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Methods for Structural Inference and Functional Module Identification in Intracellular Networks

  • Maria Manioudaki
  • Eleftheria Tzamali
  • Martin Reczko
  • Panayiota Poirazi

Abstract

The ways in which intracellular components interact in order to produce certain functions remains a mystery to the scientific community. Although parts of biological systems become more and more characterized, a more global understanding of the structure, dynamics and functionalities of complex intracellular networks is currently lacking. Systems Biology approaches aim at providing such a global picture by combining analytical and experimental techniques across several multi-disciplinary fields. In this chapter, we provide an overview of the analytical approaches and computational tools that have been applied to biological systems in order to describe them at different levels of abstraction. We start by reviewing methods that model or infer a topological map of complex biological networks (structural inference) and move on to discuss ways of discovering the functionalities of sub-network entities that comprise these networks (functional module inference). Although clearly not exclusive, this chapter aims at providing a representative overview of the currently available methods that have been successfully used to characterize complex biological networks and reveal their structure and function.

Keywords

Modelling Mathematical methods Cellular networks Modules Motifs Inference 

Suggested Reading

  1. 1.
    Hartwell, L.H., et al., From molecular to modular cell biology. Nature, 1999. 402(6761 Suppl): p. C47–52.CrossRefPubMedGoogle Scholar
  2. 2.
    Kauffman, S.A., Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor. Biol., 1969. 22(3): p. 437–467.CrossRefPubMedGoogle Scholar
  3. 3.
    Drossel, V.K.a.B., Relevant components in critical random Boolean networks. New J. Phys., 2006. 8(228).CrossRefGoogle Scholar
  4. 4.
    Derrida, B.a.P., Y., Random networks of automata: A simple annealed approximation. Europhys. Lett., 1(2):45–49., 1986. 1(2): p. 45–49.CrossRefGoogle Scholar
  5. 5.
    Gershenson, C., Introduction to Random Boolean Networks. Workshop and Tutorial Proceedings, Ninth International Conference on the Simulation and Synthesis of Living Systems 2004: p. 160–173.Google Scholar
  6. 6.
    Greil, F. and B. Drossel, Dynamics of critical Kauffman networks under asynchronous stochastic update. Phys Rev Lett, 2005. 95(4): p. 048701.CrossRefPubMedGoogle Scholar
  7. 7.
    Klemm, K. and S. Bornholdt, Stable and unstable attractors in Boolean networks. Phys Rev E Stat Nonlin Soft Matter Phys, 2005. 72(5 Pt 2): p. 055101.CrossRefPubMedGoogle Scholar
  8. 8.
    Raeymaekers, L., Dynamics of Boolean networks controlled by biologically meaningful functions. J Theor Biol, 2002. 218(3): p. 331–341.CrossRefPubMedGoogle Scholar
  9. 9.
    Shmulevich, I. and S.A. Kauffman, Activities and sensitivities in boolean network models. Phys Rev Lett, 2004. 93(4): p. 048701.CrossRefPubMedGoogle Scholar
  10. 10.
    Aldana, M., et al., Robustness and evolvability in genetic regulatory networks. J Theor Biol, 2007. 245(3): p. 433–448.CrossRefPubMedGoogle Scholar
  11. 11.
    Wuensche, A., Genomic regulation modeled as a network with basins of attraction. Pac Symp Biocomput, 1998: p. 89–102.Google Scholar
  12. 12.
    Datta, A., et al., External control in Markovian genetic regulatory networks: the imperfect information case. Bioinformatics, 2004. 20(6): p. 924–930.CrossRefPubMedGoogle Scholar
  13. 13.
    Szallasi, Z. and S. Liang, Modeling the normal and neoplastic cell cycle with “realistic Boolean genetic networks”: their application for understanding carcinogenesis and assessing therapeutic strategies. Pac Symp Biocomput, 1998: p. 66–76.Google Scholar
  14. 14.
    Shmulevich, I., et al., Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 2002. 18(2): p. 261–274.CrossRefPubMedGoogle Scholar
  15. 15.
