim, Donghyuk

    2013-01-01

    Improve the System A "systems biology" approach may clarify, for example, how particular proteins determine sensitivity of bacteria to extremes of temperature. Chang et al. (p. 1220) integrated information on protein structure with a model of metabolism, thus associating the protein structure of ...

  13. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  14. A glimpse of structural biology through X-ray crystallography.

    Science.gov (United States)

    Shi, Yigong

    2014-11-20

    Since determination of the myoglobin structure in 1957, X-ray crystallography, as the anchoring tool of structural biology, has played an instrumental role in deciphering the secrets of life. Knowledge gained through X-ray crystallography has fundamentally advanced our views on cellular processes and greatly facilitated development of modern medicine. In this brief narrative, I describe my personal understanding of the evolution of structural biology through X-ray crystallography-using as examples mechanistic understanding of protein kinases and integral membrane proteins-and comment on the impact of technological development and outlook of X-ray crystallography.

  15. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Directory of Open Access Journals (Sweden)

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  16. Information diversity in structure and dynamics of simulated neuronal networks.

    Science.gov (United States)

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  17. Community structure in time-dependent, multiscale, and multiplex networks.

    Science.gov (United States)

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A; Onnela, Jukka-Pekka

    2010-05-14

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.

  18. Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

    Science.gov (United States)

    Ganscha, Stefan; Claassen, Manfred

    2016-01-01

    Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso. PMID:27923064

  19. Improving the Network Structure can lead to Functional Failures

    CERN Document Server

    Pade, Jan Philipp

    2014-01-01

    In many real-world networks the ability to synchronize is a key property for its performance. Examples include power-grid, sensor, and neuron networks as well as consensus formation. Recent work on undirected networks with diffusive interaction revealed that improvements in the network connectivity such as making the network more connected and homogeneous enhances synchronization. However, real-world networks have directed and weighted connections. In such directed networks, understanding the impact of structural changes on the network performance remains a major challenge. Here, we show that improving the structure of a directed network can lead to a failure in the network function. For instance, introducing new links to reduce the minimum distance between nodes can lead to instabilities in the synchronized motion. This counter-intuitive effect only occurs in directed networks. Our results allow to identify the dynamical importance of a link and thereby have a major impact on the design and control of direct...

  20. Network Structure Expert System and Operation Optimization

    Institute of Scientific and Technical Information of China (English)

    刘洪谦; 袁希钢; 麻德贤

    2003-01-01

    It is proposed that double level programming technique may be adopted in synthesis strategy. Optimization of heat exchanger network structural configuration (the master problem) may be solved at the upper level, leaving the rest operating conditions( the slave problem) being optimized at the lower level. With the uniqueness in mind, an HEN synthesis expert system may be employed to address both the logical constraints and the global operation parameters′ optimization using enhanced sequential number optimization theory.Case studies demonstrate that the synthesis strategy proposed can effectively simplify both the problem-solving and the synthesis process. The validity of the strategy recommended is evidenced by case studies′ results compared.

  1. 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.

  2. Mapping Transcriptional Networks in Plants: Data-Driven Discovery of Novel Biological Mechanisms.

    Science.gov (United States)

    Gaudinier, Allison; Brady, Siobhan M

    2016-04-29

    In plants, systems biology approaches have led to the generation of a variety of large data sets. Many of these data are created to elucidate gene expression profiles and their corresponding transcriptional regulatory mechanisms across a range of tissue types, organs, and environmental conditions. In an effort to map the complexity of this transcriptional regulatory control, several types of experimental assays have been used to map transcriptional regulatory networks. In this review, we discuss how these methods can be best used to identify novel biological mechanisms by focusing on the appropriate biological context. Translating network biology back to gene function in the plant, however, remains a challenge. We emphasize the need for validation and insight into the underlying biological processes to successfully exploit systems approaches in an effort to determine the emergent properties revealed by network analyses.

  3. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  4. Optimization Techniques for Analysis of Biological and Social Networks

    Science.gov (United States)

    2012-03-28

    systematic fashion under a unifying theoretical and algorithmic framework . Optimization, Complex Networks, Social Network Analysis, Computational...analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms, test and fine...exact solutions are presented. In [3], we introduce the variable objective search framework for combinatorial optimization. The method utilizes

  5. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  6. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  7. Complex quantum networks as structured environments: engineering and probing

    Science.gov (United States)

    Nokkala, Johannes; Galve, Fernando; Zambrini, Roberta; Maniscalco, Sabrina; Piilo, Jyrki

    2016-05-01

    We consider structured environments modeled by bosonic quantum networks and investigate the probing of their spectral density, structure, and topology. We demonstrate how to engineer a desired spectral density by changing the network structure. Our results show that the spectral density can be very accurately detected via a locally immersed quantum probe for virtually any network configuration. Moreover, we show how the entire network structure can be reconstructed by using a single quantum probe. We illustrate our findings presenting examples of spectral densities and topology probing for networks of genuine complexity.

  8. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

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

    Science.gov (United States)

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

    2010-01-01

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

  10. Exploiting structural biology in the fight against parasitic diseases.

    Science.gov (United States)

    Modis, Yorgo

    2012-04-01

    Despite spectacular advances in structural biology over the past half-century, only approximately 2% of the structures in the Protein Data Bank are from eukaryotic parasites and less than 0.5% are from multicellular parasites. Even when only major human pathogens are considered, 3D structures of parasites are vastly underrepresented. Yet approximately one-third of the global burden of human disease comes from parasites. It is time to divert greater effort and resources in structural biology to benefit the fight against parasitic diseases. Using as leverage recent technological and methodological advances, a concerted effort to determine macromolecular structures from parasite pathogens would provide invaluable mechanistic insights on vital processes of the parasites and would suggest novel strategies for inhibiting infection.

  11. Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data

    Institute of Scientific and Technical Information of China (English)

    Wei-Po Lee; Kung-Cheng Yang

    2008-01-01

    Constructing biological networks is one of the most important issues in system sbiology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.

  12. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  13. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  14. Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2010-01-01

    Full Text Available In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.

  15. Process Analysis in Container Shipping Network Structure Form Change

    Directory of Open Access Journals (Sweden)

    Chen Chao

    2012-03-01

    Full Text Available Being aimed at the influence of ship-size and cargo-demand changes on container shipping networks, to reveal the evolution process of container shipping networks structure form, this paper respectively designed the operation models for two major container shipping networks structure forms: Multi-port-calling network and Hub-and-spoke network, to maximizing the investment efficiency. Based on the above models, a comprehensively integrated operation model of container shipping networks is built and the evolution process of container shipping networks structure form with changing of both ship-size and cargo demands is analyzed. Finally, through a case study, results show that the comprehensive integrated operation model is very effective in the analysis of evolution process of container shipping networks structure forms.

  16. Social inheritance can explain the structure of animal social networks.

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-06-28

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

  17. Aspartate and glutamate mimetic structures in biologically active compounds.

    Science.gov (United States)

    Stefanic, Peter; Dolenc, Marija Sollner

    2004-04-01

    Glutamate and aspartate are frequently recognized as key structural elements for the biological activity of natural peptides and synthetic compounds. The acidic side-chain functionality of both the amino acids provides the basis for the ionic interaction and subsequent molecular recognition by specific receptor sites that results in the regulation of physiological or pathophysiological processes in the organism. In the development of new biologically active compounds that possess the ability to modulate these processes, compounds offering the same type of interactions are being designed. Thus, using a peptidomimetic design approach, glutamate and aspartate mimetics are incorporated into the structure of final biologically active compounds. This review covers different bioisosteric replacements of carboxylic acid alone, as well as mimetics of the whole amino acid structure. Amino acid analogs presented include those with different distances between anionic moieties, and analogs with additional functional groups that result in conformational restriction or alternative interaction sites. The article also provides an overview of different cyclic structures, including various cycloalkane, bicyclic and heterocyclic analogs, that lead to conformational restriction. Higher di- and tripeptide mimetics in which carboxylic acid functionality is incorporated into larger molecules are also reviewed. In addition to the mimetic structures presented, emphasis in this article is placed on their steric and electronic properties. These mimetics constitute a useful pool of fragments in the design of new biologically active compounds, particularly in the field of RGD mimetics and excitatory amino acid agonists and antagonists.

  18. RENEB : running the European network of biological dosimetry and physical retrospective dosimetry

    OpenAIRE

    Kulka, Ulrike; Abend, Michael; Ainsbury, Elizabeth; Badie, Christophe; Francesc Barquinero, Joan; Barrios, Lleonard; Beinke, Christina; Bortolin, Emanuela; Cucu, Alexandra; De Amicis, Andrea; Domínguez, Inmaculada; Fattibene, Paola; Frøvig, Anne Marie; Gregoire, Eric; Guogyte, Kamile

    2017-01-01

    Purpose: A European network was initiated in 2012 by 23 partners from 16 European countries with the aim to significantly increase individualized dose reconstruction in case of large-scale radiological emergency scenarios. Results: The network was built on three complementary pillars: (1) an operational basis with seven biological and physical dosimetric assays in ready-to-use mode, (2) a basis for education, training and quality assurance, and (3) a basis for further network development r...

  19. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  20. From crystallography to structural biology, a century of discoveries

    Directory of Open Access Journals (Sweden)

    Montoya, Guillermo

    2015-04-01

    Full Text Available From crystallography, the technique mostly used to study the structure of matter, the field mutated into structural biology, has mutated in life sciences into structural biology, which has been developed as an essential and rather successful area of research to fully understand the workings of cellular pathways. The application of physical approaches to biological systems has been crucial to comprehend the structure and function of the biological components of living organisms. In this assay the author walks the reader through the last century, which has witnessed how this life sciences research area was born and moved towards larger assemblies in the core of crucial biological problems. The influence of research in physics, biochemistry and molecular biology has been key in the successes and large body of seminal results obtained by structural biologists. The author proposes that the future of this area implies the integration of its results at the cellular level apart of using more quantitative approaches to describe biological processes.La cristalografía, la técnica más ampliamente usada para estudiar la estructura de la materia, ha evolucionado en las ciencias de la vida hacia la biología estructural, una exitosa área de investigación encaminada a comprender el funcionamiento de los procesos celulares. La aplicación de aproximaciones físicas a sistemas biológicos es clave para entender la estructura y funcionamiento de los componentes de los organismos. En este artículo el autor ofrece al lector un paseo por la evolución de esta área de conocimiento durante el siglo XX, desde su nacimiento hasta el análisis de grandes complejos macromoleculares, protagonistas importantes en diversos procesos biológicos. La influencia de investigaciones en física, bioquímica y biología molecular ha sido clave para los numerosos éxitos alcanzados por biólogos estructurales. El autor sostiene que el futuro de esta disciplina pasa por la

  1. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    , with genomic modifications giving rise to differential protein dynamics, ultimately resulting in disease. The exact molecular signaling networks underlying specific disease phenotypes remain elusive, as the definition thereof requires extensive analysis of not only the genomic and proteomic landscapes within...

