Community structure in social and biological networks

Structure of the scientific community modelling the evolution of resistance

Faced with the recurrent evolution of resistance to pesticides and drugs, the scientific community has developed theoretical models aimed at identifying the main factors of this evolution and predicting the efficiency of resistance management strategies. The evolutionary forces considered by these models are generally similar for viruses, bacteria, fungi, plants or arthropods facing drugs or pesticides, so interaction between scientists working on different biological organisms would be expected. We tested this by analysing co-authorship and co-citation networks using a database of 187 articles published from 1977 to 2006 concerning models of resistance evolution to all major classes of pesticides and drugs. These analyses identified two main groups. One group, led by ecologists or agronomists, is interested in agricultural crop or stock pests and diseases. It mainly uses a population genetics approach to model the evolution of resistance to insecticidal proteins, insecticides, herbicides, antihelminthic drugs and miticides. By contrast, the other group, led by medical scientists, is interested in human parasites and mostly uses epidemiological models to study the evolution of resistance to antibiotic and antiviral drugs. Our analyses suggested that there is also a small scientific group focusing on resistance to antimalaria drugs, and which is only poorly connected with the two larger groups. The analysis of cited references indicates that each of the two large communities publishes its research in a different set of literature and has its own keystone references: citations with a large impact in one group are almost never cited by the other. We fear the lack of exchange between the two communities might slow progress concerning resistance evolution which is currently a major issue for society.

Modularity and community detection in bipartite networks

The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.

Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK

We analyse the relationship between the network of livestock movements in the UK and the dynamics of two diseases: foot-and-mouth disease (FMD), which has an incubation period of days, and scrapie, which incubates over years. For FMD, the time-scale of expected epidemics is similar to the time-scale of the evolution of the network. We argue that, under appropriate conditions, a static network analysis can be an appropriate tool for gaining insights into disease dynamics even when the relevant time-scales are similar, as with FMD. We show that a subclass of ‘linkage moves’ maintains the network structure, and so removing these links has a dramatic effect on the number of potentially infected farms, an effect corroborated by simulations. In contrast, because scrapie has a low probability of transmission per contact and a long incubation period, a static network representation is probably appropriate; however, the signature of the network in the pattern of transmission is likely to be faint. Scrapie-notifying farms were more likely to be associated with each other via trading at markets than were control farms; however, network community structure proves to be less representative of prevalence patterns than geographical region. These contradictory indicators emphasize that appropriate observation time frames and good discrimination among types of potentially infectious contacts are vital in order for network analysis to be a valuable epidemiological tool.

Recent progress on the analysis of power-law features in complex cellular networks

Complex interactions between different kinds of bio-molecules and essential nutrients are responsible for cellular functions. Rapid advances in theoretical modeling and experimental analyses have shown that drastically different biological and non-biological networks share a common architecture. That is, the probability that a selected node in the network has exactly k edges decays as a power-law. This finding has definitely opened an intense research and debate on the origin and implications of this ubiquitous pattern. In this review, we describe the recent progress on the emergence of power-law distributions in cellular networks. We first show the internal characteristics of the observed complex networks uncovered using graph theory. We then briefly review some works that have significantly contributed to the theoretical analysis of cellular networks and systems, from metabolic and protein networks to gene expression profiles. This prevalent topology observed in so many diverse biological systems suggests the existence of generic laws and organizing principles behind the cellular networks.

Uncovering fuzzy community structure in complex networks

There has been an increasing interest in properties of complex networks, such as small-world property, power-law degree distribution, and network transitivity which seem to be common to many real world networks. In this study, a useful community detection method based on non-negative matrix factorization (NMF) technique is presented. Based on a popular modular function, a proper feature matrix from diffusion kernel and NMF algorithm, the presented method can detect an appropriate number of fuzzy communities in which a node may belong to more than one community. The distinguished characteristic of the method is its capability of quantifying how much a node belongs to a community. The quantification provides an absolute membership degree for each node to each community which can be employed to uncover fuzzy community structure. The computational results of the method on artificial and real networks confirm its ability.

Random matrix analysis of complex networks

We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via the Delta_{3} statistic of RMT as well. It follows RMT prediction of linear behavior in semilogarithmic scale with the slope being approximately 1pi;{2} . Random and scale-free networks follow RMT prediction for very large scale. A small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.

