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Power-law tail probabilities of drainage areas in river basins.

We examine the appearance of power-law behavior in rooted tree graphs in the context of river networks. It has long been observed that the tails of statistical distributions of upstream areas in river networks, measured above every link, obey a power-law relationship over a range of scales. We examine this behavior by considering a subset of all links, defined as those links which drain complete Strahler basins, where the Strahler order defines a discrete measure of scale, for self-similar networks with both deterministic and random topologies. We find an excellent power-law structure in the tail probabilities for complete Strahler basin areas, over many ranges of scale. We show analytically that the tail probabilities converge to a power law under the assumptions of (1) simple scaling of the distributions of complete Strahler basin areas and (2) application of Horton's law of stream numbers. The convergence to a power law does not occur for all underlying distributions, but for a large class of statistical distributions which have specific limiting properties. For example, underlying distributions which are exponential and gamma distributed, while not power-law scaling, produce power laws in the tail probabilities when rescaled and sampled according to Horton's law of stream numbers. The power-law exponent is given by the expression phi=ln(R(b))/ln(R(A)), where R(b) is the bifurcation ratio and R(A) is the Horton area ratio. It is commonly observed that R(b) approximately equal R(A) in many river basins, implying that the tail probability exponent for complete Strahler basins is close to 1.0.

Journal Article↗

Hydrogen-bonding patterns in two structural isomers of 3,6-bis(2-chlorophenyl)-1,4-dihydro-1,2,4,5-tetrazine.

Two structural isomers, 3,6-bis(2-chlorophenyl)-1,4-dihydro-1,2,4,5-tetrazine, (I), and 3,5-bis(2-chlorophenyl)-4-amino-1H-1,2,4-triazole, (II), both C(14)H(10)Cl(2)N(4), form chain-like structures in the solid state, stabilized by N-H...N and N-H...Cl hydrogen bonds. A contribution from weak interactions to the strong hydrogen-bond network is observed in both structures. The secondary graph sets for intermolecular hydrogen bonds [R(2)(2)(11) for (I) and R(2)(2)(12) for (II)] indicate the similarity between the networks.

Journal Article↗

5-ammoniosalicylic acid chloride monohydrate.

The title compound, C7H8NO3+.Cl-.H2O, crystallized in the centrosymmetric space group P2(1)/c in an ionic form, the proton from HCl having been transferred to the amino N atom. The three H atoms on N, the H atom on the carboxyl group, the H atom of the hydroxyl group and the H atoms of the water molecule, all of which are involved in hydrogen bonding, are ordered. The eight 'best' hydrogen bonds have the following donor-acceptor distances: O...O 2.681 (2) and 2.598 (2); O...Cl 3.367 (1), 3.345 (2) and 3.156 (2); N...O 2.995 (2); N...Cl 3.173 (2) and 3.114 (2) A. In this structure, the acid cations are not linked directly to each other by hydrogen bonds, but are linked indirectly, via hydrogen bonds involving chloride ions and water molecules, into a three-dimensional network. Through basic second-level graphs, finite patterns substantially outnumber chains and rings.

Crystallography, X-Ray↗

Smashing peacocks further: drawing quasi-trees from biconnected components.

Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved quality.

Journal Article↗

Estimation of distribution algorithms with Kikuchi approximations.

The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network Estimation of Distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to estimate the distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.

Algorithms↗

Divergent evolution of a structural proteome: phenomenological models.

We develop models of the divergent evolution of genomes; the elementary object of sequence dynamics is the protein structural domain. To identify patterns of organization that reflect mechanisms of evolution, we consider the individual genomes of many procaryote species, studying the arrangement of protein structural domains in the space of all polypeptide structures. We view the network of structural similarities as a graph, called the organismal Protein Domain Universe Graph (oPDUG); vertices represent types of structural domains and edges represent strong structural similarity. As observed before, each oPDUG is a highly nonrandom graph, as evidenced in the vertex degree distribution, which resembles a Pareto law (which has a power-law asymptotic). To explain this and other peculiar properties of the oPDUGs, we construct an evolving-graph model for the long-timescale evolutionary dynamics of oPDUGs, containing only divergent mechanisms of domain discovery. The model generates degree distributions (resembling Pareto laws) and clustering-coefficient distributions that are characteristic of the oPDUGs. In the infinite-graph limit, we analytically compute the exponent for specific biological parameters, as well as the complete phase diagram of the model, finding two distinct regimes of domain innovation dynamics. Thus, divergent evolutionary dynamics quantitatively explains the nonrandom organization of oPDUGs.

Bacterial Proteins↗

Transmitting a signal by amplitude modulation in a chaotic network.