  2. Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2008-01-01

    Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional MCMC schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in t

  3. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

    Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

  4. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

    Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design...... of advanced cell factories for production of fuels, chemicals, food ingredients and pharmaceuticals. The yeast Saccharomyces cerevisiae represents an excellent model system; the density of biological information available on this organism allows it to serve as a eukaryotic model for studying human diseases....... Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single...

  5. Biology 23. Unit One -- The Cell: Structure and Physiology.

    Science.gov (United States)

    Nederland Independent School District, TX.

    GRADES OR AGES: Not given. SUBJECT MATTER: Biology, the structure and physiology of the cell. ORGANIZATION AND PHYSICAL APPEARANCE: There are four sections: a) objectives for the unit, b) bibliography, c) activities, and d) evaluation. The guide is directed to the student rather than the teacher. The guide is mimeographed and stapled, with no…

  6. Analysis and Design of Biological Materials and Structures

    CERN Document Server

    Öchsner, Andreas; Altenbach, Holm

    2012-01-01

    This collection provides researchers and scientists with advanced analyses and materials design techniques in Biomaterials and presents mechanical studies of biological structures. In 16 contributions well known experts present their research on Stress and Strain Analysis, Material Properties, Fluid and Gas mechanics and they show related problems.

  7. Distance metric learning for complex networks: Towards size-independent comparison of network structures

    Science.gov (United States)

    Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

    2015-02-01

    Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

  8. Evolutionary principles underlying structure and response dynamics of cellular networks.

    Science.gov (United States)

    Steinacher, Arno; Soyer, Orkun S

    2012-01-01

    The network view in systems biology, in conjunction with the continuing development of experimental technologies, is providing us with the key structural and dynamical features of both cell-wide and pathway-level regulatory, signaling and metabolic systems. These include for example modularity and presence of hub proteins at the structural level and ultrasensitivity and feedback control at the level of dynamics. The uncovering of such features, and the seeming commonality of some of them, makes many systems biologists believe that these could represent design principles that underpin cellular systems across organisms. Here, we argue that such claims on any observed feature requires an understanding of how it has emerged in evolution and how it can shape subsequent evolution. We review recent and past studies that aim to achieve such evolutionary understanding for observed features of cellular networks. We argue that this evolutionary framework could lead to deciphering evolutionary origin and relevance of proposed design principles, thereby allowing to predict their presence or absence in an organism based on its environment and biochemistry and their effect on its future evolution.

  9. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    Science.gov (United States)

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.

  10. Network-based drug discovery by integrating systems biology and computational technologies.

    Science.gov (United States)

    Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang

    2013-07-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

  11. Network catastrophe: self-organized patterns reveal both the instability and the structure of complex networks.

    Science.gov (United States)

    Moon, Hankyu; Lu, Tsai-Ching

    2015-03-30

    Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of-how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description - of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

  12. Process-based network decomposition reveals backbone motif structure.

    Science.gov (United States)

    Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

    2010-06-08

    A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated).

  13. The Evolving Contribution of Mass Spectrometry to Integrative Structural Biology

    Science.gov (United States)

    Faini, Marco; Stengel, Florian; Aebersold, Ruedi

    2016-06-01

    Protein complexes are key catalysts and regulators for the majority of cellular processes. Unveiling their assembly and structure is essential to understanding their function and mechanism of action. Although conventional structural techniques such as X-ray crystallography and NMR have solved the structure of important protein complexes, they cannot consistently deal with dynamic and heterogeneous assemblies, limiting their applications to small scale experiments. A novel methodological paradigm, integrative structural biology, aims at overcoming such limitations by combining complementary data sources into a comprehensive structural model. Recent applications have shown that a range of mass spectrometry (MS) techniques are able to generate interaction and spatial restraints (cross-linking MS) information on native complexes or to study the stoichiometry and connectivity of entire assemblies (native MS) rapidly, reliably, and from small amounts of substrate. Although these techniques by themselves do not solve structures, they do provide invaluable structural information and are thus ideally suited to contribute to integrative modeling efforts. The group of Brian Chait has made seminal contributions in the use of mass spectrometric techniques to study protein complexes. In this perspective, we honor the contributions of the Chait group and discuss concepts and milestones of integrative structural biology. We also review recent examples of integration of structural MS techniques with an emphasis on cross-linking MS. We then speculate on future MS applications that would unravel the dynamic nature of protein complexes upon diverse cellular states.

  14. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan S H

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  15. Completely random measures for modelling block-structured sparse networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2016-01-01

    Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world network...... is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.......Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re...

  16. 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

  17. Community Structure in Congressional Cosponsorship Networks

    CERN Document Server

    Zhang, Yan; Traud, Amanda L; Porter, Mason A; Fowler, James H; Mucha, Peter J

    2007-01-01

    We study the United States Congress by constructing networks between Members of Congress based on the legislation that they cosponsor. Using the concept of modularity, we identify the community structure of Congressmen, as connected via sponsorship/cosponsorship of the same legislation, to investigate the collaborative communities of legislators in both chambers of Congress. This analysis yields an explicit and conceptually clear measure of political polarization, demonstrating a sharp increase in partisan polarization which preceded and then culminated in the 104th Congress (1995-1996), when Republicans took control of both chambers. Although polarization has since waned in the U.S. Senate, it remains at historically high levels in the House of Representatives.

  18. Structural equation models from paths to networks

    CERN Document Server

    Westland, J Christopher

    2015-01-01

    This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

  19. Generalized hypercube structures and hyperswitch communication network

    Science.gov (United States)

    Young, Steven D.

    1992-01-01

    This paper discusses an ongoing study that uses a recent development in communication control technology to implement hybrid hypercube structures. These architectures are similar to binary hypercubes, but they also provide added connectivity between the processors. This added connectivity increases communication reliability while decreasing the latency of interprocessor message passing. Because these factors directly determine the speed that can be obtained by multiprocessor systems, these architectures are attractive for applications such as remote exploration and experimentation, where high performance and ultrareliability are required. This paper describes and enumerates these architectures and discusses how they can be implemented with a modified version of the hyperswitch communication network (HCN). The HCN is analyzed because it has three attractive features that enable these architectures to be effective: speed, fault tolerance, and the ability to pass multiple messages simultaneously through the same hyperswitch controller.

  20. Evolving networks-Using past structure to predict the future

    Science.gov (United States)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  1. Universal structural estimator and dynamics approximator for complex networks

    CERN Document Server

    Chen, Yu-Zhong

    2016-01-01

    Revealing the structure and dynamics of complex networked systems from observed data is of fundamental importance to science, engineering, and society. Is it possible to develop a universal, completely data driven framework to decipher the network structure and different types of dynamical processes on complex networks, regardless of their details? We develop a Markov network based model, sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator. The SDBM attains its topology according to that of the original system and is capable of simulating the original dynamical process. We develop a fully automated method based on compressive sensing and machine learning to find the SDBM. We demonstrate, for a large variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and predicts its dynamical behavior with high precision.

  2. Network nestedness as generalized core-periphery structures

    CERN Document Server

    Lee, Sang Hoon

    2016-01-01

    The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness, by defining the network-level measures for nestedness and core-peripheriness in case of weighted and bipartite networks. However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-peripheriness parameter space. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structur...

  3. On protocols and measures for the validation of supervised methods for the inference of biological networks

    Directory of Open Access Journals (Sweden)

    Marie eSchrynemackers

    2013-12-01

    Full Text Available Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature.In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.

  4. Making the right connections: Network biology and plant immune system dynamics

    Directory of Open Access Journals (Sweden)

    Maggie E. McCormack

    2016-04-01

    Full Text Available Network analysis has been a recent focus in biological sciences due to its ability to synthesize global visualizations of cellular processes and predict functions based on inferences from network properties. A protein–protein interaction network, or interactome, captures the emergent cellular states from gene regulation and environmental conditions. Given that proteins are involved in extensive local and systemic molecular interactions such as signaling and metabolism, understanding protein functions and interactions are essential for a systems view of biology. However, in plant sciences these network-based approaches to data integration have been few and far between due to limited data, especially protein–protein interaction data. In this review, we cover network construction from experimental data, network analysis based on topological properties, and finally we discuss advances in networks in plants and other organisms in a comparative approach. We focus on applications of network biology to discover the dynamics of host–pathogen interactions as these have potential agricultural uses in improving disease resistance in commercial crops.

  5. Neutron scattering applications in structural biology: now and the future

    Energy Technology Data Exchange (ETDEWEB)

    Trewhella, J. [Los Alamos National Lab., NM (United States)

    1996-05-01

    Neutrons have an important role to play in structural biology. Neutron crystallography, small-angle neutron scattering and inelastic neutron scattering techniques all contribute unique information on biomolecular structures. In particular, solution scattering techniques give critical information on the conformations and dispositions of the components of complex assemblies under a wide variety of relevant conditions. The power of these methods is demonstrated here by studies of protein/DNA complexes, and Ca{sup 2+}-binding proteins complexed with their regulatory targets. In addition, we demonstrate the utility of a new structural approach using neutron resonance scattering. The impact of biological neutron scattering to date has been constrained principally by the available fluxes at neutron sources and the true potential of these approaches will only be realized with the development of new more powerful neutron sources. (author)

  6. Electron crystallography--the waking beauty of structural biology.

    Science.gov (United States)

    Pope, Christopher R; Unger, Vinzenz M

    2012-08-01

    Since its debut in the mid 1970s, electron crystallography has been a valuable alternative in the structure determination of biological macromolecules. Its reliance on single-layered or double-layered two-dimensionally ordered arrays and the ability to obtain structural information from small and disordered crystals make this approach particularly useful for the study of membrane proteins in a lipid bilayer environment. Despite its unique advantages, technological hurdles have kept electron crystallography from reaching its full potential. Addressing the issues, recent initiatives developed high-throughput pipelines for crystallization and screening. Adding progress in automating data collection, image analysis and phase extension methods, electron crystallography is poised to raise its profile and may lead the way in exploring the structural biology of macromolecular complexes.

