Representing and Analyzing Biochemical Networks Using BioMaze

  • Yves Deville
  • Christian Lemer
  • Shoshana Wodak


Systems biology aims at understanding the holistic behavior of biological systems. A very important step toward this goal is to develop a theoretical framework in which we can embed the detailed knowledge that biologists are accumulating at increasing speed, which will then allow us to compute the outcomes of the complex interplay between the myriad interactions that take place in the system. This chapter deals with important basic aspects of this theoretical framework that lie on the divide between systems biology and bioinformatics. In the first part, it discusses the conceptual models used for representing detailed knowledge on various types of biochemical pathways and interactions. As much of this knowledge deals with the complex networks of functional and physical interactions between the different molecular players, the second part of this chapter reviews the conceptual models and methods used to analyze various properties of these networks.

Key Words

Biochemical networks network analysis metabolic pathways signal transduction artificial intelligence BioMaze 


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  1. 1.
    Eisen MB, Brown PO. DNA arrays for analysis of gene expression. Methods Enzymol 1999;303.Google Scholar
  2. 2.
    Harbison CT, Gordon DB, Lee TI, et al. Transcriptional regulatory code of a eukaryotic genome. Nature 2004;431(7004):99–104.PubMedCrossRefGoogle Scholar
  3. 3.
    Wang Y, Liu CL, Storey JD, et al. Precision and functional specificity in mRNA decay. Proc Natl Acad Sci USA 2002 Apr 30;99(9):5860–5865.PubMedCrossRefGoogle Scholar
  4. 4.
    Ge H, Walhout AJ, Vidal M. Integrating “omic” information: A bridge between genomics and systems biology. Trends Genet 2003;19(10):551–560.PubMedCrossRefGoogle Scholar
  5. 5.
    Tong AH, Lesage G, Bader GD, et al. Global mapping of the yeast genetic interaction network. Science 2004 Feb 6;303(5659):808–813.PubMedCrossRefGoogle Scholar
  6. 6.
    van Helden J. Regulatory sequence analysis tools. Nucleic Acids Res 2003; 31(13):3593–3596.PubMedCrossRefGoogle Scholar
  7. 7.
    Tompa M, Li N, Bailey TL, Church GM, et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005 Jan;23(1):137–144.PubMedCrossRefGoogle Scholar
  8. 8.
    Romero P, Wagg J, Green ML, et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 2005; 6(1):R2.PubMedCrossRefGoogle Scholar
  9. 9.
    Karp PD, Riley M, Saier M, et al. The EcoCyc Database. Nucleic Acids Res 2002;30:56–58.PubMedCrossRefGoogle Scholar
  10. 10.
    Croes D, Couche F, Wodak SJ, et al. Inferring meaningful pathways in weighted metabolic networks. J Mol Biol 2005;356(1):222–236.PubMedCrossRefGoogle Scholar
  11. 11.
    Huynen MA, Snel B, von Mering C, et al. Function prediction and protein networks. Curr Opin Cell Biol 2003;2:191–198.CrossRefGoogle Scholar
  12. 12.
    Donaldson I, Martin J, de Bruijn B, et al. PreBIND and Textomy—mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics 2003;27:4(1):11.CrossRefGoogle Scholar
  13. 13.
    Kaufman M, Thomas R. Emergence of complex behaviour from simple circuit structures. C R Biol 2003 Feb;326(2):205–214. Review.PubMedCrossRefGoogle Scholar
  14. 14.
    Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list. Nucleic Acids Res 2006 Jan 1;34 (Database issue):D504–D506.PubMedCrossRefGoogle Scholar
  15. 15.
    van Helden J, Naim A, Mancuso R, et al. Representing and analysing molecular and cellular function using the computer. Biol Chem 2000;381(9–10):921–935.PubMedCrossRefGoogle Scholar
  16. 16.
    Karp PD, Paley S, Romero P. The Pathway Tools software. Bioinformatics 2002;18Suppl 1:S225–S232.PubMedGoogle Scholar
  17. 17.
    Kanehisa M, Goto S, Kawashima S, et al. The KEGG databases at GenomeNet. Nucleic Acids Res 2002;30:42–46.PubMedCrossRefGoogle Scholar
  18. 18.
    Overbeek R, Larsen N, Pusch GD, et al. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res 2002;28:123–125.CrossRefGoogle Scholar
  19. 19.
    Joshi-Tope G, Gillespie M, Vastrik I, et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 2005 Jan 1;33 (Database issue): D428–D432.PubMedCrossRefGoogle Scholar
  20. 20.
    Demir E, Babur O, Dogrusoz U, et al. An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics 2004 Feb 12;20(3): 349–356.PubMedCrossRefGoogle Scholar
  21. 21.
    BioPax http://www.biopax.orgGoogle Scholar
  22. 22.
    Xenarios I, Salwínski L, Duan XJ, et al. The database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 2002;30:303–305.PubMedCrossRefGoogle Scholar
