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Showing papers on "Interaction network published in 2003"


Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations


Journal ArticleDOI
TL;DR: The assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories is proposed.
Abstract: Determining protein function is one of the most challenging problems of the post-genomic era. The availability of entire genome sequences and of high-throughput capabilities to determine gene coexpression patterns has shifted the research focus from the study of single proteins or small complexes to that of the entire proteome 1 . In this context, the search for reliable methods for assigning protein function is of primary importance. There are various approaches available for deducing the function of proteins of unknown function using information derived from sequence similarity or clustering patterns of coregulated genes 2,3 , phylogenetic profiles 4 , protein-protein interactions (refs. 5‐8 and Samanta, M.P. and Liang, S., unpublished data), and protein complexes 9,10 . Here we propose the assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories. Function assignment is proteome-wide and is determined by the global connectivity pattern of the protein network. The approach results in multiple functional assignments, a consequence of the existence of multiple equivalent solutions. We apply the method to analyze the yeast Saccharomyces cerevisiae protein-protein interaction network 5 .

626 citations


Posted Content
TL;DR: In this article, the authors propose to assign functional classes to proteins from their network of physical interactions, by minimizing the number of interacting proteins with different categories, based on the entire connectivity pattern of the protein network.
Abstract: The determination of protein functions is one of the most challenging problems of the post-genomic era. The sequencing of entire genomes and the possibility to access gene's co-expression patterns has moved the attention from the study of single proteins or small complexes to that of the entire proteome. In this context, the search for reliable methods for proteins' function assignment is of uttermost importance. Previous approaches to deduce the unknown function of a class of proteins have exploited sequence similarities or clustering of co-regulated genes, phylogenetic profiles, protein-protein interactions, and protein complexes. We propose to assign functional classes to proteins from their network of physical interactions, by minimizing the number of interacting proteins with different categories. The function assignment is made on a global scale and depends on the entire connectivity pattern of the protein network. Multiple functional assignments are made possible as a consequence of the existence of multiple equivalent solutions. The method is applied to the yeast Saccharomices Cerevisiae protein-protein interaction network. Robustness is tested in presence of a high percentage of unclassified proteins and under deletion/insertion of interactions.

602 citations


Journal ArticleDOI
TL;DR: A method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network that exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors.
Abstract: Motivation: The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. Results: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cere

387 citations



Journal ArticleDOI
TL;DR: This work presents an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.
Abstract: The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca . Current capabilities allow searching for human, mouse and yeast protein-interaction information.

334 citations


Journal ArticleDOI
TL;DR: PRODISTIN, a new computational method allowing the functional clustering of proteins on the basis of protein-protein interaction data, is described, which enabled it to classify 11% of the Saccharomyces cerevisiae proteome into several groups and to predict a cellular function for many otherwise uncharacterized proteins.
Abstract: We here describe PRODISTIN, a new computational method allowing the functional clustering of proteins on the basis of protein-protein interaction data. This method, assessed biologically and statistically, enabled us to classify 11% of the Saccharomyces cerevisiae proteome into several groups, the majority of which contained proteins involved in the same biological process(es), and to predict a cellular function for many otherwise uncharacterized proteins.

292 citations


Journal ArticleDOI
11 Dec 2003-Nature
TL;DR: It is shown that an isolated peptide ligand from the yeast protein Pbs2 recognizes its biological partner, the SH3 domain from Sho1, with near-absolute specificity, suggesting that system-wide negative selection is a subtle but powerful evolutionary mechanism to optimize specificity within an interaction network composed of overlapping recognition elements.
Abstract: Most proteins that participate in cellular signalling networks contain modular protein-interaction domains. Multiple versions of such domains are present within a given organism1: the yeast proteome, for example, contains 27 different Src homology 3 (SH3) domains2. This raises the potential problem of cross-reaction. It is generally thought that isolated domain–ligand pairs lack sufficient information to encode biologically unique interactions, and that specificity is instead encoded by the context in which the interaction pairs are presented3,4. Here we show that an isolated peptide ligand from the yeast protein Pbs2 recognizes its biological partner, the SH3 domain from Sho1, with near-absolute specificity—no other SH3 domain present in the yeast genome cross-reacts with the Pbs2 peptide, in vivo or in vitro. Such high specificity, however, is not observed in a set of non-yeast SH3 domains, and Pbs2 motif variants that cross-react with other SH3 domains confer a fitness defect, indicating that the Pbs2 motif might have been optimized to minimize interaction with competing domains specifically found in yeast. System-wide negative selection is a subtle but powerful evolutionary mechanism to optimize specificity within an interaction network composed of overlapping recognition elements.

