Biochemical Pathway Analysis via Signature Mining

  • Eleftherios Panteris
  • Stephen Swift
  • Annette Payne
  • Xiaohui Lui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3695)


Biology has been revolutionised by microarrays and bioinformatics is now a powerful tool in the hands of biologists. Gene expression analysis is at the centre of attention over the last few years mostly in the form of algorithms, exploring cluster relationships and dynamic interactions between gene variables, and programs that try to display the multidimensional microarray data in appropriate formats so that they make biological sense. In this paper we propose a simple yet effective approach to biochemical pathway analysis based on biological knowledge. This approach, based on the concept of signature and heuristic search methods such as hill climbing and simulated annealing, is developed to select a subset of genes for each pathway that fully describes the behaviour of the pathway at a given experimental condition in a bid to reduce the dimensionality of microarray data and make the analysis more biologically relevant.


Simulated Annealing Microarray Data Pathway Analysis Signature Mining Biochemical Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aggawal, K., Lee, K.H.: Functional genomics and proteomics as a foundation for systems biology. Briefings in functional genomics and proteomics 2(3), 175–184 (2003)CrossRefGoogle Scholar
  2. 2.
    Claverie, J.: Computational methods for the identification of differential and coordinated gene expression. Human Molecular Genetics 8(10), 1821–1832 (1999)CrossRefGoogle Scholar
  3. 3.
    Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., Conklin, B.R.: GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nature Genetics 31(1), 19–20 (2002)CrossRefGoogle Scholar
  4. 4.
    Duggan, D.J., Bittner, M., Chen, Y., Meltzer, P., Trent, J.: Expression profiling using cDNA microarrays. Nature Genetics 21, 10–14 (1999)CrossRefGoogle Scholar
  5. 5.
    Eisen, M., Spellman, P.T., Botstein, D., Brown, P.O.: Clustering Analysis and display of genome wide expression patterns. PNAS 95, 14863–14868 (1998)CrossRefGoogle Scholar
  6. 6.
    Goesmann, A., Haubrock, M., Meyer, F., Kalinowski, J., Giegerich, R.: PathFinder: reconstruction and dynamic visualization of metabolic pathways. Bioinformatics 18, 124–129 (2002)CrossRefGoogle Scholar
  7. 7.
    Huang, S.: Back to the biology in systems biology: What can we learn from biomolecular networks? Briefings in functional genomics and proteomics. Vol 2(4), 279–297 (2004)Google Scholar
  8. 8.
    Kanehisa, M., Goto, S.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 28, 27–30 (2000)CrossRefGoogle Scholar
  9. 9.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Khodursky, A.B., Peter, B.J., Cozzarelli, N.R., Botstein, D., Brown, P.O., Yanofsky, C.: DNA microarray analysis of gene expression in response to physiological and genetic changes that affect tryptophan metabolism in Escherichia coli. PNAS 97(22), 12170–12175 (2000)CrossRefGoogle Scholar
  11. 11.
    Kolpakov, F.A., Ananko, E.A., Kolesov, G.B., Kolchanov, N.A.: GeneNet: a gene network database and its automated visualization. Bioinformatics 14, 529–537 (1998)CrossRefGoogle Scholar
  12. 12.
    Ma, H., Zeng, A.: Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19, 270–277 (2003)CrossRefGoogle Scholar
  13. 13.
    Michalewicz, Z., Fogel, D.B.: How To Solve It: Modern Heuristics. Springer, Heidelberg (1998)Google Scholar
  14. 14.
    Papin, J., Price, N., Wiback, S., Fell, D., Palsson, B.: Metabolic pathways in the post genome era. Trends in Biochemical Sciences 28, 250–258 (2003)CrossRefGoogle Scholar
  15. 15.
    Schena, M., Shalon, D., Heller, R., Chai, A., Brown, P.O., Davies, R.W.: Parallel Human Genome Analysis: Microarray Based Expression Monitoring of 1,000 Genes. PNAS 93, 10614–10619 (1996)CrossRefGoogle Scholar
  16. 16.
    Slonim, D.K.: From patterns to pathways: gene expression data analysis comes of age. Nature Genetics 32, 502–508 (2002)CrossRefGoogle Scholar
  17. 17.
    Swift, S., Tucker, A., Vinciotti, V., Martin, N., Orengo, C., Liu, X., Kellam, P.: Consensus clustering and functional interpretation of gene expression data. Genome Biology 5, R94 (2004)CrossRefGoogle Scholar
  18. 18.
    Edgar, R., Domrachev, M., Lash, A.E.: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research 30(1), 207–210 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eleftherios Panteris
    • 1
  • Stephen Swift
    • 1
  • Annette Payne
    • 1
  • Xiaohui Lui
    • 1
  1. 1.School of Information Systems, Computing and MathematicsBrunel UniversityUxbridge, MiddlesexUK

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