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A Relevance Index Method to Infer Global Properties of Biological Networks

  • Marco Villani
  • Laura Sani
  • Michele Amoretti
  • Emilio Vicari
  • Riccardo Pecori
  • Monica Mordonini
  • Stefano Cagnoni
  • Roberto Serra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)

Abstract

Many complex systems, both natural and artificial, may be represented by networks of interacting nodes. Nevertheless, it is often difficult to find meaningful correspondences between the dynamics expressed by these systems and the topological description of their networks. In contrast, many of these systems may be well described in terms of coordinated behavior of their dynamically relevant parts. In this paper we use the recently proposed Relevance Index approach, based on information-theoretic measures. Starting from the observation of the dynamical states of any system, the Relevance Index is able to provide information about its organization. Moreover, we show how the application of the proposed approach leads to novel and effective interpretations in the T helper network case study.

Keywords

Complex systems Biological networks Dynamical behavior Relevance index T helper cells 

Notes

Acknowledgments

The work of Michele Amoretti was supported by the University of Parma Research Fund - FIL 2016 - Project “NEXTALGO: Efficient Algorithms for Next-Generation Distributed Systems”.

This work greatly benefited from discussions with Andrea Roli, to whom the authors are warmly thankful.

References

  1. 1.
    Albert, R., Jeong, H., Barabási, A.L.: Internet: diameter of the world-wide web. Nature 401(6749), 130–131 (1999)CrossRefGoogle Scholar
  2. 2.
    Cario, M.C., Nelson, B.L.: Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix. Technical report (1997)Google Scholar
  3. 3.
    Cimorelli, F., Priscoli, F.D., Pietrabissa, A., Celsi, L.R., Suraci, V., Zuccaro, L.: A distributed load balancing algorithm for the control plane in software defined networking. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 1033–1040, June 2016Google Scholar
  4. 4.
    De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)CrossRefGoogle Scholar
  5. 5.
    Delli Priscoli, F., Di Giorgio, A., Lisi, F., Monaco, S., Pietrabissa, A., Celsi, L.R., Suraci, V.: Multi-agent quality of experience control. Int. J. Control Autom. Syst. 15, 892–904 (2017)CrossRefGoogle Scholar
  6. 6.
    Ebel, H., Mielsch, L.I., Bornholdt, S.: Scale-free topology of e-mail networks. Phys. Rev. E 66, 035103 (2002). http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cond-mat/0201476CrossRefGoogle Scholar
  7. 7.
    Emmeche, C., Køppe, S., Stjernfelt, F.: Explaining emergence: towards an ontology of levels. J. Gen. Philos. Sci. 28(1), 83–117 (1997)CrossRefGoogle Scholar
  8. 8.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the Internet topology. SIGCOMM Comput. Commun. Rev. 29(4), 251–262 (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Feldt, S., Waddell, J., Hetrick, V., Berke, J., Żochowski, M.: Functional clustering algorithm for the analysis of dynamic network data. Phys. Rev. E 79(5), 056104 (2009)CrossRefGoogle Scholar
  10. 10.
    Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Poli, I., Serra, R.: On some properties of information theoretical measures for the study of complex systems. In: Pizzuti, C., Spezzano, G. (eds.) WIVACE 2014. CCIS, vol. 445, pp. 140–150. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12745-3_12Google Scholar
  11. 11.
    Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Serra, R.: Exploring the organisation of complex systems through the dynamical interactions among their relevant subsets. In: Proceedings of the European Conference on Artificial Life, pp. 286–293 (2015)Google Scholar
  12. 12.
    Haken, H.: An introduction. Synergetics, pp. 1–387. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-662-10184-1_1CrossRefGoogle Scholar
  13. 13.
    Herrgård, M.J., Covert, M.W., Palsson, B.Ø.: Reconstruction of microbial transcriptional regulatory networks. Curr. Opin. Biotechnol. 15(1), 70–77 (2004)CrossRefGoogle Scholar
  14. 14.
    Huang, Y., Wange, R.L.: T cell receptor signaling: beyond complex complexes. J. Biol. Chem. 279(28), 28827–28830 (2004)CrossRefGoogle Scholar
  15. 15.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)CrossRefGoogle Scholar
  16. 16.
    Johnson, J.: Hypernetworks in the Science of Complex Systems, vol. 3. World Scientific, Singapore (2013)zbMATHGoogle Scholar
