Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Network graphs”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18Linked to original sources
PubMed2006

Synchronization in asymmetrically coupled networks with node balance.

We study global stability of synchronization in asymmetrically connected networks of limit-cycle or chaotic oscillators. We extend the connection graph stability method to directed graphs with node balance, the property that all nodes in the network have equal input and output weight sums. We obtain the same upper bound for synchronization in asymmetrically connected networks as in the network with a symmetrized matrix, provided that the condition of node balance is satisfied. In terms of graphs, the symmetrization operation amounts to replacing each directed edge by an undirected edge of half the coupling strength. It should be stressed that without node balance this property in general does not hold.

Action Potentials↗
PubMed2005

Evolving networks by merging cliques.

We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra, and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdös-Rényi or regular graphs. Our model becomes the same model as the Barabási-Albert model under a specific condition.

Journal Article↗
PubMed2004

Improving gene network inference by comparing expression time-series across species, developmental stages or tissues.

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

Algorithms↗
PubMed2004

Semantic graphs and associative memories.

Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.

Journal Article↗
PubMed2003

Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation.

The post-genomic era is marked by huge amounts of data generated by large-scale functional genomic and proteomic experiments. A major challenge is to integrate the various types of genome-scale information in order to reveal the intra- and inter- relationships between genes and proteins that constitute a living cell. Here we present a novel application of classical graph algorithms to integrate the cellular networks of protein-protein interactions and transcription regulation. We demonstrate how integration of these two networks enables the discovery of simple as well as complex regulatory circuits that involve both protein-protein and protein-DNA interactions. These circuits may serve for positive or negative feedback mechanisms. By applying our approach to data from the yeast Saccharomyces cerevisiae, we were able to identify known simple and complex regulatory circuits and to discover many putative circuits, whose biological relevance has been assessed using various types of experimental data. The newly identified relations provide new insight into the processes that take place in the cell, insight that could not be gained by analyzing each type of data independently. The computational scheme that we propose may be used to integrate additional functional genomic and proteomic data and to reveal other types of relations, in yeast as well as in higher organisms.

DNA, Fungal↗
PubMed2004

SARGE: a tool for creation of putative genetic networks.

SUMMARY: SARGE is a tool for creating, visualizing and manipulating a putative genetic network from time series microarray data. The tool assigns potential edges through time-lagged correlation, incorporates a clustering mechanism, an interactive visual graph representation and employs simulated annealing for network optimization. AVAILABILITY: The application is available as a .jar file from http://www.bioinformatics.cs.ncl.ac.uk/sarge/index.html.

Algorithms↗
PubMed2001

Small world effect in an epidemiological model.

A model for the spread of an infection is analyzed for different population structures. The interactions within the population are described by small world networks, ranging from ordered lattices to random graphs. For the more ordered systems, there is a fluctuating endemic state of low infection. At a finite value of the disorder of the network, we find a transition to self-sustained oscillations in the size of the infected subpopulation.

Epidemiologic Methods↗
PubMed2003

Visualisation and navigation methods for typed protein-protein interaction networks.

Protein-protein interactions form large and complex networks. Their visualisation can aid biologists in gaining new insights about the processes in cells and is, therefore, very useful for building sophisticated research tools. Often standard force-directed graph drawing algorithms are used for the visualisation of these networks. However, currently available visual interfaces to biological databases only show general interactions and cannot cope well with more complex networks with different types of interactions. This paper presents a new approach to the visual analysis of protein-protein interaction networks. It uses a combination of circular and force-directed graph drawing algorithms to compute visual representations of protein networks depending on the type of the selected interaction. Smooth transitions between subsequent drawings enable users to explore different functional clusters in these networks without getting lost in the entire network. The visualisation system has been tested with data from the BRITE database.

Algorithms↗
PubMed2000

Likelihood analysis of phylogenetic networks using directed graphical models.

A method for computing the likelihood of a set of sequences assuming a phylogenetic network as an evolutionary hypothesis is presented. The approach applies directed graphical models to sequence evolution on networks and is a natural generalization of earlier work by Felsenstein on evolutionary trees, including it as a special case. The likelihood computation involves several steps. First, the phylogenetic network is rooted to form a directed acyclic graph (DAG). Then, applying standard models for nucleotide/amino acid substitution, the DAG is converted into a Bayesian network from which the joint probability distribution involving all nodes of the network can be directly read. The joint probability is explicitly dependent on branch lengths and on recombination parameters (prior probability of a parent sequence). The likelihood of the data assuming no knowledge of hidden nodes is obtained by marginalization, i.e., by summing over all combinations of unknown states. As the number of terms increases exponentially with the number of hidden nodes, a Markov chain Monte Carlo procedure (Gibbs sampling) is used to accurately approximate the likelihood by summing over the most important states only. Investigating a human T-cell lymphotropic virus (HTLV) data set and optimizing both branch lengths and recombination parameters, we find that the likelihood of a corresponding phylogenetic network outperforms a set of competing evolutionary trees. In general, except for the case of a tree, the likelihood of a network will be dependent on the choice of the root, even if a reversible model of substitution is applied. Thus, the method also provides a way in which to root a phylogenetic network by choosing a node that produces a most likely network.

Computer Graphics↗
PubMed2006

GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring.

