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PLOS ONE  2013 

Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool

DOI: 10.1371/journal.pone.0078059

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Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.


[1]  B?de C, Kovács IA, Szalay MS, Palotai R, Korcsmáros T, et al. (2007) Network analysis of protein dynamics. FEBS Lett 581: 2776–2782.
[2]  Di Paola L, De Ruvo M, Paci P, Santoni D, Giuliani A (2013) Protein contact networks: an emerging paradigm in chemistry. Chem Rev 113: 1598–1613.
[3]  Vishveshwara S, Ghosh A, Hansia P (2009) Intra and inter-molecular communications through protein structure network. Curr Protein Pept Sci 10: 146–160.
[4]  Martin AJM, Vidotto M, Boscariol F, Di Domenico T, Walsh I, et al. (2011) RING: networking interacting residues, evolutionary information and energetics in protein structures. Bioinformatics 27: 2003–2005.
[5]  Doncheva NT, Klein K, Domingues FS, Albrecht M (2011) Analyzing and visualizing residue networks of protein structures. Trends Biochem Sci 36: 179–182.
[6]  Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5: 101–113.
[7]  Chatr-Aryamontri A, Breitkreutz B-J, Heinicke S, Boucher L, Winter A, et al. (2013) The BioGRID interaction database: 2013 update. Nucleic Acids Res 41: D816–D823.
[8]  Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, et al. (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39: D561–D568.
[9]  Xenarios I (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30: 303–305.
[10]  Zhong Q, Simonis N, Li Q-R, Charloteaux B, Heuze F, et al. (2009) Edgetic perturbation models of human inherited disorders. Mol Syst Biol 5: 321.
[11]  Antal MA, B?de C, Csermely P (2009) Perturbation waves in proteins and protein networks: applications of percolation and game theories in signaling and drug design. Curr Protein Pept Sci 10: 161–172.
[12]  Garg A, Mohanram K, De Micheli G, Xenarios I (2012) Implicit methods for qualitative modeling of gene regulatory networks. Methods Mol Biol 786: 397–443.
[13]  Wang R-S, Albert R (2011) Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 5: 44.
[14]  Gong Y, Zhang Z (2007) Alternative pathway approach for automating analysis and validation of cell perturbation networks and design of perturbation experiments. Ann N Y Acad Sci 1115: 267–285.
[15]  Gong Y, Zhang Z (2007) CellFrame: a data structure for abstraction of cell biology experiments and construction of perturbation networks. Ann N Y Acad Sci 1115: 249–266.
[16]  Shmulevich I, Dougherty ER, Zhang W (2002) Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics 18: 1319–1331.
[17]  Stojmirovi A, Yu Y-K, Stojmirovi? A (2009) ITM Probe: analyzing information flow in protein networks. Bioinformatics 25: 2447–2449.
[18]  Li F, Li P, Xu W, Peng Y, Bo X, et al. (2010) PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks. Bioinformatics 26: 275–277.
[19]  Calzone L, Fages F, Soliman S (2006) BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22: 1805–1807.
[20]  Rothkegel A, Lehnertz K (2012) Conedy: a scientific tool to investigate complex network dynamics. Chaos 22: 013125.
[21]  Farkas IJ, Korcsmáros T, Kovács IA, Mihalik á, Palotai R, et al. (2011) Network-based tools for the identification of novel drug targets. Sci Signal 4: pt3.
[22]  De Almeida RMC, Lemke N, Jund P, Jullien R, Campbell IA, et al. (2001) Dynamics of complex systems above the glass temperature. J Non-Cryst Solids 287: 201–209.
[23]  Maslov S, Ispolatov I (2007) Propagation of large concentration changes in reversible protein-binding networks. Proc Natl Acad Sci U S A 104: 13655–13660.
[24]  Granovetter M (1973) The strength of weak ties. Am J Sociol 78: 1360–1380.
[25]  Trpevski D, Tang WKS, Kocarev L (2010) Model for rumor spreading over networks. Phys Rev E 81: 056102.
[26]  Miller JC (2009) Percolation and epidemics in random clustered networks. Phys Rev E 80: 020901.
[27]  Danon L, Arenas A, Díaz-Guilera A (2008) Impact of community structure on information transfer. Phys Rev E 77: 036103.
[28]  Lancichinetti A, Fortunato S (2009) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E 80: 016118.
