Showing papers by "Albert-László Barabási published in 2003"
TL;DR: In this article, the authors show that many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale free topology.
Abstract: Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks We find that several real networks, such as the Worldwideweb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems
1,981 citations
Book•
29 Apr 2003
TL;DR: In Linked, Albert-Laszlo Barabasi takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought.
Abstract: A cocktail party. A terrorist cell. Ancient bacteria. An international conglomerate. All are networks, and all are a part of a surprising scientific revolution. In Linked, Albert-Laszlo Barabasi, the nation's foremost expert in the new science of networks, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Barabasi shows that grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick and the Erdos--Renyi model brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future and of experiments in statistical mechanics on the internet, all vital parts of what would eventually be called the Barabasi--Albert model.
1,694 citations
TL;DR: A genetic footprinting technique is used for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media to identify 620 genes as essential and 3,126 genes as dispensable for growth under these conditions.
Abstract: Defining the gene products that play an essential role in an organism's functional repertoire is vital to understanding the system level organization of living cells. We used a genetic footprinting technique for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media. We identified 620 genes as essential and 3,126 genes as dispensable for growth under these conditions. Functional context analysis of these data allows individual functional assignments to be refined. Evolutionary context analysis demonstrates a significant tendency of essential E. coli genes to be preserved throughout the bacterial kingdom. Projection of these data over metabolic subsystems reveals topologic modules with essential and evolutionarily preserved enzymes with reduced capacity for error tolerance.
762 citations
Book•
01 Apr 2003
TL;DR: Barabasi and Albert-Laszlo Barabasi as mentioned in this paper showed that social networks, corporations, and living organisms are more similar than previously thought, and that grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information.
Abstract: A cocktail party. A terrorist cell. Ancient bacteria. An international conglomerate. All are networks, and all are a part of a surprising scientific revolution. In Linked, Albert-Laszlo Barabasi, the nation's foremost expert in the new science of networks, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Barabasi shows that grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick and the Erdos--Renyi model brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future and of experiments in statistical mechanics on the internet, all vital parts of what would eventually be called the Barabasi--Albert model.
753 citations
TL;DR: In this paper, the authors show that the rate at which nodes acquire links depends on the node's degree, offering direct quantitative support for the presence of preferential attachment, which is a key ingredient of many current models proposed to capture the topological evolution of complex networks.
Abstract: A key ingredient of many current models proposed to capture the topological evolution of complex networks is the hypothesis that highly connected nodes increase their connectivity faster than their less connected peers, a phenomenon called preferential attachment Measurements on four networks, namely the science citation network, Internet, actor collaboration and science coauthorship network indicate that the rate at which nodes acquire links depends on the node's degree, offering direct quantitative support for the presence of preferential attachment We find that for the first two systems the attachment rate depends linearly on the node degree, while for the last two the dependence follows a sublinear power law
572 citations
TL;DR: It is found that the conservation of proteins in distinct topological motifs correlates with the interconnectedness and function of that motif and also depends on the structure of the overall interactome topology.
Abstract: Understanding why some cellular components are conserved across species but others evolve rapidly is a key question of modern biology 1–3 . Here we show that in Saccharomyces cerevisiae, proteins organized in cohesive patterns of interactions are conserved to a substantially higher degree than those that do not participate in such motifs. We find that the conservation of proteins in distinct topological motifs correlates with the interconnectedness and function of that motif and also depends on the structure of the overall interactome topology. These findings indicate that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.
379 citations
01 Jan 2003
370 citations
09 May 2003
TL;DR: Recent advances in the characterization of complex networks are reviewed, focusing the emergence of the scale-free and the hierarchical architecture and the impact of the network topology on the ability to stop the spread of viruses in complex networks.
Abstract: Networks with complex topology describe systems as diverse as the cell or the World Wide Web. The emergence of these networks is driven by self-organizing processes that are governed by simple but generic laws. In the last three years it became clear that many complex networks, such as the Internet, the cell, or the world wide web, share the same large-scale topology. Here we review recent advances in the characterization of complex networks, focusing the emergence of the scale-free and the hierarchical architecture. We also present empirical results to demonstrate that the scale-free and the hierarchical property are shared by a wide range of complex networks. Finally, we discuss the impact of the network topology on our ability to stop the spread of viruses in complex networks.
137 citations
TL;DR: It is demonstrated quantitatively that protein complexes in the yeast Saccharomyces cerevisiae are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or nonessential), and share identical functional classification and cellular localization.
Abstract: Many important cellular functions are implemented by protein complexes that act as sophisticated molecular machines of varying size and temporal stability. Here we demonstrate quantitatively that protein complexes in the yeast Saccharomyces cerevisiae are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or nonessential), and share identical functional classification and cellular localization. This core is surrounded by a functionally mixed group of proteins, which likely represent short-lived or spurious attachments. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.
