Abstract
Proteins are essential molecules of life in the cell and are involved in multiple and highly specialized tasks encoded in the amino acid sequence. In particular, protein function is closely related to fundamental units of protein structure called domains. Here, we investigate the distribution of kinds of domains in human cells. Our findings show that while the number of domain types shared by k proteins follows a scale-free distribution, the number of proteins composed of k types of domains decays as an exponential distribution. In contrast, previous data analyses and mathematical modeling reported a scale-free distribution for the protein domain distribution because the relation between kinds of domains and the number of domains in a protein was not considered. Based on this finding, we have developed an evolutionary model based on (1) growth process and (2) copy mechanism that explains the emergence of this mixing of exponential and scale-free distributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford (2003)
Barabási, A.-L., Oltvai, Z.N.: Network Biology: Understanding the Cells’s Functional Organization. Nature Reviews Genetics 5, 101–113 (2004)
Pastor-Satorras, R., Vespignani, A.: Evolution and Structure of the Internet: A Statistical Physics Approach. Cambridge University Press, Cambridge (2004)
Newman, M., Barabási, A.-L., Watts, D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2007)
Erdös, P., Rényi, A.: On the Evolution of Random Graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960)
Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.-L.: Hierarchical Organization of Modularity in Metabolic Networks. Science 297, 1551–1555 (2002)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network Motifs: Simple Building Blocks of Complex Networks. Science 298, 824–827 (2002)
Shen-Orr, S., Milo, R., Mangan, S., Alon, U.: Network Motifs in the Transcriptional Regulation Network of Escherichia coli. Nat. Genetics 31, 64–68 (2002)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature 435, 814–818 (2005)
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.-L.: The Large-scale Organization of Metabolic Networks. Nature 407, 651–654 (2000)
Jeong, H., Mason, S., Barabási, A.-L., Oltvai, Z.N.: Lethality and Centrality in Protein Networks. Nature 411, 41–42 (2001)
Go, M.: Modular Structural Units, Exons, and Function in Chicken Lysozyme. Proc. Natl. Acad. Sci. USA 80, 1964–1968 (1983)
Go, M.: Correlation of DNA Exonic Regions with Protein Structural Units in Haemoglobin. Nature 291, 90–92 (1981)
Wuchty, S.: Scale-free Behavior in Protein Domain Networks. Mol. Biol. Evo. 18, 1694–1702 (2001)
Karev, G.P., Wolf, Y.I., Rzhetsky, A.Y., Berezovskaya, F.S., Koonin, E.V.: Birth and Death of Protein Domains: A Simple Model of Evolution Explains Power Law Behavior. BMC Evo. Biol. 2, 18 (2002)
Nacher, J.C., Hayashida, M., Akutsu, T.: Protein Domain Networks: Scale-free Mixing of Positive and Negative Exponents. Physica A 367, 538–552 (2006)
Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random Graphs with Arbitrary Degree Distributions and Their Applications. Phys. Rev. E 64, 026118 (2001)
Krapivsky, P.L., Redner, S., Leyvraz, F.: Connectivity of Growing Random Networks. Phys. Rev. Lett. 85, 4629 (2000)
Kim, J., Krapivsky, P.L., Kahng, B., Redner, S.: Infinite-order Percolation and Giant Fluctuations in a Protein Interaction Network. Phys. Rev. E. 66, 055101 (2002)
Ispolatov, I., Krapivsky, P.L., Yuryev, A.: Duplication-divergence Model of Protein Interaction Network. Phys. Rev. E. 71, 061911 (2005)
Krapivsky, P.L., Redner, S.: Organization of Growing Random Networks. Phys. Rev. E. 63, 066123 (2001)
Barrat, A., Pastor-Satorras, R.: Rate Equation Approach for Correlations in Growing Network Models. Phys. Rev. E 71, 036127 (2005)
Krapivsky, P.L., Redner, S.: Rate Equation Approach for Growing Networks. Lecture Notes in Physics 625, 3–22 (2003)
Ergün, G.: Human Sexual Contact Network as a Bipartite Graph. Physica A 308, 483–488 (2002)
The UniProt Consortium: The Universal Protein Resource (UniProt). Nucleic Acids Research 36, D190–D195 (2008)
Kersey, P., Bower, L., Morris, L., Horne, A., Petryszak, R., Kanz, C., Kanapin, A., Das, U., Michoud, K., Phan, I., Gattiker, A., Kulikova, T., Faruque, N., Duggan, K., Mclaren, P., Reimholz, B., Duret, L., Penel, S., Reuter, I., Apweiler, R.: Integr8 and Genome Reviews: Integrated Views of Complete Genomes and Proteomes. Nucleic Acids Research 33, D297–D302 (2005)
Finn, R.D., Tate, J., Mistry, J., Coggill, P.C., Sammut, J.S., Hotz, H.R., Ceric, G., Forslund, K., Eddy, S.R., Sonnhammer, E.L., Bateman, A.: The Pfam protein families database. Nucleic Acids Research 36, D281–D288 (2008)
Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A., Gerstein, M.: Genomic Analysis of Regulatory Network Dynamics Reveals Large Topological Changes. Nature 431, 308–312 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Nacher, J.C., Ochiai, T., Hayashida, M., Akutsu, T. (2009). A Bipartite Graph Based Model of Protein Domain Networks. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-02466-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02465-8
Online ISBN: 978-3-642-02466-5
eBook Packages: Computer ScienceComputer Science (R0)