Abstract
Many biocomplex networks such as the protein interaction networks and the metabolic networks exhibit an emerging pattern that the distribution of the number of connections of a protein or substrate follows a power law. As the network theory is developed recently, several quantities describing network structure such as modularity and degree-degree correlation have been introduced. Here we investigate and compare the structural properties of the yeast protein networks for different datasets with those quantities. More-over, we introduce a new quantity, called the load, characterizing the amount of signal passing through a vertex. It is shown that the load distribution also follows a power law, and its characteristics are related to the structure of the core part of the biocomplex networks.
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References
Ziemelis K, Allen L. Complex systems, and following review articles on complex systems. Nature 2001; 410:241.
Gallagher R, Appenzeller T. Complex systems, and following viewpoint articles on complex systems. Science 1999; 284:87.
Strogatz SH. Exploring complex networks. Nature 2001; 410:268–276.
Dorogovtsev SN, Mendes JFF. Evolution of networks. Oxford: Oxford University Press, 2003.
Newman MEJ. The structure and function of complex networks. SLAM Rev 2003; 45:167.
Gavin AC et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002; 415:141–147.
Marcotte EM et al. A combined algorithm for genome-wide prediction of protein function. Nature 1999; 402:83–86.
Uetz P et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000; 403:623–627.
Erdos P, Rényi A. On the evolution of random graph. Publ Math Inst Hung Acad Sci Ser A 1960; 5:17.
Barabási A-L, Albert R, Jeong H. Mean-field theory of scale-free networks. Physica A 1999; 272:173.
Albert R, Jeong H, Barabási A-L. Diameter of the world wide web. Nature 1999; 401:130–131.
Huberman BA, Adamic LA. Growth dynamics of the world wide web. Nature 1999; 401:131.
Broder A et al. Graph structure of the world wide web. Computer Networks 2000; 33:309.
Faloutsos M. Faloutsos P, Faloutsos C. On power-law relationship in the internet topology. Comput Commun Rev 1999; 29:251.
Pastor-Satorras R, Vázquez A, Vespignani A. Dynamical and correlation properties of the Internet. Phys Rev Lett 2001; 87:258701.
Goh K-I, Kahng B, Kim D. Fluctuation-driven dynamics of the Internet topology. Phys Rev Lett 2002; 88:108701.
Redner S. How popular is your paper? Eur Phys J B 1998; 4:131.
Newman MEJ. The structure of scientific collaboration. Proc Natl Acad Sci USA 2001; 98:404.
Barabási A-L, Jeong H, Ravasz R et al. On the topology of the scientific collaboration networks. Physica A 2002; 311:590–614.
Jeong H, Tombor B, Albert R et al. Large-scale organization of metabolic networks. Nature 2000; 407:651.
Ravasz E et al. Hierachical organization of modularity in metabolic networks. Science 2002; 297:1551–1555.
Ravasz E, Barabási A-L. Hierachical organization in complex networks. Phys Rev E 2003; 67:026112.
Mews HW et al. MIPS: Analysis and annotation of proteins from whole genomes. Nud Acids Res 2004; 32:D41–D44.
Salwinski L et al. The database of interacting proteins: 2004 update. Nucl Acids Res 2004; 32:D449–D451.
Bader GD, Betel D, Hogue CW. BIND: The biomolecular interaction network database. Nucl Acids Res 2003; 31:248–250.
Ito T et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001; 98:4569–4574.
Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nat Biotechnol 2000; 18:1257–1261.
Tong AH et al. A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science 2002; 295:321–324.
Ho Y et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002; 415:180–183.
Jeong H et al. Lethality and centrality in protein networks. Nature 2001; 411:41–42.
Wagner A. How the global structure of protein interaction networks evolves. Proc R Soc Lond B 2003; 270:457–466.
Newman MEJ. Assortative mixing in networks. Phys Rev Lett 2002; 89:208701.
Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science 2002; 296:910–913.
Goh K-I, Kahng B, Kim D. Universal behavior of load distribution in scale-free networks. Phys Rev Lett 2001; 87:278701.
Freeman LC. A set of measure of centrality based on betweenness. Sociometry 1977; 40:35.
Newman MEJ. Scientific collaboration networks II: Shortest paths, weighted networks, and centrality. Phys Rev E 2001; 64:016132.
Brandes U. A faster algorithm for betweenness centrality. J Math Sociol 2001; 25:163.
Goh K-I, Kahng B, Kim D. Packet transport and load distribution in scale-free network models. Physica A 2003; 318:72.
Goh K-I, Oh E, Jeong H et al. Classification of scale-free networks. Proc Natl Acad Sci USA 2002; 99:12583.
Barabási A-L, Albert R. Emergence of scaling in random networks. Science 1999; 286:509.
Solé R, Pastor-Satorras R, Smith E et al. A model of large-scale proteome evolution. Adv Complex Syst 2002; 5:43.
Meyer D. University of Oregon Route Views Archive Project 2001 (http://archive.routeviews.org).
Jung S, Kim S, Kahng B. Geometric fractal growth model for scale-free networks. Phys Rev E 2002; 65:056101.
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Goh, KI., Kahng, B., Kim, D. (2006). Graphical Analysis of Biocomplex Networks and Transport Phenomena. In: Power Laws, Scale-Free Networks and Genome Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-33916-7_2
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DOI: https://doi.org/10.1007/0-387-33916-7_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-25883-6
Online ISBN: 978-0-387-33916-0
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