  17. 17.
    Johnston, H.: Cliques of a graph-variations on the Bron-Kerbosch algorithm. Int. J. Parallel Prog. 5(3), 209–238 (1976)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Lane, D., Pumain, D., van der Leeuw, S.E., West, G.: Complexity Perspectives in Innovation and Social Change, vol. 7. Springer Science and Business Media, Berlin (2009).  https://doi.org/10.1007/978-1-4020-9663-1CrossRefGoogle Scholar
  19. 19.
    Mendoza, L., Xenarios, I.: A method for the generation of standardized qualitative dynamical systems of regulatory networks. Theor. Biol. Med. Model. 3(1), 13 (2006)CrossRefGoogle Scholar
  20. 20.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  21. 21.
    Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)CrossRefGoogle Scholar
  22. 22.
    Remy, E., Ruet, P., Mendoza, L., Thieffry, D., Chaouiya, C.: From logical regulatory graphs to standard petri nets: dynamical roles and functionality of feedback circuits. In: Priami, C., Ingólfsdóttir, A., Mishra, B., Riis Nielson, H. (eds.) Transactions on Computational Systems Biology VII. LNCS, vol. 4230, pp. 56–72. Springer, Heidelberg (2006).  https://doi.org/10.1007/11905455_3CrossRefGoogle Scholar
  23. 23.
    Roberto Serra, R., Villani, M.: Modelling Protocells. Springer Science and Business Media, Dordrecht (2017).  https://doi.org/10.1007/978-94-024-1160-7CrossRefzbMATHGoogle Scholar
  24. 24.
    Roli, A., Villani, M., Caprari, R., Serra, R.: Identifying critical states through the relevance index. Entropy 19(2), 73 (2017)CrossRefGoogle Scholar
  25. 25.
    Ruppin, E., Papin, J.A., De Figueiredo, L.F., Schuster, S.: Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. Curr. Opin. Biotechnol. 21(4), 502–510 (2010)CrossRefGoogle Scholar
  26. 26.
    Sani, L., Amoretti, M., Vicari, E., Mordonini, M., Pecori, R., Roli, A., Villani, M., Cagnoni, S., Serra, R.: Efficient search of relevant structures in complex systems. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 35–48. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49130-1_4CrossRefGoogle Scholar
  27. 27.
    Thomas, R., Kaufman, M.: Multistationarity, the basis of cell differentiation and memory. l. structural conditions of multistationarity and other nontrivial behavior. Chaos Interdisc. J. Nonlinear Sci. 11(1), 170–179 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Thomas, R., Thieffry, D., Kaufman, M.: Dynamical behaviour of biological regulatory networks-l. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull. Math. Biol. 57(2), 247–276 (1995)CrossRefzbMATHGoogle Scholar
  29. 29.
    Tononi, G., McIntosh, A.R., Russell, D.P., Edelman, G.M.: Functional clustering: identifying strongly interactive brain regions in neuroimaging data. Neuroimage 7(2), 133–149 (1998)CrossRefGoogle Scholar
  30. 30.
    Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Nat. Acad. Sci. 91(11), 5033–5037 (1994)CrossRefGoogle Scholar
  31. 31.
    Vicari, E., Amoretti, M., Sani, L., Mordonini, M., Pecori, R., Roli, A., Villani, M., Cagnoni, S., Serra, R.: GPU-based parallel search of relevant variable sets in complex systems. In: Rossi, F., Piotto, S., Concilio, S. (eds.) WIVACE 2016. CCIS, vol. 708, pp. 14–25. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57711-1_2CrossRefGoogle Scholar
  32. 32.
    Villani, M., Filisetti, A., Benedettini, S., Roli, A., Lane, D., Serra, R.: The detection of intermediate-level emergent structures and patterns. In: Proceedings of the European Conference on Artificial Life, pp. 372–378 (2013)Google Scholar
  33. 33.
    Villani, M., Roli, A., Filisetti, A., Fiorucci, M., Poli, I., Serra, R.: The search for candidate relevant subsets of variables in complex systems. Artif. Life 21, 412–431 (2015)CrossRefGoogle Scholar
  34. 34.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world" networks. Nature 393(6684), 440–442 (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marco Villani
    • 2
  • Laura Sani
    • 1
  • Michele Amoretti
    • 1
  • Emilio Vicari
    • 4
  • Riccardo Pecori
    • 1
    • 3
  • Monica Mordonini
    • 1
  • Stefano Cagnoni
    • 1
  • Roberto Serra
    • 2
  1. 1.Dip. di Ingegneria e ArchitetturaUniversità di ParmaParmaItaly
  2. 2.Dip. Scienze Fisiche, Informatiche e MatematicheUniversità di Modena e Reggio EmiliaModenaItaly
  3. 3.SMARTEST Research CentreUniversità eCAMPUSNovedrateItaly
  4. 4.Camlin ItalyParmaItaly

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