Modular networks emerge from multiconstraint optimization

Modular structure is ubiquitous among complex networks. We note that most such systems are subject to multiple structural and functional constraints, e.g., minimizing the average path length and the total number of links, while maximizing robustness against perturbations in node activity. We show that the optimal networks satisfying these three constraints are characterized by the existence of multiple subnetworks (modules) sparsely connected to each other. In addition, these modules have distinct hubs, resulting in an overall heterogeneous degree distribution.

Bridge and brick network motifs: identifying significant building blocks from complex biological systems

OBJECTIVE

A major focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically important motifs. Here we propose an algorithm for performing two tasks simultaneously: (a) detecting global statistical features and local connection structures in biological networks, and (b) locating functionally and statistically significant network motifs.

METHODS AND MATERIAL

Two gene regulation networks were tested: the bacteria Escherichia coli and the yeast eukaryote Saccharomyces cerevisiae. To understand their structural organizing principles and evolutionary mechanisms, we defined bridge motifs as composed of weak links only or of at least one weak link and multiple strong links, and defined brick motifs as composed of strong links only.

RESULTS

After examining functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions, we found that most genetic network motifs belong to the bridge category. This strongly suggests that the weak-tie links that provide unique paths for signal control significantly impact the signal processing function of transcription networks.

CONCLUSIONS

Bridge and brick motif content analysis can provide researchers with global and local views of individual real networks and help them locate functionally and topologically overlapping or isolated motifs for purposes of investigating biological system functions, behaviors, and similarities.

Near linear time algorithm to detect community structures in large-scale networks

Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.

Module identification in bipartite and directed networks

Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two nonoverlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of node, but links have an origin and an end. We show that directed unipartite networks can be conveniently represented as bipartite networks for module identification purposes. We report on an approach especially suited for module detection in bipartite networks, and we define a set of random networks that enable us to validate the approach.

Effects of the network structural properties on its controllability

In a recent paper, it has been suggested that the controllability of a diffusively coupled complex network, subject to localized feedback loops at some of its vertices, can be assessed by means of a Master Stability Function approach, where the network controllability is defined in terms of the spectral properties of an appropriate Laplacian matrix. Following that approach, a comparison study is reported here among different network topologies in terms of their controllability. The effects of heterogeneity in the degree distribution, as well as of degree correlation and community structure, are discussed.

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

Background

Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.

Results

In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.

Conclusion

Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.

Modular co-evolution of metabolic networks

Background

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.

Results

In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.

Conclusion

The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

Correlation between structure and temperature in prokaryotic metabolic networks

BACKGROUND

In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks.

RESULTS

For this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large.

CONCLUSION

This result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks.

Simulation of robustness against lesions of cortical networks

Structure entails function, and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering and hierarchical organization. Here we apply new network analysis methods to cortical interareal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks using two analysis strategies, in which we measure the effects of the removal of nodes and connections on the structural properties of the cortical networks. The structural decay of the brain networks is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as a structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of connectivity of the brain networks.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection

MOTIVATION

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism.

RESULTS

Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Semantic integration to identify overlapping functional modules in protein interaction networks

Background

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.

Results

We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.

Conclusion

The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

Spectral coarse graining of complex networks

Reducing the complexity of large systems described as complex networks is key to understanding them and a crucial issue is to know which properties of the initial system are preserved in the reduced one. Here we use random walks to design a coarse graining scheme for complex networks. By construction the coarse graining preserves the slow modes of the walk, while reducing significantly the size and the complexity of the network. In this sense our coarse graining allows us to approximate large networks by smaller ones, keeping most of their relevant spectral properties.

Quantifying the influence of sociality on population structure in bottlenose dolphins

1. The social structure of a population plays a key role in many aspects of its ecology and biology. It influences its genetic make-up, the way diseases spread through it and the way animals exploit their environment. However, the description of social structure in nonprimate animals is receiving little attention because of the difficulty in abstracting social structure from the description of association patterns between individuals. 2. Here we focus on recently developed analytical techniques that facilitate inference about social structure from association patterns. We apply them to the population of bottlenose dolphins residing along the Scottish east coast, to detect the presence of communities within this population and infer its social structure from the temporal variation in association patterns between individuals. 3. Using network analytical techniques, we show that the population is composed of two social units with restricted interactions. These two units seem to be related to known differences in the ranging pattern of individuals. By examining social structuring at different spatial scales, we confirm that the identification of these two units is the result of genuine social affiliation and is not an artefact of their spatial distribution. 4. We also show that the structure of this fission-fusion society relies principally on short-term casual acquaintances lasting a few days with a smaller proportion of associations lasting several years. These findings highlight how network analyses can be used to detect and understand the forces driving social organization of bottlenose dolphins and other social species.