TL;DR: In this article, the authors use microarray data on 287 single gene deletion Saccharomyces cerevisiae mutant strains to elucidate generic relationships among perturbed transcriptomes, and find a combinatorial utilization of shared expression subpatterns within individual links, with increasing quantitative similarity among those that connect transcriptome states induced by the deletion of functionally related gene products.
Abstract: A central goal of postgenomic biology is the elucidation of the regulatory relationships among all cellular constituents that together comprise the ‘genetic network’ of a cell or microorganism. Experimental manipulation of gene activity coupled with the assessment of perturbed transcriptome (i.e., global mRNA expression) patterns represents one approach toward this goal, and may provide a backbone into which other measurements can be later integrated. We use microarray data on 287 single gene deletion Saccharomyces cerevisiae mutant strains to elucidate generic relationships among perturbed transcriptomes. Their comparison with a method that preferentially recognizes distinct expression subpatterns allows us to pair those transcriptomes that share localized similarities. Analyses of the resulting transcriptome similarity network identify a continuum hierarchy among the deleted genes, and in the frequency of local similarities that establishes the links among their reorganized transcriptomes. We also find a combinatorial utilization of shared expression subpatterns within individual links, with increasing quantitative similarity among those that connect transcriptome states induced by the deletion of functionally related gene products. This suggests a distinct hierarchical and combinatorial organization of the S. cerevisiae transcriptional activity, and may represent a pattern that is generic to the transcriptional organization of all eukaryotic organisms. Color versions of both the Supplementary Material and the article are available at http://angel.elte.hu/bioinf .
TL;DR: This work analyzes growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells and demonstrates the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.
Abstract: We analyse growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells. For the explicit demonstration of the scale-free nature and hierarchical organization of these graphs, a deterministic construction is also used. We demonstrate the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.
TL;DR: A numerical method is presented that filters out technology-derived contributions from the existing transcriptome data, leading to improved functional predictions and underlines the need to routinely search and compensate for inherent experimental bias when analyzing systematically collected, internally consistent biological data sets.
Abstract: Global transcriptome data is increasingly combined with sophisticated mathematical analyses to extract information about the functional state of a cell. Yet the extent to which the results reflect experimental bias at the expense of true biological information remains largely unknown. Here we show that the spatial arrangement of probes on microarrays and the particulars of the printing procedure significantly affect the log-ratio data of mRNA expression levels measured during the Saccharomyces cerevisiae cell cycle. We present a numerical method that filters out these technology-derived contributions from the existing transcriptome data, leading to improved functional predictions. The example presented here underlines the need to routinely search and compensate for inherent experimental bias when analyzing systematically collected, internally consistent biological data sets.
01 Jan 2003
TL;DR: This work focuses on the metabolic network of 43 distinct organisms and shows that many small, highly connected topologic modules combine in a hierarchical manner into larger, less cohesive units, their number and degree of clustering following a power law.
Abstract: Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that the scale-free nature and high clustering of real networks are the consequence of a hierarchical organization, implying that small groups of nodes form increasingly large groups in a hierarchical manner, while maintaining a scale-free topology. In hierarchical networks the clustering coefficient follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the World Wide Web, actor network, the Internet at the domain level and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems. We the focus on the metabolic network of 43 distinct organisms and show that many small, highly connected topologic modules combine in a hierarchical manner into larger, less cohesive units, their number and degree of clustering following a power law. Within Escherichia Coli we find that the uncovered hierarchical modularity closely overlaps with known metabolic functions.
Posted Content•
TL;DR: In this paper, the authors demonstrate that proteins organized in cohesive patterns of interactions are conserved to a significantly higher degree than those that do not participate in such motifs, indicating that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.
Abstract: Understanding why some cellular components are conserved across species, while others evolve rapidly is a key question of modern biology. Here we demonstrate that in S. cerevisiae proteins organized in cohesive patterns of interactions are conserved to a significantly higher degree than those that do not participate in such motifs. We find that the conservation of proteins within distinct topological motifs correlates with the motif's inter-connectedness and function and also depends on the structure of the overall interactome topology. These findings indicate that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.
TL;DR: It is found that a few agents are responsible for a significant fraction of the total bidding activity on the online market, and these agents exert an unproportional influence on the final price of the auctioned items.
Abstract: We characterize the statistical properties of a large number of agents on two major online auction sites. The measurements indicate that the total number of bids placed in a single category and the number of distinct auctions frequented by a given agent follow power-law distributions, implying that a few agents are responsible for a significant fraction of the total bidding activity on the online market. We find that these agents exert an unproportional influence on the final price of the auctioned items. This domination of online auctions by an unusually active minority may be a generic feature of all online mercantile processes.
Posted Content•
TL;DR: It is demonstrated quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype and share identical functional classification and cellular localization.
Abstract: Many important cellular functions are implemented by protein complexes that act as sophisticated molecular machines of varying size and temporal stability. Here we demonstrate quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or non-essential) and share identical functional classification and cellular localization. This core is surrounded by a functionally mixed group of proteins, which likely represent short-lived- or spurious attachments. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.