280 citations


Journal ArticleDOI
Hai-Jun Zhou1
TL;DR: This work calculates the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized, and identifies many clusters that have well defined biological functions.
Abstract: We address the question of finding the community structure of a complex network. In an earlier effort [H. Zhou, Phys. Rev. E 67, 041908 (2003)], the concept of network random walking is introduced and a distance measure defined. Here we calculate, based on this distance measure, the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized. Each community is characterized by an upper and a lower dissimilarity threshold. The algorithm is applied to several artificial and real-world networks, and excellent results are obtained. In the case of artificially generated random modular networks, this method outperforms the algorithm based on the concept of edge betweenness centrality. For yeast's protein-protein interaction network, we are able to identify many clusters that have well defined biological functions.

279 citations


Journal ArticleDOI
Eli Eisenberg1, Erez Y. Levanon1
TL;DR: Using a cross-genome comparison, it is shown that the older a protein, the better connected it is, and the number of interactions a protein gains during its evolution is proportional to its connectivity.
Abstract: The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields this universal topology. However, it is not clear that the model assumptions hold for the protein interaction network. Using a cross-genome comparison, we show that (a) the older a protein, the better connected it is, and (b) the number of interactions a protein gains during its evolution is proportional to its connectivity. Therefore, preferential attachment governs the protein network evolution. Evolutionary mechanisms leading to such preference and some implications are discussed.

234 citations


Journal ArticleDOI
TL;DR: This work proposes a new technique which is based on collective, multi-body correlations in a genetic network based on the superparamagnetic approach, and shows that this method is more sensitive in revealing functional relationships.
Abstract: Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

Journal ArticleDOI
TL;DR: A bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites and produces an interaction matrix that represents a candidate transcriptional regulatory network is developed.
Abstract: We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems The tool can also produce output in various formats suitable for use in external visualization and analysis software In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II PAINT is available for use as an agent in BioSPICE simulation and analysis framework (wwwbiospiceorg), and can also be accessed via a WWW interface at wwwdbitjuedu/dbi/tools/paint/

Journal ArticleDOI
TL;DR: There is strong evidence that an introduced species is able to affect the network of interactions among coexisting species and the presence of cattle has significantly modified the structure of the plant–pollinator interaction network.
Abstract: Long-term conservation of biodiversity may depend not only on the maintenance of its component parts but also on their interactions. Here we provide strong evidence that an introduced species is able to affect the network of interactions among coexisting species. We studied plant–pollinator interactions in native forest sites with and without domestic cattle and used these data to construct plant–pollinator interaction networks. Results from nonmetric multidimensional scaling and permutation tests suggest that the presence of cattle has significantly modified the structure of the plant–pollinator interaction network. The effect of cattle on network structure was mainly because of the modification of a few highly frequent interactions, which are likely important from a functional perspective. This overwhelming influence of a few interactions on observed community patterns should serve as a caution to those studying community and ecosystem properties.

Proceedings ArticleDOI
10 Apr 2003
TL;DR: 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 to predict protein functions is developed.
Abstract: 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 to predict 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 in 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 different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has 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 upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, Tandem Affinity Purification (TAP) protein complex data, and protein domain information. We study the sensitivity and specificity of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of 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 enlarging the interaction network greatly increases the number of proteins whose functions can be predicted.

Journal ArticleDOI
TL;DR: It is found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life.
Abstract: To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.

Journal ArticleDOI
TL;DR: This review describes several databases that store, query, and visualize protein-protein interaction data and shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions.
Abstract: Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-protein interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughput data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.

01 Jan 2003
TL;DR: In this paper, the authors describe several databases that store, query, and visualize protein-protein interaction data and compare them with other types of data, including protein function, subcellular location, and gene expression profile.
Abstract: Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-prote in interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high- throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughp ut data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.