We discuss the ability of a model of network with nonlinear units and chaotic dynamics to transmit signals, on the basis of a linear response theory developed by Ruelle [D. Ruelle, J. Stat. Phys. 95, 393 (1999)] for dissipative systems. We discuss in particular how the dynamics may interfere with the graph topology to produce an effective transmission network, whose topology depends on the signal, and cannot be directly read on the "wired" network. Then, we show examples where, with a suitable choice of the carrier frequency (resonance), one can transmit a signal from a node to another one by amplitude modulation, in spite of chaos. Also, we give an example where a signal, transmitted to any node via different paths, can only be recovered by a couple of specific nodes. This opens up the possibility for encoding data in a way such that the recovery of the signal requires the knowledge of the carrier frequency and can be performed only at some specific node.

Journal Article↗

Scale-free networks with an exponent less than two.

We study scale-free simple graphs with an exponent of the degree distribution gamma less than 2. Generically one expects such extremely skewed networks--which occur very frequently in systems of virtually or logically connected units--to have different properties than those of scale free networks with gamma>2: The number of links grows faster than the number of nodes and they naturally possess the small world property, because the diameter increases by the logarithm of the size of the network and the clustering coefficient is finite. We discuss a simple prototype model of such networks, inspired by real world phenomena, which exhibits these properties and allows for a detailed analytical investigation.

Journal Article↗

Animat navigation using a cognitive graph.

This article describes a computational model of the hippocampus that makes it possible for a simulated rat to navigate in a continuous environment containing obstacles. This model views the hippocampus as a "cognitive graph", that is, a hetero-associative network that learns temporal sequences of visited places and stores a topological representation of the environment. Calling upon place cells, head direction cells, and "goal cells", it suggests a biologically plausible way of exploiting such a spatial representation for navigation that does not require complicated graph-search algorithms. Moreover, it permits "latent learning" during exploration, that is, the building of a spatial representation without the need of any reinforcement. When the rat occasionally discovers some rewarding place it may wish to rejoin subsequently, it simply records within its cognitive graph, through a series of goal and sub-goal cells, the direction in which to move from any given start place. Accordingly, the model implements a simple "place-recognition-triggered response" navigation strategy. Two implementations of place cell management are studied in parallel. The first one associates place cells with place fields that are given a priori and that are uniformly distributed in the environment. The second one dynamically recruits place cells as exploration proceeds and adjusts the density of such cells to the local complexity of the environment. Both implementations lead to identical results. The article ends with a few predictions about results to be expected in experiments involving simultaneous recordings of multiple cells in the rat hippocampus.

Animals↗

Random spread on the family of small-world networks.

We present analytical and numerical results of a random walk on the family of small-world graphs. The average access time shows a crossover from regular to random behavior with increasing distance from the starting point of the random walk. We introduce an independent step approximation, which enables us to obtain analytic results for the average access time. We observe a scaling relation for the average access time in the degree of the nodes. The behavior of the average access time as a function of p shows striking similarity with that of the characteristic length of the graph. This observation may have important applications in routing and switching in networks with a large number of nodes.

Journal Article↗

Where diseases and networks collide: lessons to be learnt from a study of the 2001 foot-and-mouth disease epidemic.

This paper uses a graph-theoretical approach to investigate the properties of the observed network of disease transmission in the 2001 foot-and-mouth epidemic in the United Kingdom. This analysis revealed both global and local heterogeneity in the contact pattern between the infected premises in the first 3 weeks of the disease. In particular, the global heterogeneity contributed to the failure of the culling strategy imposed by the UK government. However, a more effective strategy targeting selective deletion of key premises in the network was not available once the epidemic had begun. We recommend that post-hoc analyses of this sort should become part of preventative and proactive policy rather than part of a reaction to an ongoing crisis.

Animals↗

Evolving networks with distance preferences.

We study evolving networks where new nodes when attached to the network form links with other nodes of preferred distances. A particular case is where always the shortest distances are selected ("make friends with the friends of your present friends"). We present simulation results for network parameters like the first eigenvalue of the graph Laplacian (synchronizability), clustering coefficients, average distances, and degree distributions for different distance preferences and compare them with the parameter values for random and scale-free networks. We find that for the shortest distance rule we obtain a power-law degree distribution as in scale-free networks, while the other parameters are significantly different, especially the clustering coefficient.

Journal Article↗

Design of a directed molecular network.

An ability to rationally design complex networks from the bottom up can offer valuable quantitative model systems for use in gaining a deeper appreciation for the principles governing the self-organization and functional characteristics of complex systems. We report herein the de novo design, graph prediction, experimental analysis, and characterization of simple self-organized, nonlinear molecular networks. Our approach makes use of the sequence-dependent auto- and cross-catalytic functional characteristics of template-directed peptide fragment condensation reactions in neutral aqueous solutions. Starting with an array of 81 sequence similar 32-residue coiled-coil peptides, we estimated the relative stability difference between all plausible A(2)B-type coiled-coil ensembles and used this information to predict the auto- and cross-catalysis pathways and the resulting plausible network motif and connectivities. Similar to most complex systems, the generated graph displays clustered nodes with an overall hierarchical architecture. To test the validity of the design principles used, nine nodes composing a main segment of the graph were experimentally analyzed for their capacity in establishing the predicted network connectivity. The resulting self-organized chemical network is shown to display 25 directed edges in good agreement with the graph analysis estimations. Moreover, we show that by varying the system parameters (presence or absence of certain substrates or templates), its operating network motif can be altered, even to the extremes of turning pathways on or off. We suggest that this approach can be expanded for the construction of large-scale networks, offering a means to study and to understand better the emergent, collective behaviors of networks.