  7. A structural biology perspective on NMDA receptor pharmacology and function.

    Science.gov (United States)

    Regan, Michael C; Romero-Hernandez, Annabel; Furukawa, Hiro

    2015-08-01

    N-methyld-aspartate receptors (NMDARs) belong to the large family of ionotropic glutamate receptors (iGluRs), which are critically involved in basic brain functions as well as multiple neurological diseases and disorders. The NMDARs are large heterotetrameric membrane protein complexes. The extensive extracellular domains recognize neurotransmitter ligands and allosteric compounds and translate the binding information to regulate activity of the transmembrane ion channel. Here, we review recent advances in the structural biology of NMDARs with a focus on pharmacology and function. Structural analysis of the isolated extracellular domains in combination with the intact heterotetrameric NMDAR structure provides important insights into how this sophisticated ligand-gated ion channel may function.

  8. Opinion dynamics on a group structured adaptive network

    CERN Document Server

    Gargiulo, F

    2009-01-01

    Many models have been proposed to analyze the evolution of opinion structure due to the interaction of individuals in their social environment. Such models analyze the spreading of ideas both in completely interacting backgrounds and on social networks, where each person has a finite set of interlocutors.Moreover also the investigation on the topological structure of social networks has been object of several analysis, both from the theoretical and the empirical point of view. In this framework a particularly important area of study regards the community structure inside social networks.In this paper we analyze the reciprocal feedback between the opinions of the individuals and the structure of the interpersonal relationships at the level of community structures. For this purpose we define a group based random network and we study how this structure co-evolve with opinion dynamics processes. We observe that the adaptive network structure affects the opinion dynamics process helping the consensus formation. Th...

  9. A network biology model of micronutrient related health

    NARCIS (Netherlands)

    Ommen, B. van; Fairweather-Tait, S.; Freidig, A.; Kardinaal, A.; Scalbert, A.; Wopereis, S.

    2008-01-01

    Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise healt

  10. Biologically Inspired Target Recognition in Radar Sensor Networks

    Directory of Open Access Journals (Sweden)

    Liang Qilian

    2010-01-01

    Full Text Available One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC. Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.

  11. Integration of biological networks and gene expression data using Cytoscape

    DEFF Research Database (Denmark)

    Cline, M.S.; Smoot, M.; Cerami, E.

    2007-01-01

    Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...

  12. Modeling Wireless Sensor Networks for Monitoring in Biological Processes

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil

    parameters, as the use of wired sensors is impractical. In this thesis, a ZigBee based wireless sensor network was employed and only a part of the herd was monitored, as monitoring each individual animal in a large herd under practical conditions is inefficient. Investigations to show that the monitored...

  13. Health and the Structure of Adolescent Social Networks

    Science.gov (United States)

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  14. Functional Genomics Assistant (FUGA: a toolbox for the analysis of complex biological networks

    Directory of Open Access Journals (Sweden)

    Ouzounis Christos A

    2011-10-01

    Full Text Available Abstract Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.

  15. BiNA: a visual analytics tool for biological network data.

    Directory of Open Access Journals (Sweden)

    Andreas Gerasch

    Full Text Available Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA--the Biological Network Analyzer--a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

  16. Reverse Logistics Network Structures and Design

    NARCIS (Netherlands)

    M. Fleischmann (Moritz)

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network desi

  17. Structure-function clustering in multiplex brain networks

    Science.gov (United States)

    Crofts, J. J.; Forrester, M.; O'Dea, R. D.

    2016-10-01

    A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked “functional connectivity” and the underlying “structural connectivity” has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.

  18. Insights into the biology of Escherichia coli through structural proteomics.

    Science.gov (United States)

    Matte, Allan; Jia, Zongchao; Sunita, S; Sivaraman, J; Cygler, Miroslaw

    2007-09-01

    Escherichia coli has historically been an important organism for understanding a multitude of biological processes, and represents a model system as we attempt to simulate the workings of living cells. Many E. coli strains are also important human and animal pathogens for which new therapeutic strategies are required. For both reasons, a more complete and comprehensive understanding of the protein structure complement of E. coli is needed at the genome level. Here, we provide examples of insights into the mechanism and function of bacterial proteins that we have gained through the Bacterial Structural Genomics Initiative (BSGI), focused on medium-throughput structure determination of proteins from E. coli. We describe the structural characterization of several enzymes from the histidine biosynthetic pathway, the structures of three pseudouridine synthases, enzymes that synthesize one of the most abundant modified bases in RNA, as well as the combined use of protein structure and focused functional analysis to decipher functions for hypothetical proteins. Together, these results illustrate the power of structural genomics to contribute to a deeper biological understanding of bacterial processes.

  19. Disulfide bond formation network in the three biological kingdoms, bacteria, fungi and mammals.

    Science.gov (United States)

    Sato, Yoshimi; Inaba, Kenji

    2012-07-01

    Almost all organisms, from bacteria to humans, possess catalytic systems that promote disulfide bond formation-coupled protein folding, i.e. oxidative protein folding. These systems are necessary for the biosynthesis of many secretory and membrane proteins, such as antibodies, major histocompatibility complex molecules, growth factors, and insulin. Over the last decade, structural studies have made striking progress in this field of research, identifying how oxidative systems operate in a specific and regulated manner to maintain redox and protein homeostasis within cells. Interestingly, more and more novel catalysts that promote disulfide bond formation have been discovered in mammals, suggesting that the oxidative protein folding network is even more complicated in higher eukaryotes than previously thought. This review highlights the physiological roles and molecular bases of the disulfide bond formation pathways that have evolved in the bacterial periplasm and the endoplasmic reticulum of fungi and mammals. Accumulating knowledge about disulfide bond formation networks widely distributed throughout the biological kingdom has significantly advanced our understanding of the cellular mechanisms dedicated to protein quality control.

  20. Semantic data integration and knowledge management to represent biological network associations.

    Science.gov (United States)

    Losko, Sascha; Heumann, Klaus

    2009-01-01

    The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data including experimental data from "-omics" platforms, phenotype information, and clinical data. For bioinformatics, several challenges remain: to structure this information as biological networks enabling scientists to identify relevant information; to integrate this information as specific "knowledge bases"; and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation and, thus, the generation of new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we will introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.

  1. Studies on Hydrogen Bonding Network Structures of Konjac Glucomannan

    Institute of Scientific and Technical Information of China (English)

    PANG Jie; SUN Yu-Jing; YANG You-Hui; CHEN Yuan-Yuan; CHEN Yi-Qing; SUN Yuan-Ming

    2008-01-01

    In this paper, the hydrogen bonding network models of konjac glucomannan (KGM) are predicted in the approach of molecular dynamics (MD). These models have been proved by experiments whose results are consistent with those from simulation.The results show that the hydrogen bonding network structures of KGM are stable and the key linking points of hydrogen bonding network are at the O(6) and O(2) positions on KGM ring. Moreover, acety1 has significant influence on hydrogen bonding network and hydrogen bonding network structures are more stable after deacetylation.

  2. The fractal octahedron network of the large scale structure

    CERN Document Server

    Battaner, E

    1998-01-01

    In a previous article, we have proposed that the large scale structure network generated by large scale magnetic fields could consist of a network of octahedra only contacting at their vertexes. Assuming such a network could arise at different scales producing a fractal geometry, we study here its properties, and in particular how a sub-octahedron network can be inserted within an octahedron of the large network. We deduce that the scale of the fractal structure would range from $\\approx$100 Mpc, i.e. the scale of the deepest surveys, down to about 10 Mpc, as other smaller scale magnetic fields were probably destroyed in the radiation dominated Universe.

  3. Structure function relationship in complex brain networks expressed by hierarchical synchronization

    Science.gov (United States)

    Zhou, Changsong; Zemanová, Lucia; Zamora-López, Gorka; Hilgetag, Claus C.; Kurths, Jürgen

    2007-06-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  4. Physics textbooks from the viewpoint of network structures

    Science.gov (United States)

    Králiková, Petra; Teleki, Aba

    2017-01-01

    We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

  5. Optimizing controllability of complex networks by minimum structural perturbations.

    Science.gov (United States)

    Wang, Wen-Xu; Ni, Xuan; Lai, Ying-Cheng; Grebogi, Celso

    2012-02-01

    To drive a large, complex, networked dynamical system toward some desired state using as few external signals as possible is a fundamental issue in the emerging field of controlling complex networks. Optimal control is referred to the situation where such a network can be fully controlled using only one driving signal. We propose a general approach to optimizing the controllability of complex networks by judiciously perturbing the network structure. The principle of our perturbation method is validated theoretically and demonstrated numerically for homogeneous and heterogeneous random networks and for different types of real networks as well. The applicability of our method is discussed in terms of the relative costs of establishing links and imposing external controllers. Besides the practical usage of our approach, its implementation elucidates, interestingly, the intricate relationship between certain structural properties of the network and its controllability.

  6. Comparative Traffic Performance Analysis of Urban Transportation Network Structures

    CERN Document Server

    Amini, Behnam; Mojarradi, Morteza; Derrible, Sybil

    2015-01-01

    The network structure of an urban transportation system has a significant impact on its traffic performance. This study uses network indicators along with several traffic performance measures including speed, trip length, travel time, and traffic volume, to compare a selection of seven transportation networks with a variety of structures and under different travel demand conditions. The selected network structures are: modified linear, branch, grid, 3-directional grid, 1-ring web, 2-ring web, and radial. For the analysis, a base origin-destination matrix is chosen, to which different growth factors are applied in order to simulate various travel demand conditions. Results show that overall the 2-ring web network offers the most efficient traffic performance, followed by the grid and the 1-ring networks. A policy application of this study is that the branch, 3-directional grid, and radial networks are mostly suited for small cities with uncongested traffic conditions. In contrast, the 2-ring web, grid, and 1-r...

  7. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  8. Influence of choice of null network on small-world parameters of structural correlation networks.

    Science.gov (United States)

    Hosseini, S M Hadi; Kesler, Shelli R

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

  9. The relevance of network micro-structure for neural dynamics

    Science.gov (United States)

    Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

    2013-01-01

    The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits. PMID:23761758

  10. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    Energy Technology Data Exchange (ETDEWEB)

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  11. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

    Science.gov (United States)

    Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

    2012-01-01

    Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

  12. 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.