    Faure, A., et al., Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics, 2006. 22(14): p. e124–131.CrossRefPubMedGoogle Scholar
  16. 16.
    Herrgard, M.J., et al., Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res, 2006. 16(5): p. 627–635.CrossRefPubMedGoogle Scholar
  17. 17.
    Akutsu, T., S. Miyano, and S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac Symp Biocomput, 1999: p. 17–28.Google Scholar
  18. 18.
    Boros, E. and T. Ibaraki, Error-free and Best-fit extensions of partially defined boolean functions. Information and Computation 1998. 140(2): p. 254–283.CrossRefGoogle Scholar
  19. 19.
    Nam, D.S., Seunghyun; Kim, Sangsoo, An efficient top-down search algorithm for learning Boolean networks of gene expression. Machine Learning, 2006. 65(1): p. 229–245.CrossRefGoogle Scholar
  20. 20.
    Liang, S., S. Fuhrman, and R. Somogyi, Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput, 1998: p. 18–29.Google Scholar
  21. 21.
    Kim, H., J.K. Lee, and T. Park, Boolean networks using the chi-square test for inferring large-scale gene regulatory networks. BMC Bioinformatics, 2007. 8: p. 37.CrossRefPubMedGoogle Scholar
  22. 22.
    Lähdesmäki, H., On Learning Gene Regulatory Networks under the Boolean Network Model. Machine Learning, 2002. 52: p. 147–163.CrossRefGoogle Scholar
  23. 23.
    Akutsu, T., et al., A System for Identifying Genetic Networks from Gene Expression Patterns Produced by Gene Disruptions and Overexpressions. Genome Inform Ser Workshop Genome Inform, 1998. 9: p. 151–160.PubMedGoogle Scholar
  24. 24.
    Osamu Hirose, N.N., Yoshinori Tamada, Hideo Bannai, Seiya Imoto and Satoru Miyano, Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks, in Computational Science and Its Applications – ICCSA 2005. 2005, Springer Berlin / Heidelberg. p. 349–356.Google Scholar
  25. 25.
    Martin, S., et al., Boolean Dynamics of Genetic Regulatory Networks Inferred from Microarray Time Series Data. Bioinformatics, 2007.Google Scholar
  26. 26.
    Pal, R., et al., Intervention in context-sensitive probabilistic Boolean networks. Bioinformatics, 2005. 21(7): p. 1211–1218.CrossRefPubMedGoogle Scholar
  27. 27.
    Thomas, R., D. Thieffry, and M. Kaufman, Dynamical behaviour of biological regulatory networks—I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull Math Biol, 1995. 57(2): p. 247–276.PubMedGoogle Scholar
  28. 28.
    Thomas, R. and M. Kaufman, Multistationarity, the basis of cell differentiation and memory. I. Structural conditions of multistationarity and other nontrivial behavior. Chaos, 2001. 11(1): p. 170–179.CrossRefPubMedGoogle Scholar
  29. 29.
    Thomas, R. and M. Kaufman, Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. Chaos, 2001. 11(1): p. 180–195.CrossRefPubMedGoogle Scholar
  30. 30.
    Thomas, R., Boolean formalization of genetic control circuits. J Theor Biol, 1973. 42(3): p. 563–585.CrossRefPubMedGoogle Scholar
  31. 31.
    Mendoza, L., D. Thieffry, and E.R. Alvarez-Buylla, Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics, 1999. 15(7–8): p. 593–606.CrossRefPubMedGoogle Scholar
  32. 32.
    Thieffry, D. and L. Sanchez, Dynamical modelling of pattern formation during embryonic development. Curr Opin Genet Dev, 2003. 13(4): p. 326–330.CrossRefPubMedGoogle Scholar
  33. 33.
    Friedman, N., et al., Using Bayesian networks to analyze expression data. J Comput Biol, 2000. 7(3–4): p. 601–620.CrossRefPubMedGoogle Scholar
  34. 34.
    Hartemink, A.J., et al., Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac Symp Biocomput, 2001: p. 422–433.Google Scholar
  35. 35.
    Jansen, R., et al., A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 2003. 302(5644): p. 449–453.CrossRefPubMedGoogle Scholar
  36. 36.