  23. 23.
    Bader GD, Betel D, Hogue CWV. BIND: The Biomolecular Interaction Network Database. Nucleic Acids Res 2003;31:248–250.PubMedCrossRefGoogle Scholar
  24. 24.
    Zanzoni A, Montecchi-Palazzi L, Quondam M, et al. MINT: a Molecular INTeraction database. FEBS Lett 2002;513:135–140.PubMedCrossRefGoogle Scholar
  25. 25.
    Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R. IntAct: an open source molecular interaction database. Nucleic Acids Res 2004a Jan 1;32 (Database issue):D452–D455.PubMedCrossRefGoogle Scholar
  26. 26.
    Hermjakob H, Montecchi-Palazzi L, Bader G, et al. The HUPO PSI Mole cular Interaction Format-a community standard for the representation of protein interaction data. Nature Biotechnol 2004b;22:177–183.CrossRefGoogle Scholar
  27. 27.
    Matys V, Fricke E, Geffers R, et al. TRANSFAC®: transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003;31:374–378.PubMedCrossRefGoogle Scholar
  28. 28.
    Salgado H, Santos-Zavaleta A, Gama-Castro S, et al. RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res 2001;29:72–74.PubMedCrossRefGoogle Scholar
  29. 29.
    Krull M, Voss N, Choi C, Pistor S, et al. TRANSPATH®: an integrated database on signal transduction and a tool for array analysis. Nucleic Acids Res 2003;31:97–100.PubMedCrossRefGoogle Scholar
  30. 30.
    Takai-Igarashi T, Nadaoka Y, Kaminuma T. A database for cell signalling networks. J Comput Biol 1998;5:747–754.PubMedCrossRefGoogle Scholar
  31. 31.
    INOH. http://www.inoh.orgGoogle Scholar
  32. 32.
    Aladjem MI, Pasa S, Parodi S, et al. Molecular interaction maps-a dliagrammatic graphical language for bioregulatory networks. Sci STKE 2004 Feb 24;2004(222):pe8. Review.Google Scholar
  33. 33.
    Rison SC, Hodgman TC, Thornton JM. Comparison of functional annotation schemes for genomes. Funct Integr Genom 2000 May;1(1):56–69.Google Scholar
  34. 34.
    Harris MA, Clark J, Ireland A, et al. Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004 Jan 1;32 (Database issue):D258–D261.PubMedCrossRefGoogle Scholar
  35. 35.
    van Helden J, Naim A, Lemer C, et al. From molecular activities and processes to biological function. Brief Bioinform 2001;2(1):98–93.CrossRefGoogle Scholar
  36. 36.
    Teory TJ, Yang D, Fry JP. A logical design methodology for relational databases using the extended entity-relationship model. Computing Surveys 1986;18(2):197–222.CrossRefGoogle Scholar
  37. 37.
    Huh WK, Falvo JV, Gerke LC, et al. Global analysis of protein localization in budding yeast, Nature 2003 Oct 16;425(6959):686–691.PubMedCrossRefGoogle Scholar
  38. 38. Scholar
  39. 39.
    Deville Y, Gilbert D, van Helden J, Wodak S. An Overview of Data Models for the Analysis of Biochemical Pathways. Brief Bioinform 2003;4(3): 246–259.PubMedCrossRefGoogle Scholar
  40. 40.
    Wagner A, Fell D. The small world inside large metabolic networks. Proc R Soc Lond B Biol Sci 2001;268(1478):1803–1810.CrossRefGoogle Scholar
  41. 41.
    Mah RSH. Application of graph theory to process design and analysis. Comput Chem Eng 1983;7:239–257.CrossRefGoogle Scholar
  42. 42.
    Fell DA, Wagner A. Animating the cellular map. Structural Properties of Metabolic Networks: Implications for Evolution and Modeling of Metabolism. In: Hofmeyr JHS, Rohwer JM, Snaep JL: Model integration An overview of data models for the analysis of biochemical pathways. Stellenbosch University Press, Stellenbosch, 2000:79–85.Google Scholar
  43. 43.
    May GHW. A graph-based pathway searching system over a signal transduction database. Information technologies. University of Glasgow. 2002.Google Scholar
  44. 44.
    Friedler F, Tarjan K, Huang YW, Fan LT. Graph-theoretic approach to process synthesis: Axioms and theorems. Chem Eng Sci 1992;47(8):1973–1988.CrossRefGoogle Scholar
  45. 45.
    Ogata H, Fujibuchi W, Goto S, Kanehisa M. A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res 2000;28(20):4021–4028.PubMedCrossRefGoogle Scholar
  46. 46.
    Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks. Nature 2000;406:651–654.Google Scholar
  47. 47.
    Gunduz C, Yener B, Gultekin SH. The cell graphs of cancer. Bioinformatics 2004;20Suppl.1:i145–i151.PubMedCrossRefGoogle Scholar
  48. 48.
    Brandes U, Erlebach T. Network Analysis: Methodological Foundations. Lecture Notes in Computer Science, No. 3418, Springer 2005.Google Scholar
  49. 49.
    Batagelj V, Mrvar A. Pajek. Analysis and Visualization of Large Networks. Jünger, M., Mutzel, P, (Eds.) Graph Drawing Software. Springer, Berlin 2003;77-103.Google Scholar
  50. 50.