  13. Polysaccharides of higher fungi: Biological role, structure, and antioxidative activity

    Directory of Open Access Journals (Sweden)

    Kozarski Maja S.

    2014-01-01

    Full Text Available Fungal polysaccharides attract a lot of attention due to their multiple challenging biological properties, such as: anti-tumor, anti-viral, anticomplementary, anticoagulant, hypolipidemic and immunomodulatory and immune-stimulatory activities, which all together make them suitable for application in many quite distinctive areas, such as food industry, biomedicine, cosmetology, agriculture, environmental protection and waste water management. This article presents results with respect to biological properties, structure and procedures related to the isolation and activation of polysaccharides of higher fungi. It is considered and presented along with a review of the critical antioxidative activity and possible influence of the structural composition of polysaccharide extracts (isolated from these higher fungi upon their antioxidative properties.

  14. Image-Based Structural Modeling of the Cardiac Purkinje Network

    Directory of Open Access Journals (Sweden)

    Benjamin R. Liu

    2015-01-01

    Full Text Available The Purkinje network is a specialized conduction system within the heart that ensures the proper activation of the ventricles to produce effective contraction. Its role during ventricular arrhythmias is less clear, but some experimental studies have suggested that the Purkinje network may significantly affect the genesis and maintenance of ventricular arrhythmias. Despite its importance, few structural models of the Purkinje network have been developed, primarily because current physical limitations prevent examination of the intact Purkinje network. In previous modeling efforts Purkinje-like structures have been developed through either automated or hand-drawn procedures, but these networks have been created according to general principles rather than based on real networks. To allow for greater realism in Purkinje structural models, we present a method for creating three-dimensional Purkinje networks based directly on imaging data. Our approach uses Purkinje network structures extracted from photographs of dissected ventricles and projects these flat networks onto realistic endocardial surfaces. Using this method, we create models for the combined ventricle-Purkinje system that can fully activate the ventricles through a stimulus delivered to the Purkinje network and can produce simulated activation sequences that match experimental observations. The combined models have the potential to help elucidate Purkinje network contributions during ventricular arrhythmias.

  15. High Energy Density Nastic Structures Using Biological Transport Mechanisms

    Science.gov (United States)

    2007-02-28

    occur at the cell wall and membranes of inter-cellular organelles to transport nutrients in the plant. The concentration of ions from the active and... Planta , Vol. 179, June 1989, pp. 32–42. [4] Delrot, S., Atanassova, R., Gomes, E., and Thevenot, P., “Plasma Membrane Transporters : A Machinery...DATES COVERED Final Progress Report; 9/27/04 to 11/30/06 4. TITLE AND SUBTITLE High Energy Density Nastic Structures Using Biological Transport

  16. The potential for biological structure determination with pulsed neutrons

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, C.C. [CLRC Rutherford Appleton Laboratory, Chilton Didcot Oxon (United Kingdom)

    1994-12-31

    The potential of pulsed neutron diffraction in structural determination of biological materials is discussed. The problems and potential solutions in this area are outlined, with reference to both current and future sources and instrumentation. The importance of developing instrumentation on pulsed sources in emphasized, with reference to the likelihood of future expansion in this area. The possibilities and limitations of single crystal, fiber and powder diffraction in this area are assessed.

  17. The BIOSCI electronic newsgroup network for the biological sciences. Final report, October 1, 1992--June 30, 1996

    Energy Technology Data Exchange (ETDEWEB)

    Kristofferson, D.; Mack, D.

    1996-10-01

    This is the final report for a DOE funded project on BIOSCI Electronic Newsgroup Network for the biological sciences. A usable network for scientific discussion, major announcements, problem solving, etc. has been created.

  18. Ethnicity and population structure in personal naming networks.

    Science.gov (United States)

    Mateos, Pablo; Longley, Paul A; O'Sullivan, David

    2011-01-01

    Personal naming practices exist in all human groups and are far from random. Rather, they continue to reflect social norms and ethno-cultural customs that have developed over generations. As a consequence, contemporary name frequency distributions retain distinct geographic, social and ethno-cultural patterning that can be exploited to understand population structure in human biology, public health and social science. Previous attempts to detect and delineate such structure in large populations have entailed extensive empirical analysis of naming conventions in different parts of the world without seeking any general or automated methods of population classification by ethno-cultural origin. Here we show how 'naming networks', constructed from forename-surname pairs of a large sample of the contemporary human population in 17 countries, provide a valuable representation of cultural, ethnic and linguistic population structure around the world. This innovative approach enriches and adds value to automated population classification through conventional national data sources such as telephone directories and electoral registers. The method identifies clear social and ethno-cultural clusters in such naming networks that extend far beyond the geographic areas in which particular names originated, and that are preserved even after international migration. Moreover, one of the most striking findings of this approach is that these clusters simply 'emerge' from the aggregation of millions of individual decisions on parental naming practices for their children, without any prior knowledge introduced by the researcher. Our probabilistic approach to community assignment, both at city level as well as at a global scale, helps to reveal the degree of isolation, integration or overlap between human populations in our rapidly globalising world. As such, this work has important implications for research in population genetics, public health, and social science adding new

  19. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics.

    Science.gov (United States)

    Bruno, Jennifer Lynn; Hosseini, S M Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira Michelle; Reiss, Allan L

    2016-03-22

    Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.

  20. An Approach to Structural Approximation Analysis by Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    陆金桂; 周济; 王浩; 陈新度; 余俊; 肖世德

    1994-01-01

    This paper theoretically proves that a three-layer neural network can be applied to implementing exactly the function between the stresses and displacements and the design variables of any elastic structure based on the Kolmogorov’s mapping neural network existence theorem. A new approach to the structural approximation analysis with the global characteristic based on artificial neural networks is presented. The computer simulation experiments made by this paper show that the new approach is effective.

  1. Modeling community structure and topics in dynamic text networks

    CERN Document Server

    Henry, Teague; Chai, Christine; Owens-Oas, Derek

    2016-01-01

    The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.

  2. Biologic

    CERN Document Server

    Kauffman, L H

    2002-01-01

    In this paper we explore the boundary between biology and the study of formal systems (logic). In the end, we arrive at a summary formalism, a chapter in "boundary mathematics" where there are not only containers but also extainers ><, entities open to interaction and distinguishing the space that they are not. The boundary algebra of containers and extainers is to biologic what boolean algebra is to classical logic. We show how this formalism encompasses significant parts of the logic of DNA replication, the Dirac formalism for quantum mechanics, formalisms for protein folding and the basic structure of the Temperley Lieb algebra at the foundations of topological invariants of knots and links.

  3. Bayesian Network Enhanced with Structural Reliability Methods: Methodology

    OpenAIRE

    Straub, Daniel; Der Kiureghian, Armen

    2012-01-01

    We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in infrastructure and structural systems, and they facilitate Bayesian updating of the model when new information becomes available. On the other hand, SR...

  4. Statistical Inference for Detecting Structures and Anomalies in Networks

    Science.gov (United States)

    2015-08-27

    AFRL-AFOSR-VA-TR-2015-0262 Statistical Inference for Detecting Structures and Anomalies in Networks Cris Moore SANTA FE INSTITUTE OF SCIENCE INC...AND SUBTITLE Statistical Inference for Detecting Structures and Anomalies in Networks 5a. CONTRACT NUMBER N/A 5b. GRANT NUMBER FA9550-12-1-0432 5c...developing powerful and scalable Bayesian statistical and related inference methods for community structure, hierarchies, core-periphery structure

  5. Theoretical Neuroanatomy:Analyzing the Structure, Dynamics,and Function of Neuronal Networks

    Science.gov (United States)

    Seth, Anil K.; Edelman, Gerald M.

    The mammalian brain is an extraordinary object: its networks give rise to our conscious experiences as well as to the generation of adaptive behavior for the organism within its environment. Progress in understanding the structure, dynamics and function of the brain faces many challenges. Biological neural networks change over time, their detailed structure is difficult to elucidate, and they are highly heterogeneous both in their neuronal units and synaptic connections. In facing these challenges, graph-theoretic and information-theoretic approaches have yielded a number of useful insights and promise many more.

  6. An appraisal of biological responses and network of environmental interactions in non-mining and mining impacted coastal waters

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, C.E.G.; Malik, A; Jineesh, V.K.; Fernandes, S.O.; Das, A; Pandey, S.S.; Kanolkar, G.; Sujith, P.P.; Velip, D.; Shaikh, S.; Helekar, S.; Gonsalves, M.J.B.D.; Nair, S.; LokaBharathi, P.A

    iron brought from the hinterlands. We hypothesize that there could be a shift in biological response along with changes in network of interactions between environmental and biological variables in these mining and non-mining impacted regions, lying 160...

  7. Transmission electron microscopy in molecular structural biology: A historical survey.

    Science.gov (United States)

    Harris, J Robin

    2015-09-01

    In this personal, historic account of macromolecular transmission electron microscopy (TEM), published data from the 1940s through to recent times is surveyed, within the context of the remarkable progress that has been achieved during this time period. The evolution of present day molecular structural biology is described in relation to the associated biological disciplines. The contribution of numerous electron microscope pioneers to the development of the subject is discussed. The principal techniques for TEM specimen preparation, thin sectioning, metal shadowing, negative staining and plunge-freezing (vitrification) of thin aqueous samples are described, with a selection of published images to emphasise the virtues of each method. The development of digital image analysis and 3D reconstruction is described in detail as applied to electron crystallography and reconstructions from helical structures, 2D membrane crystals as well as single particle 3D reconstruction of icosahedral viruses and macromolecules. The on-going development of new software, algorithms and approaches is highlighted before specific examples of the historical progress of the structural biology of proteins and viruses are presented.

  8. The structural biology of enzymes involved in natural product glycosylation.

    Science.gov (United States)

    Singh, Shanteri; Phillips, George N; Thorson, Jon S

    2012-10-01

    The glycosylation of microbial natural products often dramatically influences the biological and/or pharmacological activities of the parental metabolite. Over the past decade, crystal structures of several enzymes involved in the biosynthesis and attachment of novel sugars found appended to natural products have emerged. In many cases, these studies have paved the way to a better understanding of the corresponding enzyme mechanism of action and have served as a starting point for engineering variant enzymes to facilitate to production of differentially-glycosylated natural products. This review specifically summarizes the structural studies of bacterial enzymes involved in biosynthesis of novel sugar nucleotides.