    Le Phillip, P., A. Bahl, and L.H. Ungar, Using prior knowledge to improve genetic network reconstruction from microarray data. In Silico Biol, 2004. 4(3): p. 335–353.PubMedGoogle Scholar
  37. 37.
    Murphy K, M.S., Modelling gene expression data using dynamic Bayesian networks. 1999, Computer Science Division, University of California, Berkeley.Google Scholar
  38. 38.
    Beal, M.J., et al., A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 2005. 21(3): p. 349–356.CrossRefPubMedGoogle Scholar
  39. 39.
    Bernard, A. and A.J. Hartemink, Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Pac Symp Biocomput, 2005: p. 459–470.Google Scholar
  40. 40.
    Dojer, N., et al., Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics, 2006. 7: p. 249.CrossRefPubMedGoogle Scholar
  41. 41.
    Zou, M. and S.D. Conzen, A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 2005. 21(1): p. 71–79.CrossRefPubMedGoogle Scholar
  42. 42.
    Kato, T., K. Tsuda, and K. Asai, Selective integration of multiple biological data for supervised network inference. Bioinformatics, 2005. 21(10): p. 2488–2495.CrossRefPubMedGoogle Scholar
  43. 43.
    Yamanishi, Y., J.P. Vert, and M. Kanehisa, Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics, 2005. 21 Suppl 1: p. 2468i477.Google Scholar
  44. 44.
    D'Haeseleer, P., S. Liang, and R. Somogyi, Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 2000. 16(8): p. 707–726.CrossRefPubMedGoogle Scholar
  45. 45.
    Segal, E., et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 2003. 34(2): p. 166–176.CrossRefPubMedGoogle Scholar
  46. 46.
    Bar-Joseph, Z., et al., Computational discovery of gene modules and regulatory networks. Nat Biotechnol, 2003. 21(11): p. 1337–1342.CrossRefPubMedGoogle Scholar
  47. 47.
    Janes, K.A. and M.B. Yaffe, Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol, 2006. 7(11): p. 820–828.CrossRefPubMedGoogle Scholar
  48. 48.
    D'Haeseleer, P., How does gene expression clustering work Google Scholar
  49. 49.
    Yeung, K.Y., M. Medvedovic, and R.E. Bumgarner, Clustering gene-expression data with repeated measurements. Genome Biol, 2003. 4(5): p. R34.CrossRefPubMedGoogle Scholar
  50. 50.
    Walker, M.G., et al., Prediction of gene function by genome-scale expression analysis: prostate cancer-associated genes. Genome Res, 1999. 9(12): p. 1198–1203.CrossRefPubMedGoogle Scholar
  51. 51.
    Thompson, H.G., et al., Identification and confirmation of a module of coexpressed genes. Genome Res, 2002. 12(10): p. 1517–1522.CrossRefPubMedGoogle Scholar
  52. 52.
    Eisen, M.B., et al., Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A, 1998. 95(25): p. 14863–14868.CrossRefPubMedGoogle Scholar
  53. 53.
    Tavazoie, S., et al., Systematic determination of genetic network architecture. Nat Genet, 1999. 22(3): p. 281–285.CrossRefPubMedGoogle Scholar
  54. 54.
    Claverie, J.M., Computational methods for the identification of differential and coordinated gene expression. Hum Mol Genet, 1999. 8(10): p. 1821–1832.CrossRefPubMedGoogle Scholar
  55. 55.
    Michaels, G.S., et al., Cluster analysis and data visualization of large-scale gene expression data. Pac Symp Biocomput, 1998: p. 42–53.Google Scholar
  56. 56.
    Ben-Dor, A., R. Shamir, and Z. Yakhini, Clustering gene expression patterns. J Comput Biol, 1999. 6(3–4): p. 281–297.CrossRefPubMedGoogle Scholar
  57. 57.
    Alon, U., et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A, 1999. 96(12): p. 6745–6750.CrossRefPubMedGoogle Scholar
  58. 58.
    Kim, K., et al., Measuring similarities between gene expression profiles through new data transformations. BMC Bioinformatics, 2007. 8: p. 29.CrossRefPubMedGoogle Scholar
  59. 59.
