zbMATH — the first resource for mathematics

Bio-entity network for analysis of protein-protein interaction networks. (English) Zbl 1303.93030
Summary: Protein-protein interactions (PPIs) are vitally important for every process in a living cell. Information about these interactions can improve our understanding of diseases and provide the basis to revolutionize therapeutic treatments. However, since PPIs are involved with extremely complicated biological processes, it is necessary to develop novel tools to deal with this kind of network systems. To realize this, a bio-entity network approach is introduced to show the topology structure of dynamic and collective performances of PPI networks and analyze the variance of the protein node that plays an important role in the PPI network. Also, spectrum analysis is used to capture the discrete and stochastic feature of PPIs. The yeast protein interaction network is considered as a paradigm. It is demonstrated that the proposed approach can easily and clearly identify the hub-proteins that have the most impact on the PPI system concerned. It is expected that the bio-entity network approach as presented in this paper might become a useful tool in system biology.

93A30 Mathematical modelling of systems (MSC2010)
92C42 Systems biology, networks
Full Text: DOI
[1] Tomita, E-CELL: software environment for whole-cell simulation, Bioinformatics 15 pp 72– (1999) · doi:10.1093/bioinformatics/15.1.72
[2] Chou, Gene Cloning & Expression Technologies (2002)
[3] Vollert, The phox homology (PX) domain protein interaction network in yeast, Mol. Cell. Proteomics 3 pp 1053– (2004) · doi:10.1074/mcp.M400081-MCP200
[4] Chou, Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein, J. Proteome Res. 4 pp 1681– (2005) · doi:10.1021/pr050145a
[5] Chou, Review: structural bioinformatics and its impact to biomedical science, Curr. Med. Chem. 11 pp 2105– (2004) · doi:10.2174/0929867043364667
[6] Zhang, Identification of the N-terminal functional domains of Cdk5 by molecular truncation and computer modeling, PROTEINS: Struc. Funct. Genet. 48 pp 447– (2002) · doi:10.1002/prot.10173
[7] Kanehisa, The KEGG resources for deciphering the genome, Nucleic Acids Res. 32 pp D277– (2004) · Zbl 05435934 · doi:10.1093/nar/gkh063
[8] Ben-Hur, Kernel methods for predicting protein-protein interactions, Bio-informatics 21 pp i38– (2005)
[9] Harada, The emergence of controllable transient behavior using an agent diversification strategy, IEEE Trans. SMC Part A-Syst. Humans 33 (5) pp 589– (2003) · doi:10.1109/TSMCA.2003.817373
[10] Hucka, The systems biology markup language (SBML): a medium for representation and exchange of biomedical network models, Bioinformatics 19 pp 524– (2003) · doi:10.1093/bioinformatics/btg015
[11] González-Díaz , H. Y. González-Díaz L. Santana F. M. Ubeira E. Uriarte Proteomics, networks, and connectivity indices 10.1002/pmic.200700638 2008
[12] d’Inverno, Engineering Self-Organising Systems: Methodologies and Applications (2005)
[13] Walker, The epitheliome: agent-based modelling of the social behaviour of cells, Biosystems 76 pp 89– (2004) · doi:10.1016/j.biosystems.2004.05.025
[14] Yuan, Combination framework of rendezvous algorithm for multi-agent systems with limited sensing ranges, Asian J. Control 13 (2) pp 283– (2011) · Zbl 1222.93008 · doi:10.1002/asjc.178
[15] Ding, A new framework for computational intelligence: bio-network architecture, J. Intell. Syst. 2 (2) pp 26– (2007)
[16] Ding, Design of a bio-network architecture based on immune emergent computation, Control Decis. 18 (2) pp 185– (2003)
[17] Ding, A bio-inspired emergent system for intelligent Web service composition and management, Knowledge-Based Syst. 20 (5) pp 457– (2007) · doi:10.1016/j.knosys.2007.01.007
[18] Ren, Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example, Amino Acid 35 (3) pp 565– (2008) · doi:10.1007/s00726-008-0081-2
[19] Girvan, Community structure in social and biological networks, Proc. Natl. Acad. Sci. 99 pp 7821– (2002) · Zbl 1032.91716 · doi:10.1073/pnas.122653799
[20] Tong, Global mapping of the yeast genetic interaction network, Science 303 pp 808– (2004) · doi:10.1126/science.1091317
[21] Roseli, Neural network based algorithm for dynamic system optimization, Asian J. Control 3 (2) pp 131– (2001)
[22] Graham, Analysis and design of networked control systems using the additive noise model methodology, Asian J. Control 12 (4) pp 443– (2010)
[23] Andraos, Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs, Can. J. Chem. 86 pp 342– (2008) · doi:10.1139/v08-020
[24] Chou, Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases, Anal. Biochem. 221 pp 217– (1994) · doi:10.1006/abio.1994.1405
[25] Qi, New 3D graphical representation of DNA sequence based on dual nucleotides, J. Theor. Biology 249 pp 681– (2007) · doi:10.1016/j.jtbi.2007.08.025
[26] Aguero-Chapin, Comparative study of topological indices of Macro/Supra-molecular RNA complex networks, J. Chem. Inf. Model. 48 pp 2265– (2008) · doi:10.1021/ci8001809
[27] Cruz-Monteagudo, Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: the blood proteome case, Polymer 49 pp 5575– (2008) · doi:10.1016/j.polymer.2008.09.070
[28] Xiao, Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image, J. Theor. Biology 254 pp 691– (2008) · Zbl 1400.92416 · doi:10.1016/j.jtbi.2008.06.016
[29] Mewes, MIPS: analysis and annotation of proteins from whole genomes, Nucleic Acids Res. 32 pp D41– (2004) · Zbl 05436334 · doi:10.1093/nar/gkh092
[30] Hughes, Functional discovery via a compendium of expression profiles, Cell 102 pp 109– (2000) · doi:10.1016/S0092-8674(00)00015-5
[31] Gibson, Proc. 9th ACM Conf. Hypertext Hypermedia (1998)
[32] Goldberg, Assessing experimentally derived interactions in a small world, Proc. Natl. Acad. Sci. U.S.A. 100 pp 4372– (2003) · Zbl 1132.92327 · doi:10.1073/pnas.0735871100
[33] Yu, Computational approaches for predicting protein-protein interactions: a survey, J. Med. Syst. 30 pp 39– (2006) · doi:10.1007/s10916-006-7402-3
[34] Bang , S. J. Choi J. Park S. J. Park A Hub-protein based visualization of large protein-protein interaction networks 1217 1220 2007
[35] Qi, A mathematical model of P53 gene regulatory networks under radiotherapy, Biosystems 90 pp 698– (2007) · doi:10.1016/j.biosystems.2007.02.007
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.