Amino Acid Sequence↗

Assessing experimentally derived interactions in a small world.

Experimentally determined networks are susceptible to errors, yet important inferences can still be drawn from them. Many real networks have also been shown to have the small-world network properties of cohesive neighborhoods and short average distances between vertices. Although much analysis has been done on small-world networks, small-world properties have not previously been used to improve our understanding of individual edges in experimentally derived graphs. Here we focus on a small-world network derived from high-throughput (and error-prone) protein-protein interaction experiments. We exploit the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein-protein interactions. By ascertaining how well each protein-protein interaction (edge) fits the pattern of a small-world network, we stratify even those edges with identical experimental evidence. This result promises to improve the quality of inference from protein-protein interaction networks in particular and small-world networks in general.

Cluster Analysis↗

Distribution of epicenters in the Olami-Feder-Christensen model.

We show that the well established Olami-Feder-Christensen (OFC) model for the dynamics of earthquakes is able to reproduce a striking property of real earthquake data. Recently, it has been pointed out by Abe and Suzuki that the epicenters of earthquakes could be connected in order to generate a graph, with properties of a scale-free network of the Barabási-Albert type. However, only the nonconservative version of the Olami-Feder-Christensen model is able to reproduce this behavior. The conservative version, instead, behaves like a random graph. Besides indicating the robustness of the model to describe earthquake dynamics, those findings reinforce that conservative and nonconservative versions of the OFC model are qualitatively different. Also, we propose a completely different dynamical mechanism that, even without an explicit rule of preferential attachment, generates a scale-free network. The preferential attachment is in this case a "byproduct" of the long term correlations associated with the self-organized critical state.

Journal Article↗

Complexity and fragility in ecological networks.

A detailed analysis of three species-rich ecosystem food webs has shown that they display skewed distributions of connections. Such graphs of interaction are, in fact, shared by a number of biological and technological networks, which have been shown to display a very high homeostasis against random removals of nodes. Here, we analyse the responses of these ecological graphs to both random and selective perturbations (directed against the most-connected species). Our results suggest that ecological networks are very robust against random removals but can be extremely fragile when selective attacks are used. These observations have important consequences for biodiversity dynamics and conservation issues, current estimations of extinction rates and the relevance and definition of keystone species.

Ecosystem↗

Generating protein interaction maps from incomplete data: application to fold assignment.

MOTIVATION: We present a framework to generate comprehensive overviews of protein-protein interactions. In the post-genomic view of cellular function, each biological entity is seen in the context of a complex network of interactions. Accordingly, we model functional space by representing protein-protein-interaction data as undirected graphs. We suggest a general approach to generate interaction maps of cellular networks in the presence of huge amounts of fragmented and incomplete data, and to derive representations of large networks which hide clutter while keeping the essential architecture of the interaction space. This is achieved by contracting the graphs according to domain-specific hierarchical classifications. The key concept here is the notion of induced interaction, which allows the integration, comparison and analysis of interaction data from different sources and different organisms at a given level of abstraction. RESULTS: We apply this approach to compute the overlap between the DIP compendium of interaction data and a dataset of yeast two-hybrid experiments. The architecture of this network is scale-free, as frequently seen in biological networks, and this property persists through many levels of abstraction. Connections in the network can be projected downwards from higher levels of abstraction down to the level of individual proteins. As an example, we describe an algorithm for fold assignment by network context. This method currently predicts protein folds at 30% accuracy without any requirement of detectable sequence similarity of the query protein to a protein of known structure. We used this algorithm to compile a list of structural assignments for previously unassigned genes from yeast. Finally we discuss ways forward to use interaction networks for the prediction of novel protein-protein interactions. AVAILABILITY: http://www.ebi.ac.uk/~lappe/FoldPred/.

Algorithms↗

Scale-free behavior in protein domain networks.

Several technical, social, and biological networks were recently found to demonstrate scale-free and small-world behavior instead of random graph characteristics. In this work, the topology of protein domain networks generated with data from the ProDom, Pfam, and Prosite domain databases was studied. It was found that these networks exhibited small-world and scale-free topologies with a high degree of local clustering accompanied by a few long-distance connections. Moreover, these observations apply not only to the complete databases, but also to the domain distributions in proteomes of different organisms. The extent of connectivity among domains reflects the evolutionary complexity of the organisms considered.

Amino Acid Sequence↗