    Kuncheva, L.I. and D.P. Vetrov, Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans Pattern Anal Mach Intell, 2006. 28(11): p. 1798–1808.CrossRefPubMedGoogle Scholar
  60. 60.
    Lu, Y., et al., Incremental genetic k-means algorithm and its application in gene expression data analysis. BMC Bioinformatics, 2004. 5: p. 172.CrossRefPubMedGoogle Scholar
  61. 61.
    Kohonen, T., Self Organizing Maps. 1995, Berlin: Springer.Google Scholar
  62. 62.
    Wang, J., et al., Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics, 2002. 3: p. 36.CrossRefPubMedGoogle Scholar
  63. 63.
    Toronen, P., et al., Analysis of gene expression data using self-organizing maps. FEBS Lett, 1999. 451(2): p. 142–146.CrossRefPubMedGoogle Scholar
  64. 64.
    Tamayo, P., et al., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A, 1999. 96(6): p. 2907–2912.CrossRefPubMedGoogle Scholar
  65. 65.
    Misra, J., et al., Interactive exploration of microarray gene expression patterns in a reduced dimensional space. Genome Res, 2002. 12(7): p. 1112–1120.CrossRefPubMedGoogle Scholar
  66. 66.
    Alter, O., P.O. Brown, and D. Botstein, Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA, 2000. 97(18): p. 10101–10106.CrossRefPubMedGoogle Scholar
  67. 67.
    Boulesteix, A.L. and K. Strimmer, Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach. Theor Biol Med Model, 2005. 2: p. 23.CrossRefPubMedGoogle Scholar
  68. 68.
    Datta, S., Exploring relationships in gene expressions: a partial least squares approach. Gene Expr, 2001. 9(6): p. 249–255.PubMedGoogle Scholar
  69. 69.
    Clementi, M., et al., Robust multivariate statistics and the prediction of protein secondary structure content. Protein Eng, 1997. 10(7): p. 747–749.CrossRefPubMedGoogle Scholar
  70. 70.
    Zhang, Z. and M. Gerstein, Reconstructing genetic networks in yeast. Nat Biotechnol, 2003. 21(11): p. 1295–1297.CrossRefPubMedGoogle Scholar
  71. 71.
    Cassman, M., Barriers to progress in Systems Biology. Nature, 2005. 438(7071): p. 1079.CrossRefPubMedGoogle Scholar
  72. 72.
    Lemmens, K., et al., Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biol, 2006. 7(5): p. R37.CrossRefPubMedGoogle Scholar
  73. 73.
    Tanay, A., et al., Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci U S A, 2004. 101(9): p. 2981–2986.CrossRefPubMedGoogle Scholar
  74. 74.
    Jeong, H., et al., Lethality and centrality in protein networks. Nature, 2001. 411(6833): p. 41–42.CrossRefPubMedGoogle Scholar
  75. 75.
    Tong, A.H., et al., A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science, 2002. 295(5553): p. 321–324.CrossRefPubMedGoogle Scholar
  76. 76.
    Han, J.D., et al., Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature, 2004. 430(6995): p. 88–93.CrossRefPubMedGoogle Scholar
  77. 77.
    Jeong, H., et al., The large-scale organization of metabolic networks. Nature, 2000. 407(6804): p. 651–654.CrossRefPubMedGoogle Scholar
  78. 78.
    Guimera, R. and L.A. Nunes Amaral, Functional cartography of complex metabolic networks. Nature, 2005. 433(7028): p. 895–900.CrossRefPubMedGoogle Scholar
  79. 79.
    Stuart, J.M., et al., A gene-coexpression network for global discovery of conserved genetic modules. Science, 2003. 302(5643): p. 249–255.CrossRefPubMedGoogle Scholar
  80. 80.
    Tong, A.H., et al., Global mapping of the yeast genetic interaction network. Science, 2004. 303(5659): p. 808–813.CrossRefPubMedGoogle Scholar
  81. 81.
    Lee, T.I., et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799–804.CrossRefPubMedGoogle Scholar
  82. 82.