UNLABELLED: We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. AVAILABILITY: http://www.cytoscape.org/plugins2.php. SUPPLEMENTARY INFORMATION: Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.

Algorithms↗
PubMed2005

Automatic drawing of biological networks using cross cost and subcomponent data.

Automatic graph drawing function for biopathways is indispensable for biopathway databases and softwares. This paper proposes a new grid-based algorithm for biopathway layout that considers (a) edge-edge crossing, (b) node-edge crossing, (c) distance measures between nodes, as its costs, and (d) subcellular localization information from Gene Ontology, as its constraints. For this algorithm, we newly define cost functions, devise an efficient method for computing the costs (a)-(c) by employing a matrix representing the difference between two layouts, and take a steepest descent method for searching locally optimal solutions and multi-step layout method for finding better solutions. We implemented this algorithm on Cell Illustrator which is a biopathway modeling and simulation software. The algorithm is applied to a signal transduction pathway of apoptosis induced by fas ligand. We compare our layout with that of the grid-based algorithm by Li and Kurata (Bioinformatics 21 (9):2036-2042, 2005). The result shows that our algorithm reduces edge-edge crossings and node-edge crossings, and solves the ''isolated island problem'', that is, despite the intension, some groups of nodes are apart from other nodes in the layout. As a result, the biological understandability of the layout is fairly improved.

Algorithms↗
PubMed2006

Social inertia in collaboration networks.

This work is a study of the properties of collaboration networks employing the formalism of weighted graphs to represent their one-mode projection. The weight of the edges is directly the number of times that a partnership has been repeated. This representation allows us to define the concept of social inertia that measures the tendency of authors to keep on collaborating with previous partners. We use a collection of empirical datasets to analyze several aspects of the social inertia: (1) its probability distribution, (2) its correlation with other properties, and (3) the correlations of the inertia between neighbors in the network. We also contrast these empirical results with the predictions of a recently proposed theoretical model for the growth of collaboration networks.

Journal Article↗
PubMed2000

Hydrogen bonding and C-H.O interactions in 2-

The title acid, C(19)H(16)O(2), crystallized in the centrosymmetric space group Pnna with three molecules in the asymmetric unit (apparently the first reported instance of Z' = 3 in this space group). Four intermolecular hydrogen bonds have O(D).O(A) distances of 2.660 (2), 2.594 (2), 2.633 (2) and 2.646 (2) A, and angles of 177 (2), 179 (2), 175 (2) and 175 (2) degrees. The four hydrogen bonds form two R(2)(2)(8) cyclic dimers, one about a twofold axis. Five leading intermolecular C-H.O interactions are present. Although first- and second-level graph sets involving these nine interactions are dominated by finite patterns, a three-dimensional network becomes evident upon analysis of higher-level graphs. A number of intramolecular C-H.O interactions are also present.

Journal Article↗
PubMed2004

Functional topology in a network of protein interactions.

MOTIVATION: The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. RESULTS: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. CONCLUSIONS: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.

Algorithms↗
PubMed2005

Networks and epidemic models.

Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a 'mixing network'. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections. Therefore, characteristics of mixing networks-and how these deviate from the random-mixing norm-have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures. Here, we review the basis of epidemiological theory (based on random-mixing models) and network theory (based on work from the social sciences and graph theory). We then describe a variety of methods that allow the mixing network, or an approximation to the network, to be ascertained. It is often the case that time and resources limit our ability to accurately find all connections within a network, and hence a generic understanding of the relationship between network structure and disease dynamics is needed. Therefore, we review some of the variety of idealized network types and approximation techniques that have been utilized to elucidate this link. Finally, we look to the future to suggest how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control.

Animals↗
PubMed2006

PathSys: integrating molecular interaction graphs for systems biology.

BACKGROUND: The goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately. RESULTS: Here we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping. CONCLUSION: Results from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSys's client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at http://brak.sdsc.edu/pub/BiologicalNetworks.

Computer Graphics↗
PubMed2005

New probabilistic graphical models for genetic regulatory networks studies.

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

Algorithms↗
PubMed2005

Topology of mammalian transcription networks.

We present a first attempt to evaluate the generic topological principles underlying the mammalian transcriptional regulatory networks. Transcription networks, TN, studied here are represented as graphs where vertices are genes coding for transcription factors and edges are causal links between the genes, each edge combining both gene expression and trans-regulation events. Two transcription networks were retrieved from the TRANSPATH database: The first one, TN_RN, is a 'complete' transcription network referred to as a reference network. The second one, TN_p53, displays a particular transcriptional sub-network centered at p53 gene. We found these networks to be fundamentally non-random and inhomogeneous. Their topology follows a power-law degree distribution and is best described by the scale-free model. Shortest-path-length distribution and the average clustering coefficient indicate a small-world feature of these networks. The networks show the dependence of the clustering coefficient on the degree of a vertex, thereby indicating the presence of hierarchical modularity. Clear positive correlation between the values of betweenness and the degree of vertices has been observed in both networks. The top list of genes displaying high degree and high betweennes, such as p53, c-fos, c-jun and c-myc, is enriched with genes that are known as having tumor-suppressor or proto-oncogene properties, which supports the biological significance of the identified key topological elements.

Animals↗