A network partition algorithm for mining gene functional modules of colon cancer from DNA microarray data

Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free. Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules’ functions changed with their structures.

Network visualization and network analysis

Network analysis of living systems is an essential component of contemporary systems biology. It is targeted at assemblance of mutual dependences between interacting systems elements into an integrated view of whole-system functioning. In the following chapter we describe the existing classification of what is referred to as biological networks and show how complex interdependencies in biological systems can be represented in a simpler form of network graphs. Further structural analysis of the assembled biological network allows getting knowledge on the functioning of the entire biological system. Such aspects of network structure as connectivity of network elements and connectivity degree distribution, degree of node centralities, clustering coefficient, network diameter and average path length are touched. Networks are analyzed as static entities, or the dynamical behavior of underlying biological systems may be considered. The description of mathematical and computational approaches for determining the dynamics of regulatory networks is provided. Causality as another characteristic feature of a dynamically functioning biosystem can be also accessed in the reconstruction of biological networks; we give the examples of how this integration is accomplished. Further questions about network dynamics and evolution can be approached by means of network comparison. Network analysis gives rise to new global hypotheses on systems functionality and reductionist findings of novel molecular interactions, based on the reliability of network reconstructions, which has to be tested in the subsequent experiments. We provide a collection of useful links to be used for the analysis of biological networks.

Getting connected: analysis and principles of biological networks

The execution of complex biological processes requires the precise interaction and regulation of thousands of molecules. Systematic approaches to study large numbers of proteins, metabolites, and their modification have revealed complex molecular networks. These biological networks are significantly different from random networks and often exhibit ubiquitous properties in terms of their structure and organization. Analyzing these networks provides novel insights in understanding basic mechanisms controlling normal cellular processes and disease pathologies.

Mixture models and exploratory analysis in networks

Networks are widely used in the biological, physical, and social sciences as a concise mathematical representation of the topology of systems of interacting components. Understanding the structure of these networks is one of the outstanding challenges in the study of complex systems. Here we describe a general technique for detecting structural features in large-scale network data that works by dividing the nodes of a network into classes such that the members of each class have similar patterns of connection to other nodes. Using the machinery of probabilistic mixture models and the expectation-maximization algorithm, we show that it is possible to detect, without prior knowledge of what we are looking for, a very broad range of types of structure in networks. We give a number of examples demonstrating how the method can be used to shed light on the properties of real-world networks, including social and information networks.

Identification of network modules by optimization of ratio association

We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association. This cost function arises when the communities detection problem is described in the probabilistic autoencoder frame. An analogy with kernel k-means methods allows us to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on real data sets and on simulated networks.

Griffiths singularities and algebraic order in the exact solution of an Ising model on a fractal modular network

We use an exact renormalization-group transformation to study the Ising model on a complex network composed of tightly knit communities nested hierarchically with the fractal scaling recently discovered in a variety of real-world networks. Varying the ratio KJ of intercommunity to intracommunity couplings, we obtain an unusual phase diagram: at high temperatures or small KJ we have a disordered phase with a Griffiths singularity in the free energy, due to the presence of rare large clusters, which we analyze through the Yang-Lee zeros in the complex magnetic field plane. As the temperature is lowered, true long-range order is not seen, but there is a transition to algebraic order, where pair correlations have power-law decay with distance, reminiscent of the XY model. The transition is infinite order at small KJ and becomes second order above a threshold value (KJ)_{m} . The existence of such slowly decaying correlations is unexpected in a fat-tailed scale-free network, where correlations longer than nearest neighbor are typically suppressed.

Design principles of molecular networks revealed by global comparisons and composite motifs

A global comparison of the four basic molecular networks in yeast - regulatory, co-expression, interaction and metabolic - reveals general design principles.

Background

Molecular networks are of current interest, particularly with the publication of many large-scale datasets. Previous analyses have focused on topologic structures of individual networks.