    Ravasz, E., et al., Hierarchical organization of modularity in metabolic networks. Science, 2002. 297(5586): p. 15511555.CrossRefGoogle Scholar
  83. 83.
    Schilling, C.H., D. Letscher, and B.O. Palsson, Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol, 2000. 203(3): p. 229–248.CrossRefPubMedGoogle Scholar
  84. 84.
    Schuster, S., D.A. Fell, and T. Dandekar, A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol, 2000. 18(3): p. 326–332.CrossRefPubMedGoogle Scholar
  85. 85.
    Burgard, A.P., et al., Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res, 2004. 14(2): p. 301–312.CrossRefPubMedGoogle Scholar
  86. 86.
    Kuepfer, L., U. Sauer, and L.M. Blank, Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res, 2005. 15(10): p. 1421–1430.CrossRefPubMedGoogle Scholar
  87. 87.
    Carter, S.L., et al., Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics, 2004. 20(14): p. 2242–2250.CrossRefPubMedGoogle Scholar
  88. 88.
    Ma, H.W., J. Buer, and A.P. Zeng, Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach. BMC Bioinformatics, 2004. 5: p. 199.CrossRefPubMedGoogle Scholar
  89. 89.
    Pereira-Leal, J.B., E.D. Levy, and S.A. Teichmann, The origins and evolution of functional modules: lessons from protein complexes. Philos Trans R Soc Lond B Biol Sci, 2006. 361(1467): p. 507–517.CrossRefPubMedGoogle Scholar
  90. 90.
    Cho, Y.-R., W. Hwang, and A. Zhang, Efficient Modularization of Weighted Protein Interaction Networks using k-Hop Graph Reduction, in Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06). 2006.Google Scholar
  91. 91.
    Pereira-Leal, J.B., A.J. Enright, and C.A. Ouzounis, Detection of functional modules from protein interaction networks. Proteins, 2004. 54(1): p. 49–57.CrossRefPubMedGoogle Scholar
  92. 92.
    Rives, A.W. and T. Galitski, Modular organization of cellular networks. Proc Natl Acad Sci U S A, 2003. 100(3): p. 1128–1133.CrossRefPubMedGoogle Scholar
  93. 93.
    Arnau, V., S. Mars, and I. Marin, Iterative cluster analysis of protein interaction data. Bioinformatics, 2005. 21(3): p. 364–378.CrossRefPubMedGoogle Scholar
  94. 94.
    Milo, R., et al., Network motifs: simple building blocks of complex networks. Science, 2002. 298(5594): p. 824–827.CrossRefPubMedGoogle Scholar
  95. 95.
    Shen-Orr, S.S., et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet, 2002. 31(1): p. 64–68.CrossRefPubMedGoogle Scholar
  96. 96.
    Ma, H.W., et al., An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucleic Acids Res, 2004. 32(22): p. 6643–6649.CrossRefPubMedGoogle Scholar
  97. 97.
    Zhang, L.V., et al., Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J Biol, 2005. 4(2): p. 6.CrossRefPubMedGoogle Scholar
  98. 98.
    Wuchty, S., Z.N. Oltvai, and A.L. Barabasi, Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet, 2003. 35(2): p. 176–179.CrossRefPubMedGoogle Scholar
  99. 99.
    Conant, G.C. and A. Wagner, Convergent evolution of gene circuits. Nat Genet, 2003. 34(3): p. 264–266.CrossRefPubMedGoogle Scholar
  100. 100.
    Snel, B. and M.A. Huynen, Quantifying modularity in the evolution of biomolecular systems. Genome Res, 2004. 14(3): p. 391–397.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Maria Manioudaki
    • 1
  • Eleftheria Tzamali
    • 1
  • Martin Reczko
    • 2
  • Panayiota Poirazi
    • 3
  1. 1.University of CreteHeraklionGreece
  2. 2.Foundation for Research and Technology-HellasHeraklionGreece
  3. 3.Computational Biology LaboratoryInstitute of Molecular Biology and Biotechnology (IMBB), Foundation of Research and Technology-Hellas (FORTH) Vassilika VoutonHeraklionGreece

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