    Pazos F, Valencia A, De Lorenzo V. The organization of the microbial biodegradation network from a systems-biology perspective. EMBO Rep 2003;(10):994–999.CrossRefGoogle Scholar
  51. 51.
    Eppstein D. Finding the k shortest paths. SIAM J Comp 1998;28(2): 652–673.CrossRefGoogle Scholar
  52. 52.
    Jiménez VM, Marzal A. Computing the K Shortest Paths: A New Algorithm and an Experimental Comparison. Algorithm Engineering: 3rd International Workshop, WAE’99, London, UK, July 1999; LNCS 1668, 1999, Springer Verlag.Google Scholar
  53. 53.
    van Dongen S. A cluster algorithm for graphs. Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000. Scholar
  54. 54.
    Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003;4:2. Epub 2003 Jan 13.PubMedCrossRefGoogle Scholar
  55. 55.
    King AD, Przulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics 2004 Nov 22;20(17):3013–3020. Epub 2004 Jun 4.PubMedCrossRefGoogle Scholar
  56. 56.
    Krogan NJ, Cagney G, Yu H, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006;440(7084):637–643.PubMedCrossRefGoogle Scholar
  57. 57.
    Gagneur J, Jackson DB, Casari G. Hierarchical analysis of dependency in metabolic networks. Bioinformatics 2003 May 22;19(8):1027–1034.PubMedCrossRefGoogle Scholar
  58. 58.
    Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: Simple building blocks of complex networks. Science 2002;298:824–827.PubMedCrossRefGoogle Scholar
  59. 59.
    Shen-Orr S, Milo R, Mangan S, Alon U. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet 2002;31:64–68.PubMedCrossRefGoogle Scholar
  60. 60.
    Kashtan N, Itzkovitz S, Milo R, Alon U. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 2004;20 no. 11:1746–1758.PubMedCrossRefGoogle Scholar
  61. 61.
    Wernicke S, Rasche F. FANMOD: a tool for fast network motif detection. Bioinformatics 2006 May 1;22(9):1152–1153.PubMedCrossRefGoogle Scholar
  62. 62.
    Schreiber F, Schwöbbermeyer H. MAVisto: a tool for the exploration of network motifs. Bioinformatics 2005;21:3572–3574.PubMedCrossRefGoogle Scholar
  63. 63.
    Van Hentenryck P. The OPL Optimization Programming Language. Cambridge: The MIT Press; 1999.Google Scholar
  64. 64.
    K. Apt. Principles of Constraint Programming. Cambridge University Press; 2003.Google Scholar
  65. 65.
    Backofen R, Gilbert D. Bioinformatics and constraints. Constraints 2001: 6(2/3).Google Scholar
  66. 66.
    Dooms G, Deville Y, Dupont P. CP(Graph): Introducing a Graph Computation Domain in Constraint Programming. International Conference on Principles and Practice on Constraint Programming, Sitges, Barcelona, Spain, October 2005.Google Scholar
  67. 67.
    Pinter RY, Rokhlenko O, Yeger-Lotem E, Ziv-Ukelson M. Alignment of Metabolic Pathways. Bioinformatics 2005;21,16:3401–3408.PubMedCrossRefGoogle Scholar
  68. 68.
    Koyuturk M, Kim Y, Topkara U, et al. Pairwise alignment of protein interaction networks. J Comp Biol 2006;13(2):182–199.CrossRefGoogle Scholar
  69. 69.
    Kelley BP, Yuan B, Lewitter F, et al. PathBLAST: a tool for aligment of protein interaction networks. Nuc Acids Res 2004;32:W83–W88.CrossRefGoogle Scholar
  70. 70.
    Zampelli S, Deville Y, Dupont P. Approximate Constrained Subgraph Matching. International Conference on Principles and Practice on Constraint Programming, Sitges, Barcelona, Spain, October 2005.Google Scholar
  71. 71.
    Faloutsos C, McCurley KM, Tomkins A. Fast discovery of connection subgraphs. 10th ACM Conference on Knowledge Discovery and Data Mining (KDD). 2004;2:118–127.CrossRefGoogle Scholar
  72. 72.
    Vast S, Dupont P, Deville Y. Automatic extraction of relevant nodes in biochemical networks. Learning and Bioinformatic Workshop, CAp 2005; Conférence d’Apprentissage, Nice: 21–31.Google Scholar
  73. 73.
    Eclipse: Scholar
  74. 74.
    Gecode: Generic constraint development environment, 2005. http://www. Scholar
  75. 75.
    Mendes P. GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci. 1993;5:563–571.Google Scholar
  76. 76.
    Ander M, Beltrao P, Di Ventura B, et al. SmartCell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks. Syst Biol 2004;1:129–138.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Yves Deville
    • 1
  • Christian Lemer
    • 2
  • Shoshana Wodak
    • 3
  1. 1.Computing Science and Engineering DepartmentUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Unité de Conformation des Macromolécules BiologiquesUniversité Libre de BruxellesBruxellesBelgium
  3. 3.Department of Biochemistry and Structural Biology, Department of Medical GeneticsUniversity of TorontoTorontoCanada

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