Data Mining for Bioinformatics— Systems Biology

  • T.V. Prasad
  • S.I. Ahson


Biology, like many other sciences, changes when technology brings in new tools that extend the scope of inquiry. The invention of the optical microscope in late 1600 brought an entirely new vista to biology when cellular structures could be more clearly seen by scientists. Much more modern and recent electron microscope developed in the 60’s enhanced the visualization of cells considerably. The application of computing to biological problems has created yet another new opportunity for the biologists of the 21st century. As computers continue to change the society at large, there is not doubt that several years of development in databases, software for data analysis, computational algorithms, computer generated visualization, use of computers to determine structures of complex bio-molecules, computational simulation of ecosystem, analysis of evolutionary pathways and many more computational methods have brought several new dimensions to biology. The technological revolution, from an ordinary computer to high-performance/grid computing, the processes have further automated, and led to flooding of data which cannot be handled properly, due to lack of proper standards at proper time. Billions of records are being pushed by the researchers and scientists into the data repositories across the world.


System Biology Markup Language System Biology Markup Language Model Gulatory Network Monte Carlo Simulation Tool Finite Linear State 
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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References Articles/Papers/Presentations/Books

  1. Aravind, L. (2000). Guilt by association: Contextual information in genome analysis, Genome Research, 10: 1074–1077.Google Scholar
  2. Baldi, P. and Brunak, S. (2003). Bioinformatics: The Machine Learning Approach, 2ed. Affiliated East-West Press Pvt. Ltd., New Delhi.Google Scholar
  3. D’haeseler, P., Liang, S. and Somogyi, R. (2000). Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics, 16: 707–726.CrossRefGoogle Scholar
  4. Diehn, M. and Relman, D. (2001). Comparing functional genomic datasets: Lessons from DNA microarray analyses of host-pathogen interactions. Current Opinion on Microbiology, 4: 95–101.CrossRefGoogle Scholar
  5. Eisenberg, D., Marcotte, E.M., Xenarios, I. and Yeates, T.O. (2000). Protein function in the post-genomic era. Nature, 405: 823–826.PubMedCrossRefGoogle Scholar
  6. Forst, C.V. and Schulten, K. (1999). Evolution of metabolism: A new method for the comparison of metabolic pathways using genomic information. Journal of Computational Biology, 6: 343–360.PubMedCrossRefGoogle Scholar
  7. Forst, C.V. (2001). A Tutorial on Network Genomics. International Conference on Intelligent Systems for Molecular Biology, Copenhagen, Denmark.Google Scholar
  8. Funahashi, A. (2003). Introduction to SBML and SBML compliant software. ERATO Kitano Symbiotic Systems Project, presentation slides. Available at workshops/tokyotutorial/tutorial.htm.Google Scholar
  9. Fuente, A. de la and Mendes, P. (2003). Integrative modeling of gene expression and cell metabolism. Applied Bioinformatics, Open Mind Journals, 2(2): 79–90.Google Scholar
  10. Goldsby, R.A., Kindt, T.J. and Osborne, B.A. (1999). Kuby Immunology. W. H. Freeman & Co., 4 edition.Google Scholar
  11. Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. Elsevier, San Francisco.Google Scholar
  12. Hayes, B. (2000). Graph Theory in Practice: Part 1. American Scientist, 88(1). Available at Scholar
  13. Jagadish, H.V. and Olken, F. (2003). Data Management for the Biosciences. Report of the NSF/NLM Workshop of Data Management for Molecular and Cell Biology, Feb 2003. Available at wdmb_rpt.pdf.Google Scholar
  14. Jagota, Arun (2001). Microarray data analysis and visualization. Dept. of Computer Engineering, University of California, CA., USA.Google Scholar
  15. McAdams, H. and Shapiro, L. (1995). Circuit simulation of genetic networks. Science, 269: 650–656.PubMedCrossRefGoogle Scholar
  16. Mitchell, T.M. (1997). Machine Learning. McGraw Hill International Edition, New Delhi.Google Scholar
  17. Schmidt, H. and Jirstrand, M. (2005). Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology. Bioinformatics Advance Access, available at Scholar
  18. Somogyi, R. and Sniegoski, C. (1996). Modeling the complexity of genetic networks. Complexity, 1: 45–63.Google Scholar
  19. Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. (1999). Systematic determination of genetic network architecture. Nature Genetics, 22: 281–285.PubMedCrossRefGoogle Scholar
  20. Ullah, M. and Wolkenhauer, O. (2007). Family tree of Markov models in systems biology. IET Systems Biology, 1(4).Google Scholar
  21. White, K.P., Rifkin, S.A., Hurban, P. and Hogness, D.S. (1999). Microarray analysis of Drosophila development during metamorphosis. Science, 286(5447): 2179–2184.PubMedCrossRefGoogle Scholar
  22. Wiechert, W. (2002). Modeling and simulation: Tools for metabolic engineering. Journal of Biotechnology, 94: 37–63.PubMedCrossRefGoogle Scholar
  23. Wolkenhauer, O. (2002). Mathematical modelling in the post-genome era: understanding genome expression and regulation — a system theoretic approach. BioSystems, 65: 1–18.PubMedCrossRefGoogle Scholar
  24. Wooley, J.C. and Lin, H.S. (2001). Catalyzing inquiry at the interface of Computing and Biology. The National Academies Press, Washington D.C. Available at http:/ / Scholar
  25. Zhu, H. and Snyder, M. (2001). Protein arrays and microarrays. Current Opinion in Chemical Biology, 5: 40–45.PubMedCrossRefGoogle Scholar


