Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

We review the recent fast progress in statistical physics of evolving networks. Interest has focused mainly on the structural properties of random complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of such a kind came into existence recently. This opens a wide field for the study of their topology, evolution, and complex processes occurring in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of large sizes of these networks, the distances between most their vertices are short — a feature known as the “smallworld” effect. We discuss how growing networks self-organize into scale-free structures and the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation in these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in Nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems

Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/ protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at

this paper is the interactions occurring within specific complexes. These were obtained from the MIPS complexes catalog (Fellenberg et al. 2000), which represents a carefully annotated, comprehensive data set of protein complexes culled from the scientific literature. In addition, we looked at other types of protein-protein interactions from large "aggregated" data sets collecting many heterogeneous pair-wise interactions. We collected these from the MIPS catalogs of physical and genetic interactions (Fellenberg et al. 2000), databases of interacting proteins (DIP and BIND) (Bader and Hogue 2000; Xenarios 2000), and a comprehensive collection of yeast two-hybrid experiments (Cagney et al. 2000; lto et al. 2000; Schwikowski et al. 2000; Uetz et al. 2000; Uetz and Hughes 2000; lto et al. 2001). These interactions are subdivided into groups based on their method of discovery. They include physical interactions (e.g., collected through coimmunoprecipitation and copurification), genetic interactions (e.g., determined through genetic means such as synthetic lethality or suppression experiments), and yeast twohybrid pairs

Motivation: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse, using experimental methods such as yeasttwo-hybrids and protein chips. Such data can be errorful and they often do not provide adequate functional information for the detected interactions; it is therefore useful to develop an in silico approach to further validate and annotate the detected protein interactions. Results: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale experimentally determined domain-domain interaction data do not exist; as such, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including rosetta stone sequences, protein interactions, and protein complexes. We show the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. Contact:

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N.k / � k ¡ ¯), yet the exponents, ¯, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein–protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

statistically to which of the six types of interfaces a pool of 1000 residues belongs at 63 -- 100% accuracy. All interfaces differed significantly from the background of all residues in SWISS-PROT, from the group of surface residues, and from internal residues that were not involved in non-trivial interactions. Overall, our results suggest that the interface type could be predicted from sequence and that interface-type specific mean-field potentials may be adequate for certain applications. q 2003 Elsevier Science Ltd. All rights reserved Keywords: protein -- protein interaction; protein complexes; protein interface; protein folding; drug design *Corresponding authors Introduction Do different types of interactions use different biochemical mechanisms? Non-covalent contacts between residue sidechains are the basis for protein folding, protein assembly, and protein -- protein interaction. These contacts occur under many different conditions, and facilitate a variety of interacti

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ABSTRACT Protein–protein interactions play a key role in many biological systems. High-throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false-positive and falsenegative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co-complex relationship, and (3) pathway co-membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity-based k-Nearest-Neighbor,

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression network, protein complex data and domain structures of individual proteins together to prediction protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with di#erent weights for di#erent sources of data. It is a global approach taking the whole network into consideration. The other is that posterior probability for the protein to have the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based on MIPS protein function classifications and the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, and Tandem A#nity Purification (TAP) protein complex data, and protein domain information. We study the sensitivity and specificity of the integrated approach using di#erent sources of information by the leave-one-out approach. Compared to using MIPS physical interactions only, the integrated approach combining all the information increases the sensitivity from 57% to 87% when the specificity is set at 57%, an increase of 30%. It should also be noted that by enlarging the interaction network, the number of proteins whose functions can be...