  9. Synthesis, crystal structure and biological activity of novel diester cyclophanes

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Pengfei; Yang, Bingqin; Fang, Xianwen; Cheng, Zhao; Yang, Meipan, E-mail: yangbq@nwu.edu.cn [Department of Chemistry, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry, Northwest University, Shaanxi (China)

    2012-10-15

    A series of novel diester cyclophanes was synthesized by esterification of 1,2-benzenedicarbonyl chloride with eight different diols under high dilution conditions. The structures of the compounds were verified by elemental analysis, {sup 1}H nuclear magnetic resonance (NMR), IR spectroscopy and high resolution mass spectrometry (HRMS). The crystal structures of two compounds were characterized by single crystal X-ray diffractometry (XRD). All the new cyclophanes were evaluated for biological activities and the results showed that some of these compounds have low antibacterial or antifungal activities (author)

  10. Magnetic Micro/Nano Structures for Biological Manipulation

    Science.gov (United States)

    Huang, Chen-Yu; Hsieh, Teng-Fu; Chang, Wei-Chieh; Yeh, Kun-Chieh; Hsu, Ming-Shinn; Chang, Ching-Ray; Chen, Jiann-Yeu; Wei, Zung-Hang

    2016-05-01

    Biomanipulation based on micro/nano structures is an attractive approach for biotechnology. To manipulate biological systems by magnetic forces, the magnetic labeling technology utilized magnetic nanoparticles (MNPs) as a common rule. Ferrofluid, well-dispersed MNPs, can be used for magnetic modification of the surface or as molds to form organized microstructures. For magnetic-based micro/nano structures, different methods to modulate magnetic field at the microscale have been developed. Specifically, this review focused on a new strategy which uses the concept of micromagnetism of patterned magnetic thin film with specific domain walls configurations to generate stable magnetic poles for cell patterning.

  11. Network Analysis of Cosmic Structures : Network Centrality and Topological Environment

    CERN Document Server

    Hong, Sungryong

    2015-01-01

    We apply simple analyses techniques developed for the study of complex networks to the study of the cosmic web, the large scale galaxy distribution. In this paper, we measure three network centralities (ranks of topological importance), Degree Centrality (DC), Closeness Centrality (CL), and Betweenness Centrality (BC) from a network built from the Cosmological Evolution Survey (COSMOS) catalog. We define 8 galaxy populations according to the centrality measures; Void, Wall, and Cluster by DC, Main Branch and Dangling Leaf by BC, and Kernel, Backbone, and Fracture by CL. We also define three populations by voronoi tessellation density to compare these with the DC selection. We apply the topological selections to galaxies in the (photometric) redshift range $0.91

  12. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  13. Network versus portfolio structure in financial systems

    Science.gov (United States)

    Kobayashi, Teruyoshi

    2013-10-01

    The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007-2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011)] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks’ balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined “infectiveness” of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.

  14. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  15. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Directory of Open Access Journals (Sweden)

    Victor Trevino

    2016-04-01

    Full Text Available The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell

  16. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Science.gov (United States)

    Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antczak, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; Guindani, Michele; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

    2016-04-01

    The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks

  17. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

    Science.gov (United States)

    Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antzack, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J.; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

    2016-01-01

    Abstract The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication

  18. Cooperativity governs the size and structure of biological interfaces.

    Science.gov (United States)

    Qin, Zhao; Buehler, Markus J

    2012-11-15

    Interfaces, defined as the surface of interactions between two parts of a system at a discontinuity, are very widely found in nature. While it is known that the specific structure of an interface plays an important role in defining its properties, it is less clear whether or not there exist universal scaling laws that govern the structural evolution of a very broad range of natural interfaces. Here we show that cooperativity of interacting elements, leading to great strength at low material use, is a key concept that governs the structural evolution of many natural interfaces. We demonstrate this concept for the cases of β-sheet proteins in spider silk, gecko feet, legs of caterpillars, and self-assembling of penguins into huddles, which range in scales from the submolecular to the macroscopic level. A general model is proposed that explains the size and structure of biological interfaces from a fundamental point of view.

  19. Structuring successful collaboration: a longitudinal social network analysis of a translational research network

    OpenAIRE

    Long, Janet C; Hibbert, Peter; Braithwaite, Jeffrey

    2016-01-01

    Background In 2012 and 2013, we conducted a social network survey of a new translational research network (TRN) designed to deliver better care to cancer patients. Results of these two surveys showed that silos of researchers and clinicians existed before the TRN was established but that the network had mediated collaborative relationships. This paper reports on a third social network survey of the TRN and focusses on the structure of the collaborative arrangements among members. Methods Memb...

  20. Multichannel Convolutional Neural Network for Biological Relation Extraction

    Science.gov (United States)

    Quan, Chanqin; Sun, Xiao; Bai, Wenjun

    2016-01-01

    The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores. PMID:28053977

  1. Robustness and modular structure in networks

    DEFF Research Database (Denmark)

    Bagrow, James P.; Lehmann, Sune; Ahn, Yong-Yeol

    2015-01-01

    Complex networks have recently attracted much interest due to their prevalence in nature and our daily lives [1, 2]. A critical property of a network is its resilience to random breakdown and failure [3-6], typically studied as a percolation problem [7-9] or by modeling cascading failures[10......-12]. Many complex systems, from power grids and the Internet to the brain and society [13-15], can be modeled using modular networks comprised of small, densely connected groups of nodes [16, 17]. These modules often overlap, with network elements belonging to multiple modules [18, 19]. Yet existing work...

  2. Structural bottlenecks for communication in networks.

    Science.gov (United States)

    Sreenivasan, Sameet; Cohen, Reuven; López, Eduardo; Toroczkai, Zoltán; Stanley, H Eugene

    2007-03-01

    We consider the effect of network topology on the optimality of packet routing which is quantified by gammac, the rate of packet insertion beyond which congestion and queue growth occurs. We show that for any network, there exists an absolute upper bound, expressed in terms of vertex separators, for the scaling of gammac with network size N, irrespective of the static routing protocol used. We then derive an estimate to this upper bound for scale-free networks and introduce a static routing protocol, the "hub avoidance protocol," which, for large packet insertion rates, is superior to the shortest path routing protocol.

  3. J D Bernal and the genesis of structural biology

    Science.gov (United States)

    Caffrey, Martin

    2007-02-01

    I was invited to participate in this Symposium a month or so before the event. At that time however, I knew little about J D Bernal. I vaguely remembered a brief conversation on the topic over a decade ago with Professor Vittorio Luzzati as we ambled around the gardens at the Palace of Varsailles. Vittorio likely knew Bernal through his friend Rosalind Franklin who worked with Bernal at Birbeck College. But beyond that I knew nothing about the man or his science. And so it was most fortunate that Andrew Brown's book J D Bernal: The Sage of Science appeared in 2005 and I was able to call on it. Indeed, much of the material included in this chapter is based on that source and on Dorothy Hodgkin's biographic memoir of J D Bernal, her postgraduate supervisor. Given that this chapter is to be published in a Physics journal I thought it appropriate to provide some background to the theme of my presentation, structural biology. Accordingly, I will begin with an introduction to proteins, one of structural biology's central characters, and to which Bernal devoted much energy and attention. How the molecular structure of a protein determines its activity and function will then be described. Bernal's major contribution in this area was to X-ray crystallography, the primary method by which a protein's structure is determined. The method, and aspects of its development, will be described. I will also make reference to some of Bernal's additional contributions in related fields. Finally, Vincent Casey, the symposium organizer, asked that I comment on how structural biology might impact on society. I will attempt to address that at the close of my presentation.

  4. J D Bernal and the genesis of structural biology

    Energy Technology Data Exchange (ETDEWEB)

    Caffrey, Martin [Department of Chemical and Environmental Sciences, University of Limerick, Limerick (Ireland)

    2007-02-02

    I was invited to participate in this Symposium a month or so before the event. At that time however, I knew little about J D Bernal. I vaguely remembered a brief conversation on the topic over a decade ago with Professor Vittorio Luzzati as we ambled around the gardens at the Palace of Varsailles. Vittorio likely knew Bernal through his friend Rosalind Franklin who worked with Bernal at Birbeck College. But beyond that I knew nothing about the man or his science. And so it was most fortunate that Andrew Brown's book J D Bernal: The Sage of Science appeared in 2005 and I was able to call on it. Indeed, much of the material included in this chapter is based on that source and on Dorothy Hodgkin's biographic memoir of J D Bernal, her postgraduate supervisor. Given that this chapter is to be published in a Physics journal I thought it appropriate to provide some background to the theme of my presentation, structural biology. Accordingly, I will begin with an introduction to proteins, one of structural biology's central characters, and to which Bernal devoted much energy and attention. How the molecular structure of a protein determines its activity and function will then be described. Bernal's major contribution in this area was to X-ray crystallography, the primary method by which a protein's structure is determined. The method, and aspects of its development, will be described. I will also make reference to some of Bernal's additional contributions in related fields. Finally, Vincent Casey, the symposium organizer, asked that I comment on how structural biology might impact on society. I will attempt to address that at the close of my presentation.

  5. BiologicalNetworks - tools enabling the integration of multi-scale data for the host-pathogen studies

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2011-01-01

    Full Text Available Abstract Background Understanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage. Results To study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use. Conclusions The developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org and web interface (at http://flu.sdsc.edu.

  6. Dynamic structure evolution of time-dependent network

    Science.gov (United States)

    Zhang, Beibei; Zhou, Yadong; Xu, Xiaoyan; Wang, Dai; Guan, Xiaohong

    2016-08-01

    In this paper, we research the long-voided problem of formulating the time-dependent network structure evolution scheme, it focus not only on finding new emerging vertices in evolving communities and new emerging communities over the specified time range but also formulating the complex network structure evolution schematic. Previous approaches basically applied to community detection on time static networks and thus failed to consider the potentially crucial and useful information latently embedded in the dynamic structure evolution process of time-dependent network. To address these problems and to tackle the network non-scalability dilemma, we propose the dynamic hierarchical method for detecting and revealing structure evolution schematic of the time-dependent network. In practice and specificity, we propose an explicit hierarchical network evolution uncovering algorithm framework originated from and widely expanded from time-dependent and dynamic spectral optimization theory. Our method yields preferable results compared with previous approaches on a vast variety of test network data, including both real on-line networks and computer generated complex networks.

