The large-scale organization of metabolic networks

Jeong, Hawoong; Tombor, B.; Albert, Réka; Oltvai, Zoltán N.; Barabási, Albert László

doi:10.1038/35036627

Cited by 4,089 publications

(1,886 citation statements)

References 24 publications

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“…The simultaneous emergence of an inhomogeneous structure in both metabolic- 5,11 and protein interaction networks indicates the evolutionary selection of a common large scale structure of biological networks, and strongly suggests that future systematic protein-protein interaction studies in other organisms will uncover an essentially identical protein network topology. The correlation between the connectivity and indispensability of a given protein confirms that despite the importance of individual biochemical function and genetic redundancy, the robustness against mutations in yeast is also derived from the organization of interactions and topologic position of individual proteins 12 .…”

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confidence: 99%

Lethality and centrality in protein networks

Jeong

Mason

Barabási

et al. 2001

Nature

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3,616

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Lethality and centrality in protein networksCell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms. Currently, we witness the emergence of a post-genomic view that expands the protein's role, regarding it as an element in a network of proteinprotein interactions as well, with a 'contextual' or 'cellular' function within functional modules 1, 2 . Here we provide quantitative support for this paradigm shift by demonstrating that the phenotypic consequence of a single gene deletion in the yeast, S. cerevisiae, is affected, to a high degree, by the topologic position of its protein product in the complex, hierarchical web of molecular interactions.The S. cerevisiae protein-protein interaction network we investigate has 1870 proteins as nodes, connected by 2240 identified direct physical interactions, and is derived from combined, nonoverlapping data 3, 4 obtained mostly by systematic two-hybrid analyses 3 . Due to its size, a complete map of the network (Fig. 1a), while informative, in itself offers little insight into its large-scale characteristics. Thus, our first goal was to identify the architecture of this network, determining if it is best described by an inherently uniform exponential topology with proteins on average possessing the same number of links, or by a highly heterogeneous scale-free topology with proteins having widely different connectivities 5 . As we show in Fig. 1b, the probability that a given yeast protein interacts with k other yeast proteins follows a power-law 5 with an exponential cutoff 6 at k c ≅ 20, a topology that is also shared by the protein-protein interaction network of the bacterium, H. pylori 7 . This indicates that the network of protein interactions in two separate organisms forms a highly inhomogeneous scale-free network in which a few highly connected proteins play a central role in mediating interactions among numerous, less connected proteins.An important known consequence of the inhomogeneous structure is the network's simultaneous tolerance against random errors coupled with fragility against the removal of the most connected nodes 8 . Indeed, we find that random mutations in the genome of S. cerevisiae, -modeled by the removal of randomly selected yeast proteins-, do not affect the overall topology of the network. In contrast, when

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confidence: 99%

Lethality and centrality in protein networks

Jeong

Mason

Barabási

et al. 2001

Nature

Self Cite

4,766

154

3,616

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“…We refer to this network representation as the finite-state representation, by analogy with finite-state machines, 61 and contrast it with the commonly used representations of metabolic networks as interaction networks, in which individual metabolites are network nodes and network edges connect all reaction participants with each other. 5,32,62,63 The finite-state representation of the reaction network is a directed network with edge weights given by thermodynamic and / or kinetic parameter values.…”

Section: Probing the Chemistry Of The Formose Reaction Networkmentioning

confidence: 99%

“…1,2 Characteristic features of complex reactions include branching and interference of reaction pathways, autocatalysis, and product inhibition and are observed in systems as varied as transition-metal catalysis, 3 cell metabolism, 4,5 and polymerization. 1,6,7 A better understanding of the network effects in these complex reactions offers means for influencing their dynamics and product composition.…”

Section: Introductionmentioning

confidence: 99%

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Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

Rappoport

Galvin

Zubarev

et al. 2014

J. Chem. Theory Comput.

132

155

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While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here we present heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction of prebiotic evolution reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

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“…Studying diverse biological systems, it has been shown that biochemical pathways are not arranged with random numbers of connectivities; instead, they display a scale-free character [1]. A crucial characteristic of this type of system is that it is largely controlled by a few hubs or nexuses, which dominate the overall connectivity and stability of the graph.…”

Section: Introductionmentioning

confidence: 99%

Mining DNA microarray data using a novel approach based on graph theory

et al. 2001

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The recent demonstration that biochemical pathways from diverse organisms are arranged in scale-free, rather than random, systems [Jeong et al., Nature 407 (2000) 651^654], emphasizes the importance of developing methods for the identification of biochemical nexuses^the nodes within biochemical pathways that serve as the major input/output hubs, and therefore represent potentially important targets for modulation. Here we describe a bioinformatics approach that identifies candidate nexuses for biochemical pathways without requiring functional gene annotation; we also provide proof-ofprinciple experiments to support this technique. This approach,

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The large-scale organization of metabolic networks

Cited by 4,089 publications

References 24 publications

Lethality and centrality in protein networks

Lethality and centrality in protein networks

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

Mining DNA microarray data using a novel approach based on graph theory

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