[29]  Kovács IA, Palotai R, Szalay MS, Csermely P (2010) Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics. PLoS ONE 5: e12528.
[30]  Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: Bringing order to the web. Stanford Digital Library Technologies Project: 1–17.
[31]  Franks DW, Noble J, Kaufmann P, Stagl S (2008) Extremism propagation in social networks with hubs. Adapt Behav 16: 264–274.
[32]  Csermely P (2006) Weak Links: The Universal Key to the Stability of Networks and Complex Systems (The Frontiers Collection). 1st ed. New York: Springer.
[33]  Guimerà R, Nunes Amaral LA (2005) Functional cartography of complex metabolic networks. Nature 433: 895–900.
[34]  Palotai R, Szalay MS, Csermely P (2008) Chaperones as integrators of cellular networks: changes of cellular integrity in stress and diseases. IUBMB Life 60: 10–18.
[35]  Mihalik á, Csermely P (2011) Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation. PLoS Comput Biol 7: e1002187.
[36]  Holstege FC, Jennings EG, Wyrick JJ, Lee TI, Hengartner CJ, et al. (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95: 717–728.
[37]  Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11: 4241–4257.
[38]  Reimand J, Arak T, Vilo J (2011) g:Profiler — a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res 39: W307–W315.
[39]  Mizock BA (1995) Alterations in carbohydrate metabolism during stress: a review of the literature. Am J Med 98: 75–84.
[40]  Lindquist S (1981) Regulation of protein synthesis during heat shock. Nature 293: 311–314.
[41]  Albert I, Thakar J, Li S, Zhang R, Albert R (2008) Boolean network simulations for life scientists. Source Code Biol Med 3: 16.
[42]  May R (1972) Will a large complex systembe stable? Nature 238: 413–414.
[43]  Ghosh A, Vishveshwara S (2007) A study of communication pathways in methionyl- tRNA synthetase by molecular dynamics simulations and structure network analysis. Proc Natl Acad Sci U S A 104: 15711–15716.
[44]  Csermely P, Palotai R, Nussinov R (2010) Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends Biochem Sci 35: 539–546.
[45]  Sethi A, Eargle J, Black AA, Luthey-Schulten Z (2009) Dynamical networks in tRNA:protein complexes. Proc Natl Acad Sci U S A 106: 6620–6625.
[46]  Liu T, Whitten ST, Hilser VJ (2007) Functional residues serve a dominant role in mediating the cooperativity of the protein ensemble. Proc Natl Acad Sci U S A 104: 4347–4352.
[47]  Piazza F, Sanejouand Y-H (2008) Discrete breathers in protein structures. Phys Biol 5: 026001.
[48]  Kitano H (2007) A robustness-based approach to systems-oriented drug design. Nat Rev Drug Discov 6: 202–210.
[49]  Saadatpour A, Wang R-S, Liao A, Liu X, Loughran TP, et al. (2011) Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLoS Comput Biol 7: e1002267.
[50]  Csermely P, ágoston V, Pongor S (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26: 178–182.
[51]  Nussinov R, Tsai C-J, Csermely P (2011) Allo-network drugs: harnessing allostery in cellular networks. Trends Pharmacol Sci 32: 686–693.
[52]  Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R (2013) Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review. Pharmacol Ther 138: 333–408.
[53]  Behar M, Dohlman HG, Elston TC (2007) Kinetic insulation as an effective mechanism for achieving pathway specificity in intracellular signaling networks. Proc Natl Acad Sci U S A 104: 16146–16151.
[54]  Chen C-T (1998) Linear System Theory and Design. 3rd ed. Sedra AS, Lightner MR, New York: Oxford University Press.
[55]  Scott EE, He YA, Wester MR, White MA, Chin CC, et al. (2003) An open conformation of mammalian cytochrome P450 2B4 at 1.6-A resolution. Proc Natl Acad Sci U S A 100: 13196–13201.
[56]  Scott EE, White MA, He YA, Johnson EF, Stout CD, et al. (2004) Structure of mammalian cytochrome P450 2B4 complexed with 4-(4-chlorophenyl)imidazole at 1.9-A resolution: insight into the range of P450 conformations and the coordination of redox partner binding. J Biol Chem 279: 27294–27301.
[57]  R Core Team (2012) R: A language and environment for statistical computing. Available:
[58]  Schr?dinger LLC (2010) The PyMOL molecular graphics system. Available:
[59]  Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems: 1695.


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