  7. KeyPathwayMiner - De-novo network enrichment by combining multiple OMICS data and biological networks

    DEFF Research Database (Denmark)

    Baumbach, Jan; Alcaraz, Nicolas; Pauling, Josch K.

    We tackle the problem of de-novo pathway extraction. Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts and visualizes all maximal connected sub-networks that contain mainly genes that are dysregulated, e.g., differentially expressed, in most cases...... studied. The exact quantities for ``mainly'' and ``most'' are modeled with two easy-to-interpret parameters that allow the user to control the number of outliers (not dysregulated genes/cases) in the solutions. We developed two slightly varying models that fall into the class of NP-Hard optimization...

  8. Biomolecular Deuteration for Neutron Structural Biology and Dynamics.

    Science.gov (United States)

    Haertlein, Michael; Moulin, Martine; Devos, Juliette M; Laux, Valerie; Dunne, Orla; Forsyth, V Trevor

    2016-01-01

    Neutron scattering studies provide important information in structural biology that is not accessible using other approaches. The uniqueness of the technique, and its complementarity with X-ray scattering, is greatest when full use is made of deuterium labeling. The ability to produce tailor-made deuterium-labeled biological macromolecules allows neutron studies involving solution scattering, crystallography, reflection, and dynamics to be optimized in a manner that has major impact on the scope, quality, and throughput of work in these areas. Deuteration facilities have now been developed at many neutron centres throughout the world; these are having a crucial effect on neutron studies in the life sciences and on biologically related studies in soft matter. This chapter describes methods that have been developed for the efficient production of deuterium-labeled samples for a wide range of neutron scattering applications. Examples are given that illustrate the use of these samples for each of the main techniques. Perspectives for biological deuterium labeling are discussed in relation to developments at current facilities and those that are planned in the future.

  9. Structural and robustness properties of smart-city transportation networks

    Science.gov (United States)

    Zhang, Zhen-Gang; Ding, Zhuo; Fan, Jing-Fang; Meng, Jun; Ding, Yi-Min; Ye, Fang-Fu; Chen, Xiao-Song

    2015-09-01

    The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

  10. The JCSG high-throughput structural biology pipeline.

    Science.gov (United States)

    Elsliger, Marc André; Deacon, Ashley M; Godzik, Adam; Lesley, Scott A; Wooley, John; Wüthrich, Kurt; Wilson, Ian A

    2010-10-01

    The Joint Center for Structural Genomics high-throughput structural biology pipeline has delivered more than 1000 structures to the community over the past ten years. The JCSG has made a significant contribution to the overall goal of the NIH Protein Structure Initiative (PSI) of expanding structural coverage of the protein universe, as well as making substantial inroads into structural coverage of an entire organism. Targets are processed through an extensive combination of bioinformatics and biophysical analyses to efficiently characterize and optimize each target prior to selection for structure determination. The pipeline uses parallel processing methods at almost every step in the process and can adapt to a wide range of protein targets from bacterial to human. The construction, expansion and optimization of the JCSG gene-to-structure pipeline over the years have resulted in many technological and methodological advances and developments. The vast number of targets and the enormous amounts of associated data processed through the multiple stages of the experimental pipeline required the development of variety of valuable resources that, wherever feasible, have been converted to free-access web-based tools and applications.

  11. The relevance of network micro-structure for neural dynamics

    Directory of Open Access Journals (Sweden)

    Volker ePernice

    2013-06-01

    Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.

  12. Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks

    CERN Document Server

    Banerjee, Soumya Jyoti; Roy, Soumen

    2014-01-01

    There have been many studies on malicious targeting of network nodes using degree, betweenness etc. We propose a new network metric, edge proximity, ${\\cal P}_e$, which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. Effects of removing edges with high ${\\cal P}_e$ might initially seem inconspicuous but is eventually shown to be very harmful for the network. When compared to existing strategies, removal of edges by ${\\cal P}_e$, leads to remarkable increase of diameter and average path length in real and random networks till the first disconnection and beyond. ${\\cal P}_e$ can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by ${\\cal P}_e$ causes notable efficiency loss in US and European power grid. ${\\cal P}_e$ identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections...

  13. Compressed Sensing Electron Tomography for Determining Biological Structure

    Science.gov (United States)

    Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.

    2016-06-01

    There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.

  14. Structure of deviations from optimality in biological systems.

    Science.gov (United States)

    Pérez-Escudero, Alfonso; Rivera-Alba, Marta; de Polavieja, Gonzalo G

    2009-12-01

    Optimization theory has been used to analyze evolutionary adaptation. This theory has explained many features of biological systems, from the genetic code to animal behavior. However, these systems show important deviations from optimality. Typically, these deviations are large in some particular components of the system, whereas others seem to be almost optimal. Deviations from optimality may be due to many factors in evolution, including stochastic effects and finite time, that may not allow the system to reach the ideal optimum. However, we still expect the system to have a higher probability of reaching a state with a higher value of the proposed indirect measure of fitness. In systems of many components, this implies that the largest deviations are expected in those components with less impact on the indirect measure of fitness. Here, we show that this simple probabilistic rule explains deviations from optimality in two very different biological systems. In Caenorhabditis elegans, this rule successfully explains the experimental deviations of the position of neurons from the configuration of minimal wiring cost. In Escherichia coli, the probabilistic rule correctly obtains the structure of the experimental deviations of metabolic fluxes from the configuration that maximizes biomass production. This approach is proposed to explain or predict more data than optimization theory while using no extra parameters. Thus, it can also be used to find and refine hypotheses about which constraints have shaped biological structures in evolution.

  15. Synthesis, structure and biological properties of active spirohydantoin derivatives

    Directory of Open Access Journals (Sweden)

    Lazić Anita M.

    2016-01-01

    Full Text Available Spirohidantoins represent an pharmacologically important class of heterocycles since many derivatives have been recognized that display interesting activities against a wide range of biological targets. First synthesis of cycloalkanespiro-5-hydantoins was performed by Bucherer and Lieb 1934 by the reaction of cycloalkanone, potassium cyanide and ammonium-carbonate at reflux in a mixture of ethanol and water. QSAR (Quantitative Structure-Activity Relationship studies showed that a wide range of biological activities of spirohydantoin derivatives strongly depend upon their structure. This paper describes different methods of synthesis of spirohydantoin derivatives, their physico-chemical properties and biological activity. It emphasizes the importance of cycloalkanespiro-5-hydantoins with anticonvulsant, antiproliferative, antipsychotic, antimicrobial and antiinflammatory properties as well as their importance in the treatment of diabetes. Numerous spirohydantoin compounds exhibit physiological activity such as serotonin and fibrinogen antagonist, inhibitors of the glycine binding site of the NMDA receptor also, antagonist of leukocyte cell adhesion, acting as allosteric inhibitors of the protein-protein interactions. Some spirohydantoin derivatives have been identified as antitumor agents. Their activity depends on the substituent presented at position N-3 of the hydantoin ring and increases in order alkene > ester > ether. Besides that, compounds that contain two electron withdrawing groups (e.g. fluorine or chlorine on the third and fourth position of the phenyl ring are better antitumor agents than compounds with a single electron withdrawing group. [Projekat Ministarstva nauke Republike Srbije, br. 172013

  16. Resolving structural variability in network models and the brain.

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  17. Characterizing the Structure of Affiliation Networks

    NARCIS (Netherlands)

    Liu, D.; Blenn, N.; Van Mieghem, P.F.A.

    2012-01-01

    ur society contains all types of organizations, such as companies, research groups and hobby clubs. Affliation networks, as a large and important portion of social networks, consist of individuals and their affiation relations: Two individuals are connected by a link if they belong to the same organ

  18. Wireless Sensor Networks : Structure and Algorithms

    NARCIS (Netherlands)

    van Dijk, T.C.

    2014-01-01

    In this thesis we look at various problems in wireless networking. First we consider two problems in physical-model networks. We introduce a new model for localisation. The model is based on a range-free model of radio transmissions. The first scheme is randomised and we analyse its expected perform

  19. Unveiling the Multi-fractal Structure of Complex Networks

    CERN Document Server

    Jalan, Sarika; Sarkar, Camellia; Boccaletti, Stefano

    2016-01-01

    The fractal nature of graphs has traditionally been investigated by using the nodes of networks as the basic units. Here, instead, we propose to concentrate on the graph edges, and introduce a practical and computationally not demanding method for revealing changes in the fractal behavior of networks, and particularly for allowing distinction between mono-fractal, quasi mono-fractal, and multi-fractal structures. We show that degree homogeneity plays a crucial role in determining the fractal nature of the underlying network, and report on six different protein-protein interaction networks along with their corresponding random networks. Our analysis allows to identify varying levels of complexity in the species.

  20. Identifying Driver Nodes in the Human Signaling Network Using Structural Controllability Analysis.

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang

    2015-01-01

    Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.

  1. Structural Properties of US Flight Network

    Institute of Scientific and Technical Information of China (English)

    池丽平; 王茹; 苏航; 徐新平; 赵金松; 李炜; 蔡勖

    2003-01-01

    We analyse the directed, weighted and evolutionary US flight network, in which vertices are the airports and the flights connecting two airports represent the edges. It is shown that such a network displays two important features recently found in small-world networks. First, the average shortest-path length is 2.4 s, the clustering coefficient of the entire network, 0.618, is greatly larger than that of the random networks with the same N system size)and (average degree), 0.065. We study the detailed flight information both in a week and on a whole.In both the cases, using the Pareto distributions, we find the degree distributions follow two-segment power laws. The weight distributions have power-law tails with the exponents of around 2, and the clustering coefficient distributions follow linear distributions.

  2. Spatial Structure and Scaling of Agricultural Networks

    CERN Document Server

    Sousa, Daniel

    2016-01-01

    Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...

  3. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2008-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  4. Covariance, correlation matrix, and the multiscale community structure of networks.