Results

Here, we present a global comparison of four basic molecular networks: regulatory, co-expression, interaction, and metabolic. In terms of overall topologic correlation - whether nearby proteins in one network are close in another - we find that the four are quite similar. However, focusing on the occurrence of local features, we introduce the concept of composite hubs, namely hubs shared by more than one network. We find that the three 'action' networks (metabolic, co-expression, and interaction) share the same scaffolding of hubs, whereas the regulatory network uses distinctly different regulator hubs. Finally, we examine the inter-relationship between the regulatory network and the three action networks, focusing on three composite motifs - triangles, trusses, and bridges - involving different degrees of regulation of gene pairs. Our analysis shows that interaction and co-expression networks have short-range relationships, with directly interacting and co-expressed proteins sharing regulators. However, the metabolic network contains many long-distance relationships: far-away enzymes in a pathway often have time-delayed expression relationships, which are well coordinated by bridges connecting their regulators.

Conclusion

We demonstrate how basic molecular networks are distinct yet connected and well coordinated. Many of our conclusions can be mapped onto structured social networks, providing intuitive comparisons. In particular, the long-distance regulation in metabolic networks agrees with its counterpart in social networks (namely, assembly lines). Conversely, the segregation of regulator hubs from other hubs diverges from social intuitions (as managers often are centers of interactions).

An algorithm for modularization of MAPK and calcium signaling pathways: comparative analysis among different species

Signaling pathways are large complex biochemical networks. It is difficult to analyze the underlying mechanism of such networks as a whole. In the present article, we have proposed an algorithm for modularization of signal transduction pathways. Unlike studying a signaling pathway as a whole, this enables one to study the individual modules (less complex smaller units) easily and hence to study the entire pathway better. A comparative study of modules belonging to different species (for the same signaling pathway) has been made, which gives an overall idea about development of the signaling pathways over the taken set of species of calcium and MAPK signaling pathways. The superior performance, in terms of biological significance, of the proposed algorithm over an existing community finding algorithm of Newman [Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103(23):8577-82] has been demonstrated using the aforesaid pathways of H. sapiens.

An information-theoretic framework for resolving community structure in complex networks

To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.

An information-theoretic framework for resolving community structure in complex networks

To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.

Structure and tie strengths in mobile communication networks

Electronic databases, from phone to e-mails logs, currently provide detailed records of human communication patterns, offering novel avenues to map and explore the structure of social and communication networks. Here we examine the communication patterns of millions of mobile phone users, allowing us to simultaneously study the local and the global structure of a society-wide communication network. We observe a coupling between interaction strengths and the network's local structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties but fall apart after a phase transition if the weak ties are removed. We show that this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities and find that, when it comes to information diffusion, weak and strong ties are both simultaneously ineffective.

A network-based method for target selection in metabolic networks

MOTIVATION

The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets.

RESULTS

Here, we propose an approach that is based on the mapping of the interactions between biochemical agents, such as proteins and metabolites, onto complex networks. We report that nodes and links in complex biochemical networks can be grouped into a small number of classes, based on their role in connecting different functional modules. Specifically, for metabolic networks, in which nodes represent metabolites and links represent enzymes, we demonstrate that some enzyme classes are more likely to be essential, some are more likely to be species-specific and some are likely to be both essential and specific. Our network-based enzyme classification scheme is thus a promising tool for the identification of drug targets.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics

It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins ("hubs"). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many "shortest paths" going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., "bottleneck-ness") is a much more significant indicator of essentiality than degree (i.e., "hub-ness"). Furthermore, bottlenecks correspond to the dynamic components of the interaction network-they are significantly less well coexpressed with their neighbors than non-bottlenecks, implying that expression dynamics is wired into the network topology.

From functional genomics to systems biology

This review discusses the talks presented at the third EMBL Biennial Symposium, From functional genomics to systems biology, held in Heidelberg, Germany, 14-17 October 2006. Current issues and trends in various subfields of functional genomics and systems biology are considered, including analysis of regulatory elements, signalling networks, transcription networks, protein-protein interaction networks, genetic interaction networks, medical applications of DNA microarrays, and metagenomics. Several technological advances in the fields of DNA microarrays, identification of regulatory elements in the genomes of higher eukaryotes, and MS for detection of protein interactions are introduced. Major directions of future systems biology research are also discussed.