Journal ArticleDOI
TL;DR: It is shown that quantifying longer pathways helps in determining which species have more important direct or indirect effects on others, and a keystone pattern of relative species importance, based on positionality in the interaction network, seems to characterize this community.
Abstract: We present a new method to asses the strength of indirect interactions and to indentify candidate keystone species in quantitative food webs. We apply this method to the structural analysis of a host-parasitoid community. The strength and symmetry of indirect interactions between 12 leaf-miner hosts and their 27 hymenopteran parasitoids are quantified. It is shown that (1) quantifying longer pathways helps in determining which species have more important direct or indirect effects on others, (2) a keystone pattern of relative species importance, based on positionality in the interaction network, seems to characterize this community, (3) considering longer pathways results in a characteristic “few strong - many weak” distribution of interaction strength, and (4) between the majority of species pairs the interaction is weakly asymmetrical. We emphasise that a very simple network algebra approach may offer important predictions on both species- and community-level patterns.

Journal ArticleDOI
TL;DR: This work uses k-cores of protein-protein interaction networks and phylogenetic analysis to predict the functions of some function-unknown proteins of E.coli.
Abstract: Comprehensive analysis of protein-protein interactions and amino acid sequences plays important roles to understand protein functions in molecular level. A k-core of a network or a graph is a subgraph in which all nodes are connected to at least k other nodes in the subgraph. In a protein-protein interaction network, a node represents a protein and an edge represents an interaction between proteins. A kcore of a protein-protein interaction network usually contains cohesive groups of proteins. On the other hand, phylogenetic analysis classifies proteins into groups based on the similarity of amino acid sequences. In this work, we use k-cores of protein-protein interaction networks and phylogenetic analysis to predict the functions of some function-unknown proteins of E.coli.

Journal ArticleDOI
TL;DR: It is shown that, in terms of global connectivities, the distribution of essential proteins is distinct from the background, which highlights a fundamental difference between the essential and the non-essential proteins in the network.
Abstract: Motivation Biologically significant information can be revealed by modeling large-scale protein interaction data using graph theory based network analysis techniques. However, the methods that are currently being used draw conclusions about the global features of the network from local connectivity data. A more systematic approach would be to define global quantities that measure (1) how strongly a protein ties with the other parts of the network and (2) how significantly an interaction contributes to the integrity of the network, and connect them with phenotype data from other sources. In this paper, we introduce such global connectivity measures and develop a stochastic algorithm based upon percolation in random graphs to compute them. Results We show that, in terms of global connectivities, the distribution of essential proteins is distinct from the background. This observation highlights a fundamental difference between the essential and the non-essential proteins in the network. We also find that the interaction data obtained from different experimental methods such as immunoprecipitation and two-hybrid techniques contribute differently to network integrities. Such difference between different experimental methods can provide insight into the systematic bias present among these techniques. Supplementary information The full list of our results can be found in the supplemental web site http://www.nas.nasa.gov/Groups/SciTech/nano/msamanta/projects/percolation/index.php

Journal ArticleDOI
TL;DR: A significant correlation between co-occurrence of TF binding sites and the vicinity in the protein interaction network is demonstrated and it is found that directly interacting transcription factors and those which are members of a protein complex are more likely to occur together as putative DNA-binding modules.

Posted Content
TL;DR: In this paper, the authors study the large-scale protein interaction network of yeast and show that the distribution of essential proteins is distinct from the background in terms of global connectivities, highlighting a fundamental difference between the essential and non-esse ntial proteins in the network.
Abstract: In this paper, we study the large-scale protein interaction network of yeast uti lizing a stochastic method based upon percolation of random graphs. In order to find the global features of connectivities in the network, we introduce numeric al measures that quantify (1) how strongly a protein ties with the other parts o f the network and (2) how significantly an interaction contributes to the integr ity of the network. Our study shows that the distribution of essential proteins is distinct from the background in terms of global connectivities. This observ ation highlights a fundamental difference between the essential and the non-esse ntial proteins in the network. Furthermore, we find that the interaction data o btained from different experimental methods such as immunoprecipitation and two- hybrid techniques possess different characteristics. We discuss the biological implications of these observations.