    Science.gov (United States)

    Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

    2010-07-01

    Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

  5. Fucoidans from brown alga Fucus evanescens: structure and biological activity

    Directory of Open Access Journals (Sweden)

    Roza Menshova

    2016-08-01

    Full Text Available Brown alga Fucus evanescens, widespread in the Far Eastern seas of Russia, is valuable source of sulfated polysaccharides – fucoidans with beneficial biological activities. The most homogenous fraction of fucoidan from F. evanescens was shown to be molecule containing linear main chain of alternating 2-sulfated 1,3- and 1,4-linked α-L-fucose residues. Few sulfate groups were found in position 4 of some 1,3-linked fucose residues. Acetyl groups occupied free C-3 of 1,4-linked residues and/or the C-4 of 1,3-linked fucose residues. Enzymatic hydrolysis, mild acid hydrolysis and autohydrolysis of native fucoidan were used for elucidation of the fine structural characteristics of fucoidan from F. evanescens. The aim of this review to summarize published data on biological activities of fucoidan from F. evanescens: antiviral, anticoagulant, thrombolytic, hepatoprotective, immunomodulatory, anticancer, and their practical application.

  6. Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms

    Science.gov (United States)

    Li, Le; Yip, Kevin Y.

    2016-01-01

    Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature.Availability: Source code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/. PMID:27976738

  7. Building gene co-expression networks using transcriptomics data for systems biology investigations

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

    Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four...

  8. Structural Changes in Online Discussion Networks

    DEFF Research Database (Denmark)

    Yang, Yang; Medaglia, Rony

    2014-01-01

    Social networking platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique socio-cultural and institutional environment. This paper investigates the evolution of patterns of similar-minded and different-minded interactions over...... time on an online discussion platform. We analyse the content and reply networks of 18,000+ messages on four highly debated topics on the Chinese BBS platform Tianya in 2011. Findings illustrate the opinion distribution of the message networks, providing nuanced evidence to the phenomenon of group...

  9. Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.

    Science.gov (United States)

    Guo, Xiaojuan; Wang, Yan; Chen, Kewei; Wu, Xia; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

    2014-01-01

    Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.

  10. Supervised neural networks for the classification of structures.

    Science.gov (United States)

    Sperduti, A; Starita, A

    1997-01-01

    Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relationships among the components of the structures. However, we show that neural networks can, in fact, represent and classify structured patterns. The key idea underpinning our approach is the use of the so called "generalized recursive neuron", which is essentially a generalization to structures of a recurrent neuron. By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. The results obtained by some of the above networks (with generalized recursive neurons) on the classification of logic terms are presented.

  11. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gallagher, B; Eliassi-Rad, T

    2007-10-22

    We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

  12. Network Structure Inference, A Survey: Motivations, Methods, and Applications

    CERN Document Server

    Brugere, Ivan; Berger-Wolf, Tanya Y

    2016-01-01

    Networks are used to represent relationships between entities in many complex systems, spanning from online social networks to biological cell development and brain activity. These networks model relationships which present various challenges. In many cases, relationships between entities are unambiguously known: are two users friends in a social network? Do two researchers collaborate on a published paper? Do two road segments in a transportation system intersect? These are unambiguous and directly observable in the system in question. In most cases, relationship between nodes are not directly observable and must be inferred: does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak? Existing approaches use specialized knowledge in different home domains to infer and measure the goodness of inferred network for a specific task. However, current research lacks a rigorous validation framework which employs standard stat...

  13. Congestion induced by the structure of multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Arenas, Alex

    2016-01-01

    Multiplex networks are representations of multilayer interconnected complex networks where the nodes are the same at every layer. They turn out to be good abstractions of the intricate connectivity of multimodal transportation networks, among other types of complex systems. One of the most important critical phenomena arising in such networks is the emergence of congestion in transportation flows. Here we prove analytically that the structure of multiplex networks can induce congestion for flows that otherwise will be decongested if the individual layers were not interconnected. We provide explicit equations for the onset of congestion and approximations that allow to compute this onset from individual descriptors of the individual layers. The observed cooperative phenomenon reminds the Braess' paradox in which adding extra capacity to a network when the moving entities selfishly choose their route can in some cases reduce overall performance. Similarly, in the multiplex structure, the efficiency in transport...

  14. Large-scale structure of time evolving citation networks

    Science.gov (United States)

    Leicht, E. A.; Clarkson, G.; Shedden, K.; Newman, M. E. J.

    2007-09-01

    In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.

  15. Constraints of Biological Neural Networks and Their Consideration in AI Applications

    Directory of Open Access Journals (Sweden)

    Richard Stafford

    2010-01-01

    Full Text Available Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances. Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

  16. Generalized method for finding community structures in networks

    CERN Document Server

    Chang, Chang

    2013-01-01

    To date, most algorithms aiming to find community structures in networks mainly focus on unipartite or bipartite networks. However, to our knowledge, there is no algorithm specifically designed for the mixture network, a third type defined in our paper that represents a wide range of real-world networks. Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network, suggesting that the mixture network can be considered as a general condition. Based on this observation, we propose a probabilistic model based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E. Newman, Phys. Rev. E 84, 036103 (2011)] by redefining this model in the context of a bipartite network and generalizing the bipartite network version model to a mixture network, the general condition, which can be used to find modules in unipartite, bipartite, and mixture networks in a unified framework. We show that both the model of Ball et al. (unipartite, undirected ne...

  17. Robustness and modular structure in networks

    CERN Document Server

    Bagrow, James P; Ahn, Yong-Yeol

    2011-01-01

    Many complex systems, from power grids and the internet, to the brain and society, can be modeled using modular networks. Modules, densely interconnected groups of elements, often overlap due to elements that belong to multiple modules. The elements and modules of these networks perform individual and collective tasks such as generating and consuming electrical load, transmitting data, or executing parallelized computations. We study the robustness of these systems to the failure of random elements. We show that it is possible for the modules themselves to become isolated or uncoupled (non-overlapping) well before the network falls apart. When modular organization is critical to overall functionality, networks may be far more vulnerable than expected.

  18. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration.......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...

  19. Learning modular structures from network data and node variables

    CERN Document Server

    Azizi, Elham; Airoldi, Edoardo M

    2014-01-01

    A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and unverifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structur...

  20. Discovering and Analyzing Network Function and Structure

    Science.gov (United States)

    2015-07-08

    that the numerical linear algebra community has been seeking for a long time: sparse approximate inverses. To explain these, I recall that the classical...accelerate the computation. Our presently best algorithm computes the matrices L and U and applies them to solve a linear system in parallel time O(log6 n...imization, comes from Zhu, Ghahramani and Lafferty [ZGL+03], and only applies to undirected networks. Formally, one is given a network with vertex set

  1. Residual Network Data Structures in Android Devices

    Science.gov (United States)

    2011-09-01

    alter the user data partition, but does require the device to be rooted which would could be legally challenged. Vidas Et al. explored the concept of...EXT4 filesystem: current status and future plans . 2:21–33, June 2007. ISSN 1530-2075. The Proceedings of the 2007 Linux Symposium. [14] K. D. Fairbanks...23_series/23.923/. [25] Mobile Network Codes (MNC) for the International Identification Plan for Public Networks and Subscriptions, June 2010

  2. Data publication with the structural biology data grid supports live analysis

    NARCIS (Netherlands)

    Meyer, Peter A.; Socias, Stephanie; Key, Jason; Ransey, Elizabeth; Tjon, Emily C.; Buschiazzo, Alejandro; Lei, Ming; Botka, Chris; Withrow, James; Neau, David; Rajashankar, Kanagalaghatta; Anderson, Karen S.; Baxter, Richard H.; Blacklow, Stephen C.; Boggon, Titus J.; Bonvin, Alexandre M J J; Borek, Dominika; Brett, Tom J.; Caflisch, Amedeo; Chang, Chung I.; Chazin, Walter J.; Corbett, Kevin D.; Cosgrove, Michael S.; Crosson, Sean; Dhe-Paganon, Sirano; Di Cera, Enrico; Drennan, Catherine L.; Eck, Michael J.; Eichman, Brandt F.; Fan, Qing R.; Ferré-D'Amaré, Adrian R.; Fromme, J. Christopher; Garcia, K. Christopher; Gaudet, Rachelle; Gong, Peng; Harrison, Stephen C.; Heldwein, Ekaterina E.; Jia, Zongchao; Keenan, Robert J.; Kruse, Andrew C.; Kvansakul, Marc; McLellan, Jason S.; Modis, Yorgo; Nam, Yunsun; Otwinowski, Zbyszek; Pai, Emil F.; Pereira, Pedro José Barbosa; Petosa, Carlo; Raman, C. S.; Rapoport, Tom A.; Roll-Mecak, Antonina; Rosen, Michael K.; Rudenko, Gabby; Schlessinger, Joseph; Schwartz, Thomas U.; Shamoo, Yousif; Sondermann, Holger; Tao, Yizhi J.; Tolia, Niraj H.; Tsodikov, Oleg V.; Westover, Kenneth D.; Wu, Hao; Foster, Ian; Fraser, James S.; Maia, Filipe R N C; Gonen, Tamir; Kirchhausen, Tom; Diederichs, Kay; Crosas, Mercé; Sliz, Piotr

    2016-01-01

    Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Dat

  3. Temporal and structural heterogeneities emerging in adaptive temporal networks

    CERN Document Server

    Aoki, Takaaki; Gross, Thilo

    2015-01-01

    We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.

  4. LTE/MVNO NETWORKS STRUCTURE OPTIMIZATION BASED ON TENSOR DECOMPOSITION

    OpenAIRE

    Strelkovskaya, Iryna; Solovskaya, Iryna

    2015-01-01

    The usage of tensor methods on the decomposition basis is offered for the tasks solution of structure optimization for LTE/MVNO networks mobile communication. The choice problem of optimum topology of e-Node B base stations connectionsin the radio access of E-UTRAN/LTE network was solved. The assessment problem of QoS quality characteristics of complex LTE/MVNO network architecture was solved.

  5. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    Science.gov (United States)

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

  6. Optimal structure of complex networks for minimizing traffic congestion.

    Science.gov (United States)

    Zhao, Liang; Cupertino, Thiago Henrique; Park, Kwangho; Lai, Ying-Cheng; Jin, Xiaogang

    2007-12-01

    To design complex networks to minimize traffic congestion, it is necessary to understand how traffic flow depends on network structure. We study data packet flow on complex networks, where the packet delivery capacity of each node is not fixed. The optimal configuration of capacities to minimize traffic congestion is derived and the critical packet generating rate is determined, below which the network is at a free flow state but above which congestion occurs. Our analysis reveals a direct relation between network topology and traffic flow. Optimal network structure, free of traffic congestion, should have two features: uniform distribution of load over all nodes and small network diameter. This finding is confirmed by numerical simulations. Our analysis also makes it possible to theoretically compare the congestion conditions for different types of complex networks. In particular, we find that network with low critical generating rate is more susceptible to congestion. The comparison has been made on the following complex-network topologies: random, scale-free, and regular.