The community structure of human cellular signaling network

Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

Data mining and predictive modeling of biomolecular network from biomedical literature databases

In this paper, we present a novel approach Bio-IEDM (Biomedical Information Extraction and Data Mining) to integrate text mining and predictive modeling to analyze biomolecular network from biomedical literature databases. Our method consists of two phases. In phase 1, we discuss a semisupervised efficient learning approach to automatically extract biological relationships such as protein-protein interaction, protein-gene interaction from the biomedical literature databases to construct the biomolecular network. Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical literature based on the discovered patterns. The derived biomolecular network forms a large scale-free network graph. In phase 2, we present a novel clustering algorithm to analyze the biomolecular network graph to identify biologically meaningful subnetworks (communities). The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters with different density level. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical literature. The integration of data mining and information extraction provides a promising direction for analyzing the biomolecular network.

Detecting complex network modularity by dynamical clustering

Based on cluster desynchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort O(KN) on a generic graph with N nodes and K links.

Controllability of complex networks via pinning

We study the problem of controlling a general complex network toward an assigned synchronous evolution by means of a pinning control strategy. We define the pinning controllability of the network in terms of the spectral properties of an extended network topology. The roles of the control and coupling gains, as well as of the number of pinned nodes, are also discussed.

Perturbation of interaction networks for application to cancer therapy

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.

Network-based prediction of protein function

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

Majority model on a network with communities

We focus on the majority model in a topology consisting of two coupled fully connected networks, thereby mimicking the existence of communities in social networks. We show that a transition takes place at a value of the interconnectivity parameter. Above this value, only symmetric solutions prevail, where both communities agree with each other and reach consensus. Below this value, in contrast, the communities can reach opposite opinions and an asymmetric state is attained. The importance of the interface between the subnetworks is shown.

Cascading dynamics in modular networks

In this paper we study a simple cascading process in a structured heterogeneous population, namely, a network composed of two loosely coupled communities. We demonstrate that under certain conditions the cascading dynamics in such a network has a two-tiered structure that characterizes activity spreading at different rates in the communities. We study the dynamics of the model using both simulations and an analytical approach based on annealed approximation and obtain good agreement between the two. Our results suggest that network modularity might have implications in various applications, such as epidemiology and viral marketing.

Approaches for extracting practical information from gene co-expression networks in plant biology

Gene co-expression, in many cases, implies the presence of a functional linkage between genes. Co-expression analysis has uncovered gene regulatory mechanisms in model organisms such as Escherichia coli and yeast. Recently, accumulation of Arabidopsis microarray data has facilitated a genome-wide inspection of gene co-expression profiles in this model plant. An approach using network analysis has provided an intuitive way to represent complex co-expression patterns between many genes. Co-expression network analysis has enabled us to extract modules, or groups of tightly co-expressed genes, associated with biological processes. Furthermore, integrated analysis of gene expression and metabolite accumulation has allowed us to hypothesize the functions of genes associated with specific metabolic processes. Co-expression network analysis is a powerful approach for data-driven hypothesis construction and gene prioritization, and provides novel insights into the system-level understanding of plant cellular processes.

Clustering by common friends finds locally significant proteins mediating modules

MOTIVATION

Much research has been dedicated to large-scale protein interaction networks including the analysis of scale-free topologies, network modules and the relation of domain-domain to protein-protein interaction networks. Identifying locally significant proteins that mediate the function of modules is still an open problem.

METHOD

We use a layered clustering algorithm for interaction networks, which groups proteins by the similarity of their direct neighborhoods. We identify locally significant proteins, called mediators, which link different clusters. We apply the algorithm to a yeast network.

RESULTS

Clusters and mediators are organized in hierarchies, where clusters are mediated by and act as mediators for other clusters. We compare the clusters and mediators to known yeast complexes and find agreement with precision of 71% and recall of 61%. We analyzed the functions, processes and locations of mediators and clusters. We found that 55% of mediators to a cluster are enriched with a set of diverse processes and locations, often related to translocation of biomolecules. Additionally, 82% of clusters are enriched with one or more functions. The important role of mediators is further corroborated by a comparatively higher degree of conservation across genomes. We illustrate the above findings with an example of membrane protein translocation from the cytoplasm to the inner nuclear membrane.

AVAILABILITY

All software is freely available under Supplementary information.

Random walks along the streets and canals in compact cities: spectral analysis, dynamical modularity, information, and statistical mechanics

Different models of random walks on the dual graphs of compact urban structures are considered. Analysis of access times between streets helps to detect the city modularity. The statistical mechanics approach to the ensembles of lazy random walkers is developed. The complexity of city modularity can be measured by an information-like parameter which plays the role of an individual fingerprint of Genius loci. Global structural properties of a city can be characterized by the thermodynamic parameters calculated in the random walk problem.