  1. Biology WorkBench. San Diego Supercomputer Centre, Bioinformatics and Computational Biology group, Department of Bioengineering at University of California, San Diego, Available at Scholar
  2. BioPathways Consortium. BioPathways website. Available at http:// Scholar
  3. Colantouni, C., Henry, G. and Pevsner, J. (2000). Standardization and Normalization of Microarray Data (SNOMAD) software. Available at http:// Scholar
  4. de Hoon, M., Imoto, S. and Miyano, S. (2004). The C Clustering Library (Cluster 3.0) software. University of Tokyo, Institute of Medical Science, Human Genome Center, Japan. Available at Scholar
  5. DeRisi Lab. Department of Biochemistry & Biophysics, University of California at San Francisco. Website available at Scholar
  6. European Bioinformatics Institute (EBI). EBI website. Available at http:// Scholar
  7. Gene Expression Data Analysis (GEDA) Tool. GEDA software. University of Pennsylvania MC Health Systems. Available at GEDA.html.Google Scholar
  8. Genomes: GTL (32007). Genomes: GTL (formerly Genomes to Life Initiative), US Department of Energy. Available at Scholar
  9. Harvard Medical School. Department of Systems Biology, Harvard Medical School. Available at Scholar
  10. IBM. IBM Research website. Available at Scholar
  11. Institute for Systems Biology (ISB) (2007). ISB website. Available at http:// Scholar
  12. Joint Center for Structural Genomics. Joint Center for Structural Genomics. Available at Scholar
  13. Keck Graduate Institute. Keck Computational Systems Biology (California) website. Available at Scholar
  14. Massachusetts Institute of Technology. Computational and Systems Biology Initiative (MIT) website. Available at Scholar
  15. Molecular Sciences Institute. Molecular Sciences Institute (Berkeley). Available at http:/ Scholar
  16. National Cancer Institute (2002). Gene Expression Data Portal (GEDP), National Institutes of Health, USA. Available at http://gedp.nci.nih/gov/de/servlet/manager.Google Scholar
  17. National Centre for Biotechnology Information (2002). Gene Expression Omnibus (GEO), National Institutes of Health, USA. Gene expression datasets available at Scholar
  18. Ottawa Institute of Systems Biology. Ottawa Institute of Systems Biology (Canada) website. Available at Scholar
  19. Pacific Northwest National Laboratory. Systems Biology at PNNL. Available at Scholar
  20. Princeton University. Lewis Siegler Institute for Integrative Genomics, Princeton University. Available at Scholar
  21. Princeton University. Center for Systems Biology, Institute for Advanced Study, Princeton University. Available at Scholar
  22. SBML. Caltech ERATO Kiranto Systems Biology Project Group. Available at http:/ / Scholar
  23. SilicoCyte (2004). SilicoCyte v 1.3 software. Available at Scholar
  24. Systems Biology Institute, Japan (2003). The Systems Biology Institute (Japan) website. Available at, see also Scholar
  25. Tom Sawyer (2003). Tom Sawyer software Image Gallery. Website available at http:/ / Scholar
  26. University of Auckland. University of Auckland, New Zealand. Available at http:// Scholar
  27. University of Stuttgart. Systems Biology Group at the University of Stuttgart (Germany) website. Available at Scholar
  28. Virginia Tech. GEPASI software website, Virginia Bioinformatics Institute, Virginia Tech. Available at Scholar

Copyright information

© Capital Publishing Company 2009

Authors and Affiliations

  • T.V. Prasad
    • 1
  • S.I. Ahson
    • 2
  1. 1.Dept. of Computer Science & EngineeringLingaya’s Institute of Management & TechnologyFaridabad
  2. 2.Department of Computer ScienceJamia Millia IslamiaNew Delhi

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