  7. Structural and Infrastructural Underpinnings of International R&D Networks

    DEFF Research Database (Denmark)

    Niang, Mohamed; Sørensen, Brian Vejrum

    2009-01-01

    This paper explores the process of globally distributing R&D activities with an emphasis on the effects of network maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center to dis...

  8. Chinese lexical networks: The structure, function and formation

    Science.gov (United States)

    Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

    2012-11-01

    In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

  9. Some structural determinants of Pavlovian conditioning in artificial neural networks

    NARCIS (Netherlands)

    Sanchez, Jose M.; Galeazzi, Juan M.; Burgos, Jose E.

    2010-01-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hip

  10. Biana: a software framework for compiling biological interactions and analyzing networks

    Directory of Open Access Journals (Sweden)

    Planas-Iglesias Joan

    2010-01-01

    Full Text Available Abstract Background The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. Results We introduce BIANA (Biologic Interactions and Network Analysis, a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i the integration of multiple sources of biological information, including biological entities and their relationships, and ii the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. Conclusions BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

  11. Laser-matter structuration of optical and biological materials

    Energy Technology Data Exchange (ETDEWEB)

    Hallo, L., E-mail: hallo@celia.u-bordeaux1.fr [CELIA, Universite Bordeaux 1 (France); Mezel, C., E-mail: candice.mezel@cea.fr [CELIA, Universite Bordeaux 1 (France); CEA Le Ripault, 37260 Monts (France); Guillemot, F., E-mail: fabien.guillemot@inserm.fr [UMR 577 INSERM, Universite Bordeaux 2 (France); Chimier, B., E-mail: chimier@celia.u-bordeaux1.fr [CELIA, Universite Bordeaux 1 (France); Bourgeade, A., E-mail: antoine.bourgeade@cea.fr [CEA-CESTA, Le Barp (France); Regan, C., E-mail: regan@celia.u-bordeaux1.fr [CELIA, Universite Bordeaux 1 (France); Duchateau, G., E-mail: duchateau@celia.u-bordeaux1.fr [CELIA, Universite Bordeaux 1 (France); Souquet, A., E-mail: agnes.souquet@inserm.fr [UMR 577 INSERM, Universite Bordeaux 2 (France); Hebert, D., E-mail: david.hebert@cea.fr [CEA-CESTA, Le Barp (France)

    2012-09-15

    Highlights: Black-Right-Pointing-Pointer In this study we model nanomaterial structuring. Black-Right-Pointing-Pointer The laser energy deposition is discussed first. Black-Right-Pointing-Pointer Full and approximate models are discussed. Black-Right-Pointing-Pointer Dynamic material response is addressed via hydrodynamics. Black-Right-Pointing-Pointer Sild effects are accounted for - Abstract: Interaction of ultrafast laser, i.e. from the femtosecond (fs) to the nanosecond (ns) regime, with initially transparent matter may produce very high energy density hot spots in the bulk as well as at the material surface, depending on focusing conditions. In the fs regime, absorption is due to ionisation of the dielectric, which enables absorption process to begin, and then hydrodynamic to take place. In the ns regime both absorption and hydrodynamic are coupled to each other, which complexifies considerably the comprehension but matter structuration looks similar. A numerical tool including solution of 3D Maxwell equations and a rate equation for free electrons is first compared to some available simple models of laser energy absorption. Then, subsequent material deformation, i.e. structuration, is determined by solving hydrodynamic equations, including or not solid behaviour. We show that nature of the final structures strongly depends on the amount of deposited energy and on the shape of the absorption zone. Then we address some problems related to laser-matter structuration of optical and biological materials in the fs, ps and ns regimes.

  12. Synchrotron radiation and structure biology. From the instrumentation view point

    Energy Technology Data Exchange (ETDEWEB)

    Sakabe, N. [Tsukuba Univ., Ibaraki (Japan). Inst. of Applied Biochemistry

    1996-12-31

    Structure Biology is based on a three dimensional macromolecule structures, the most of which are studied by x-ray crystal structure analysis. Synchrotron radiation X-rays are quite strong, tunable, very parallel and pico-second order bunch and are very suitable for diffraction data collection of macromolecular crystals. To collect accurate data at high resolution from large unit cell protein crystals using SR, we made screenless large Weissenberg cameras with imaging plates at the PF. 146 research projects using this data collection system were running in 1995. They include 51 projects from 11 overseas countries. Recently we have developed user-friendly type Weissenberg camera for the structure biology project of TARA (Tsukuba Advanced Research Alliance), and installed it at BL6B of the PF. Cylindrical cassette radius of this new camera is 575.7mm and two large format (400x800mm) imaging plates (IP) can be fixed into the cassette by suction from back side. However, the system is not automatic and manual tasks to be performed are heavy. Therefore an automation of the system is very urgent to maintain accuracy and resolution. We are now developing a high accuracy, high resolution and high speed automated data collection system. This fully automated system consists of a camera, an IP reader equipped with 8 reading heads, an IP eraser, and a cassette transportation mechanism. In the new system, one imaging plate is fixed inside of a movable cylindrical cassette. The cassette presenting 16 rectangular holes, direct beam injection of i.e. 1.0A X-rays would produce 8 images of data at 2.6A resolutions. As 2 cassettes can be used simultaneously on the cassette transportation system, one cassette is being read while the other one is being exposed, therefore completely removing the reading bottleneck problem. This system therefore permits to use the radiation with the maximum of efficiency, and reduce the manpower necessary for data recording. (author)

  13. An artificial network model for estimating the network structure underlying partially observed neuronal signals.

    Science.gov (United States)

    Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

    2014-01-01

    Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed.

  14. Systems biology of the cell cycle of Saccharomyces cerevisiae: From network mining to system-level properties.

    Science.gov (United States)

    Alberghina, Lilia; Coccetti, Paola; Orlandi, Ivan

    2009-01-01

    Following a brief description of the operational procedures of systems biology (SB), the cell cycle of budding yeast is discussed as a successful example of a top-down SB analysis. After the reconstruction of the steps that have led to the identification of a sizer plus timer network in the G1 to S transition, it is shown that basic functions of the cell cycle (the setting of the critical cell size and the accuracy of DNA replication) are system-level properties, detected only by integrating molecular analysis with modelling and simulation of their underlying networks. A detailed network structure of a second relevant regulatory step of the cell cycle, the exit from mitosis, derived from extensive data mining, is constructed and discussed. To reach a quantitative understanding of how nutrients control, through signalling, metabolism and transcription, cell growth and cycle is a very relevant aim of SB. Since we know that about 900 gene products are required for cell cycle execution and control in budding yeast, it is quite clear that a purely systematic approach would require too much time. Therefore lines for a modular SB approach, which prioritises molecular and computational investigations for faster cell cycle understanding, are proposed. The relevance of the insight coming from the cell cycle SB studies in developing a new framework for tackling very complex biological processes, such as cancer and aging, is discussed.

  15. Adaptive Fractal-like Network Structure for Efficient Search of Inhomogeneously Distributed Targets at Unknown Positions

    CERN Document Server

    Hayashi, Yukio

    2012-01-01

    Since a spatial distribution of communication requests is inhomogeneous and related to a population, in constructing a network, it is crucial for delivering packets on short paths through the links between proximity nodes and for distributing the load of nodes how to locate the nodes as base-stations on a realistic wireless environment. In this paper, from viewpoints of complex network science and biological foraging, we propose a scalably self-organized geographical network, in which the proper positions of nodes and the network topology are simultaneously determined according to the population, by iterative divisions of rectangles for load balancing of nodes in the adaptive change of their territories. In particular, we consider a decentralized routing by using only local information,and show that, for searching targets around high population areas, the routing on the naturally embedded fractal-like structure by population has higher efficiency than the conventionally optimal strategy on a square lattice.

  16. Optimizing controllability of edge dynamics in complex networks by perturbing network structure

    Science.gov (United States)

    Pang, Shaopeng; Hao, Fei

    2017-03-01

    Using the minimum input signals to drive the dynamics in complex networks toward some desired state is a fundamental issue in the field of network controllability. For a complex network with the dynamical process defined on its edges, the controllability of this network is optimal if it can be fully controlled by applying one input signal to an arbitrary non-isolated vertex of it. In this paper, the adding-edge strategy and turning-edge strategy are proposed to optimize the controllability by minimum structural perturbations. Simulations and analyses indicate that the minimum number of adding-edges required for the optimal controllability is equal to the minimum number of turning-edges, and networks with positively correlated in- and out-degrees are easier to achieve optimal controllability. Furthermore, both the strategies have the capacity to reveal the relationship between certain structural properties of a complex network and its controllability of edge dynamics.

  17. Mesoscopic structure conditions the emergence of cooperation on social networks

    Energy Technology Data Exchange (ETDEWEB)

    Lozano, S.; Arenas, A.; Sanchez, A.

    2008-12-01

    We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  18. Mesoscopic structure conditions the emergence of cooperation on social networks.

    Directory of Open Access Journals (Sweden)

    Sergi Lozano

    Full Text Available BACKGROUND: We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. METHODOLOGY/PRINCIPAL FINDINGS: We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. CONCLUSION: Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  19. Recovery Management in All Optical Networks Using Biologically-Inspired Complex Adaptive System

    Directory of Open Access Journals (Sweden)

    Inadyuti Dutt

    2013-01-01

    Full Text Available All-Optical Networks have the ability to display varied advantages like performance efficiency, throughput etc but their efficiency depends on their survivability as they are attack prone. These attacks can be categorised as active or passive because they try to access information within the network or alter the information in the network. The attack once detected has to be recovered by formulating back-up or alternative paths. The proposed heuristic uses biologically inspired Complex Adaptive System, inspired by Natural Immune System. The study shows that natural immune system exhibit unique behaviour of detecting foreign bodies in our body and removing them on their first occurrences. This phenomenon is being utilised in the proposed heuristic for recovery management in All-optical Network

  20. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection......, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision....