Showing papers by "Albert-László Barabási published in 2004"
TL;DR: This work states that rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize the view of biology and disease pathologies in the twenty-first century.
Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.
7,475 citations
1,487 citations
TL;DR: A flux balance analysis of the metabolism of Escherichia coli strain MG1655 shows that network use is highly uneven, which probably represents a universal feature of metabolic activity in all cells, with potential implications for metabolic engineering.
Abstract: Cellular metabolism, the integrated interconversion of thousands of metabolic substrates through enzyme-catalysed biochemical reactions, is the most investigated complex intracellular web of molecular interactions. Although the topological organization of individual reactions into metabolic networks is well understood, the principles that govern their global functional use under different growth conditions raise many unanswered questions. By implementing a flux balance analysis of the metabolism of Escherichia coli strain MG1655, here we show that network use is highly uneven. Whereas most metabolic reactions have low fluxes, the overall activity of the metabolism is dominated by several reactions with very high fluxes. E. coli responds to changes in growth conditions by reorganizing the rates of selected fluxes predominantly within this high-flux backbone. This behaviour probably represents a universal feature of metabolic activity in all cells, with potential implications for metabolic engineering.
694 citations
TL;DR: This work compares four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large‐scale properties and the impact of the proteins' function and cellular localization on the network topology.
Abstract: The elucidation of the cell’s large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network’s generic large-scale properties and the impact of the proteins’ function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network’s structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.
623 citations
TL;DR: It is shown that a network's large-scale topological organization and its local subgraph structure mutually define and predict each other, as confirmed by direct measurements in five well studied cellular networks.
Abstract: Recent evidence indicates that the abundance of recurring elementary interaction patterns in complex networks, often called subgraphs or motifs, carry significant information about their function and overall organization. Yet, the underlying reasons for the variable quantity of different subgraph types, their propensity to form clusters, and their relationship with the networks' global organization remain poorly understood. Here we show that a network's large-scale topological organization and its local subgraph structure mutually define and predict each other, as confirmed by direct measurements in five well studied cellular networks. We also demonstrate the inherent existence of two distinct classes of subgraphs, and show that, in contrast to the low-density type II subgraphs, the highly abundant type I subgraphs cannot exist in isolation but must naturally aggregate into subgraph clusters. The identified topological framework may have important implications for our understanding of the origin and function of subgraphs in all complex networks.
318 citations
TL;DR: It is shown that the observed scaling can explain the competition between the system's internal collective dynamics and changes in the external environment, allowing us to predict the relevant scaling exponents.
Abstract: Most complex networks serve as conduits for various dynamical processes, ranging from mass transfer by chemical reactions in the cell to packet transfer on the Internet. We collected data on the time dependent activity of five natural and technological networks, finding that for each the coupling of the flux fluctuations with the total flux on individual nodes obeys a unique scaling law. We show that the observed scaling can explain the competition between the system's internal collective dynamics and changes in the external environment, allowing us to predict the relevant scaling exponents.
248 citations
TL;DR: Individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the E. coli transcriptional regulatory network and play a central role in defining its global topological organization.
Abstract: Background
Transcriptional regulation of cellular functions is carried out through a complex network of interactions among transcription factors and the promoter regions of genes and operons regulated by them.To better understand the system-level function of such networks simplification of their architecture was previously achieved by identifying the motifs present in the network, which are small, overrepresented, topologically distinct regulatory interaction patterns (subgraphs). However, the interaction of such motifs with each other, and their form of integration into the full network has not been previously examined.
247 citations
TL;DR: By capturing the simultaneous activity of several of the system's components, the method allows us to systematically determine the origin of fluctuations in various real systems, finding that while the Internet and the computer chip have robust internal dynamics, highway and Web traffic are driven by external demand.
Abstract: The observable behavior of a complex system reflects the mechanisms governing the internal interactions between the system's components and the effect of external perturbations. Here we show that by capturing the simultaneous activity of several of the system's components we can separate the internal dynamics from the external fluctuations. The method allows us to systematically determine the origin of fluctuations in various real systems, finding that while the Internet and the computer chip have robust internal dynamics, highway and Web traffic are driven by external demand. As multichannel measurements are becoming the norm in most fields, the method could help uncover the collective dynamics of a wide array of complex systems.
119 citations
Posted Content•
TL;DR: In this paper, the effect of weight assignment and network topology on the organization of complex networks was explored using the minimum spanning tree (MST) to explore the impact of weak links.
Abstract: A complete understanding of real networks requires us to understand the consequences of the uneven interaction strengths between a system's components. Here we use the minimum spanning tree (MST) to explore the effect of weight assignment and network topology on the organization of complex networks. We find that if the weight distribution is correlated with the network topology, the MSTs are either scale-free or exponential. In contrast, when the correlations between weights and topology are absent, the MST degree distribution is a power-law and independent of the weight distribution. These results offer a systematic way to explore the impact of weak links on the structure and integrity of complex networks.
110 citations
TL;DR: The following discussion is an edited summary of the public debate started during the conference "Growing Networks and Graphs in Statistical Physics, Finance, Biology and Social Systems" held in Rome in September 2003 as discussed by the authors.
Abstract: The following discussion is an edited summary of the public debate started during the conference "Growing Networks and Graphs in Statistical Physics, Finance, Biology and Social Systems" held in Rome in September 2003 Drafts documents were circulated electronically among experts in the field and additions and follow-up to the original discussion have been included Among the scientists participating to the discussion L A N Amaral, A Barrat, A L Barabasi, G Caldarelli, P De Los Rios, A Erzan, B Kahng, R Mantegna, J F F Mendes, R Pastor-Satorras, A Vespignani are acknowledged for their contributions and editing
63 citations
01 Aug 2004-Nuclear Instruments & Methods in Physics Research Section B-beam Interactions With Materials and Atoms
TL;DR: In this article, the Sigmund's theory of ion sputtering, modified for the case of self-affine surfaces, was employed to compute the sputter yields of ion-eroded surfaces, and it was shown that the changes in the sputtering yield, associated with the non-planar surface morphology, are strongly dependent on the parameters characterizing the surface roughness (such as the saturation width and the correlation length) and the incident ion beam.
Abstract: As extensive experimental studies have shown, under certain conditions, ion bombardment of solid targets induces a random (self-affine) morphology on the ion-eroded surfaces. The rough morphology development is known to cause substantial variations in the sputtering yields. In this article, we present a theoretical model describing the sputter yields from random, self-affine surfaces subject to energetic ion bombardment. We employ the Sigmund’s theory of ion sputtering, modified for the case of self-affine surfaces, to compute the sputter yields. We find that the changes in the sputtering yield, associated with the non-planar surface morphology, are strongly dependent on the parameters characterizing the surface roughness (such as the saturation width and the correlation length) and the incident ion beam (such as the incident ion energy and the deposited energy widths). It is shown that, for certain ranges of the parameters variations, surface roughness leads to substantial enhancements in the yield, with magnitude of the effect being more than 100%, as compared to the flat surface value. Furthermore, we find that, depending on the interplay between these parameters, the surface roughness can both enhance and suppress the sputter yields.
01 Aug 2004-Nuclear Instruments & Methods in Physics Research Section B-beam Interactions With Materials and Atoms
TL;DR: In this article, the effect of rippled surface morphology on sputtering yields was investigated and it was shown that the surface morphology development may cause substantial variations in the sputter yields, depending on a complex interplay between the parameters characterizing the ripple structure and the incident ion beam.
Abstract: Off-normal ion bombardment of solid targets with energetic particles often leads to development of periodically modulated structures on the surfaces of eroded materials. Ion-induced surface roughening, in its turn, causes sputtering yield changes. We report on a comprehensive theoretical study of the effect of rippled surface morphology on the sputtering yields. The yield is computed as a function of the parameters characterizing the surface morphology and the incident ion beam, using the Sigmund's theory of ion sputtering. We find that the surface morphology development may cause substantial variations in the sputter yields, depending on a complex interplay between the parameters characterizing the ripple structure and the incident ion beam. For certain realizations of the ripple structure, the surface morphology is found to induce enhanced, relative to the flat surface value, sputtering yields. On the other hand, there exist regimes in which the sputtering yield is suppressed by the surface roughness below the flat surface result. We confront the obtained theoretical results with available experimental data and find that our model provides an excellent qualitative and, in some cases, quantitative agreement with the results of experimental studies.
TL;DR: In this paper, the authors collected data on the time dependent activity of five natural and technological networks, finding evidence of orders of magnitude differences in the fluxes of individual nodes, and proposed a method to separate these two components, allowing them to predict the relevant scaling exponents.
Abstract: Most complex networks serve as conduits for various dynamical processes, ranging from mass transfer by chemical reactions in the cell to packet transfer on the Internet. We collected data on the time dependent activity of five natural and technological networks, finding evidence of orders of magnitude differences in the fluxes of individual nodes. This dynamical inhomogeneity reflects the emergence of localized high flux regions or “hot spots”, carrying an overwhelming fraction of the network’s activity. We find that each system is characterized by a unique scaling law, coupling the flux fluctuations with the total flux on individual nodes, a result of the competition between the system’s internal collective dynamics and changes in the external environment. We propose a method to separate these two components, allowing us to predict the relevant scaling exponents. As high fluctuations can lead to dynamical bottlenecks and jamming, these findings have a strong impact on the predictability and failure prevention of complex transportation networks.
TL;DR: This paper surveys the most prominent characteristics of biological networks focusing on the emergence of scale-free architecture and hierarchical arrangement of functional modules and presents empirical evidence that cohesive parts of the protein interaction network have a significantly higher tendency to be evolutionary conserved.
Abstract: Network principles describe uniformly systems as diverse as the cell or the Internet. The emergence of these networks is driven by self-organizing processes that are governed by simple but generic laws. While unraveling the complex and interwoven systems of different interacting units, it has become clear that the topology of networks of different origin share the same characteristics on the large scale. In biological systems, networks appear in many different disguises ranging from protein interactions to metabolic networks. In this paper, we survey the most prominent characteristics of biological networks focusing on the emergence of scale-free architecture and hierarchical arrangement of functional modules. Finally, we present empirical evidence that cohesive parts of the protein interaction network have a significantly higher tendency to be evolutionary conserved.
TL;DR: The empirical energies obtained from the restructuring can be described by a universal function f (k) approximately -k ln k , which is consistent with and justifies the validity of the preferential attachment rule proposed for growing networks.
Abstract: We provide a method to deduce the preferences governing the restructuring dynamics of a network from the observed rewiring of the edges. Our approach is applicable for systems in which the preferences can be formulated in terms of a single-vertex energy function with fskd being the contribution of a node of degree k to the total energy, and the dynamics obeys the detailed balance. The method is first tested by Monte Carlo simulations of restructuring graphs with known energies; then it is used to study variations of real network systems ranging from the coauthorship network of scientific publications to the asset graphs of the New York Stock Exchange. The empirical energies obtained from the restructuring can be described by a universal function fsk d, ˛k ln k, which is consistent with and justifies the validity of the preferential attachment rule proposed for growing networks.
01 Jan 2004
TL;DR: In this paper, the authors download all online-auction bidding histories closing on a single day on eBay, including 264,073 auctioned items, involved by 384,058 distinct agents, finding emerging behaviors in electronic biddings.
Abstract: We downloaded all online-auction bidding histories closing on a single day on eBay, including 264,073 auctioned items, involved by 384,058 distinct agents, finding emerging behaviors in electronic biddings. The data analysis indicates 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. To confirm the validity of our findings in different markets and time spans, we collected the data over a one year period from eBay’s Korean partner auction.co.kr, and confirmed the findings obtained from the eBay data. This domination of online auctions by a few unusually active minorities may be a generic feature of all online mercantile processes.