Book ChapterDOI
01 Jan 2003
TL;DR: A toy protein interaction network model is investigated, where the network grows by the processes of node duplication and particular form of random mutations, and a non-universal degree distribution.
Abstract: The rate equations are applied to investigate the structure of growing networks. Within this framework, the degree distribution of a network in which nodes are introduced sequentially and attach to an earlier node of degree k with rate A k ˜kγ is computed. Very different behaviors arise for γ 1. The rate equation approach is extended to determine the joint order-degree distribution, the degree correlations of neighboring nodes, as well as basic global properties. The complete solution for the degree distribution of a finite-size network is outlined. Some unusual properties associated with the most popular node are discussed; these follow simply from the order-degree distribution. Finally, a toy protein interaction network model is investigated, where the network grows by the processes of node duplication and particular form of random mutations. This system exhibits an infinite-order percolation transition, giant sample-specific fluctuations, and a non-universal degree distribution.

Journal ArticleDOI
TL;DR: This article shows that comparing the connectivities of individual proteins can reveal that a common tendency between methods has been missed by the pairwise comparison of interactions, and finds significant correlations between experimental methods and also between various in silico methods.

Journal ArticleDOI
TL;DR: A new layout algorithm with complexity management operations in visualizing a large-scale protein interaction network was developed and implemented in a program called InterViewer3, which simplifies a complex network by collapsing a group of nodes with the same interacting partners into a composite node and by replacing a clique with a star-shaped subgraph.
Abstract: Motivation: Protein-protein interaction networks often consist of thousands of nodes or more. This severely limits the utility of many graph drawing tools because they become too slow for an interactive analysis of the networks and because they produce cluttered drawings with many edge crossings. Results: An ew layout algorithm with complexity management operations in visualizing a large-scale protein interaction network was developed and implemented in a program called InterViewer3. InterViewer3 simplifies a complex network by collapsing a group of nodes with the same interacting partners into a composite node and by replacing a clique with a star-shaped subgraph. The experimental results demonstrated that InterViewer3 is one order of magnitude faster than the other drawing programs and that its complexity management is successful.

Journal ArticleDOI
TL;DR: This paper presents a model for the competition dynamics in the World Wide Web market, representing each site by a vertex in a graph and each competitive interaction as an edge, and evaluates the dynamical evolution of the fraction of the market controlled by the sites through a set of differential equations based on the Lotka–Volterra equations.
Abstract: This paper presents a model for the competition dynamics in the World Wide Web market We show that this problem is suitable to be analyzed in the framework of the theory of complex networks, representing each site by a vertex in a graph and each competitive interaction as an edge Once the topology of the interaction network has been defined, we evaluate the dynamical evolution of the fraction of the market controlled by the sites through a set of differential equations based on the Lotka–Volterra equations We show that, under these assumptions, some interesting and novel nonlinear effects emerge in this kind of markets

Journal ArticleDOI
TL;DR: The bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed and exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins is explored.
Abstract: The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.

Proceedings ArticleDOI
11 Aug 2003
TL;DR: This paper has performed the initial characterization of the protein interaction network in human brain tissue and classified and characterized all identified interactions based on gene ontology (GO) annotation of interacting loci, and described the "scale-free" topological structure of the network.
Abstract: Study of protein interaction networks is crucial to post-genomic systems biology. Aided by high-throughput screening technologies, biologists are rapidly accumulating protein-protein interaction data. Using a random yeast two-hybrid (R2H) process, we have performed large-scale yeast two-hybrid searches with approximately fifty thousand random human brain cDNA bait fragments against a human brain cDNA prey fragment library. From these searches, we have identified 13,656 unique protein-protein interaction pairs involving 4,473 distinct known human loci. In this paper, we have performed our initial characterization of the protein interaction network in human brain tissue. We have classified and characterized all identified interactions based on gene ontology (GO) annotation of interacting loci. We have also described the "scale-free" topological structure of the network.

Journal ArticleDOI
TL;DR: This paper proposes a new interconnection network, referred to as the arrangement-star network, which is constructed from the product of the star and arrangement networks, which makes it possible to efficiently embed grids, pipelines, as well as other computationally important topologies in a very natural manner.

Journal Article
TL;DR: A hypothesis of 'biological network evolution' is applied to explain the positive correlation between interaction and age and agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions.
Abstract: The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.