Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining

  • Su-Shing Chen
  • William E. Haskins
  • Andrew K. Ottens
  • Ronald L. Hayes
  • Nancy Denslow
  • Kevin K. W. Wang
Part of the Springer Optimization and Its Applications book series (SOIA, volume 7)


The importance of neuroproteomic studies is that they will help elucidate the currently poorly understood biochemical mechanisms or pathways underlying various psychiatric, neurological and neurodegenerative diseases. In this chapter, we focus on traumatic brain injury (TBI), a neurological disorder currently with no FDA approved therapeutic treatment. This chapter describes data mining strategies for proteomic analysis in traumatic brain injury research so that the diagnosis and treatment of TBI can be developed. We should note that brain imaging provides only coarse resolutions and proteomic analysis yields much finer resolutions to these two problems. Our data mining approach is not only at the collected data level, but rather an integrated scheme of animal modeling, instrumentation and data analysis.


Traumatic Brain Injury Protein Identification Traumatic Brain Injury Patient Protein Separation Control Cortical Impact 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J.N. Adkins, S.M. Varnum, K.J. Auberry, R.J. Moore, N.H. Angell, R.D. Smith, D.L. Springer, and J.G. Pounds. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Molecular & Cellular Proteomics, 1: 947–955, 2002.CrossRefGoogle Scholar
  2. 2.
    R. Aebersold and J.D. Watts. The need for national centers for proteomics. Nature Biotechnology, 20(7): 651–651, 2002.PubMedCrossRefGoogle Scholar
  3. 3.
    R.D. Appel, A. Bairoch, and D.F. Hochstrasser. 2-D Databases on the World Wide Web in Methods in Molecular Biology. In A.J. Link, editor, 2-D Proteome Analysis Protocols, pages 383–391. Humana Press, Totowa, NJ, 1999.Google Scholar
  4. 4.
    R. Axelrod. Structure of Decision. Princeton University Press, 1976.Google Scholar
  5. 5.
    W.V. Bienvenut, C. Deon, C. Pasquarello, J.M. Campbell, J.C. Sanchez, M.L. Vestal, and D.F. Hochstrasser. Matrix-assisted laser desorption/ionization-tandem mass spectrometry with high resolution and sensitivity for identification and characterization of proteins. Proteomics, 2(7): 868–876, 2002.PubMedCrossRefGoogle Scholar
  6. 6.
    B. Bjellqvist, K. Ek, P.G. Righetti, E. Gianazza, A. Gorg, and R. Westermeier. Isoelectric focusing in immobilized pH gradients: Principle, methodology and some applications. Journal of Biochemical and Biophysical Methods, 6: 317–339, 1982.PubMedCrossRefGoogle Scholar
  7. 7.
    J. Boguslavsky. Resolving the proteome by relying on 2DE methods. Drug Discovery & Development, 6(7): 57–60, 2003.Google Scholar
  8. 8.
    R.G.G. Cattell, editor. The Object Database Standard: ODMG-93, Morgan Kaufmann, 1996.Google Scholar
  9. 9.
    D.N. Chakravarti, B. Chakrarti, and I. Moutsatsos. Informatic tools for proteome profiling. Bio Techniques, Computational Proteomics Supplement, 32: S4–S15, 2002.Google Scholar
  10. 10.
    S. Chen. Knowledge acquisition on neural networks. In B. Bouchon, L. Saitta and R. R. Yager, editors, Uncertainty and Intelligent Systems, pages 281–289. Lecture Notes in Computer Science, Springer-Verlag, Vol. 313, 1988.Google Scholar
  11. 11.
    S. Chen. Some extensions of probabilistic logic. Proceedings of the AAAI Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, August 8–10, pages 43–48, 1986; An extended version appeared in Uncertainty in Artificial Intelligence, Vol. 2, edited by L. N. Kanal and J. F. Lemmer, North-Holland, 1986.Google Scholar
  12. 12.
    S. Chen. Automated reasoning on neural networks: A probabilistic approach. IEEE First International Conference on Neural Networks, San Diego CA, June 21–24, 1987.Google Scholar
  13. 13.
    S. Chen. Knowledge discovery of gene functions and metabolic pathways. IEEE BioInformatic and Biomedical Engineering Conference, Washington, DC, November 2000.Google Scholar
  14. 14.
    S. Chen. Knowledge representation for systems biology. First International Conference on Systems Biology, Tokyo Japan, November 14–16, 2000.Google Scholar
  15. 15.
    S. Chen. Digital Libraries: The Life Cycle of Information. Better Earth Publisher, 1998.Google Scholar
  16. 16.
    P. Davidsson, A. Westman-Brinkmalm, C.L. Nilsson, M. Lindbjer, L. Paulson, N. Andreasen, M. Sjogren, and K. Blennow. Proteome analysis of cerebrospinal fluid proteins in Alzheimer patients. Neuroreport, 13(5):611–5, 2002.PubMedCrossRefGoogle Scholar
  17. 17.
    N.D. Denslow, M.E. Michel, M.D. Temple, C. Hsu, K. Saatman, and R.L. Hayes. Application of Proteomics Technology to the Field of Neurotrauma. Journal of Neurotrauma, 20: 401–407, (2003).PubMedCrossRefGoogle Scholar
  18. 18.
    S. Derra. Lab-on-a-chip technologies emerging from infancy. Drug Discovery & Development, 6(5): 40–45, 2003.Google Scholar
  19. 19.
    J. Finnie Animal models of traumatic brain injury: a review. Australian Veterinary Journal, 79(9): 628–633, 2001.PubMedCrossRefGoogle Scholar
  20. 20.
    M. Fountoulakis, R. Hardmaier, E. Schuller, and G. Lubec. Differences in protein level between neonatal and adult brain. Electrophoresis, 21(3): 673–678, 2000.PubMedCrossRefGoogle Scholar
  21. 21.
    M. Fountoulakis, E. Schuller, R. Hardmeier, P. Berndt, and G. Lubec. Rat brain proteins: two-dimensional protein database and variations in the expression level. Electrophoresis, 20(18): 3572–3579, 1999.PubMedCrossRefGoogle Scholar
  22. 22.
    S.A. Gerber, J. Rush, O. Stemman, M.W. Kirschner, and S.P. Gygi. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proceedings of the National Academy of Sciences, 100: 6940–6945, 2003.CrossRefGoogle Scholar
  23. 23.
    I. Goldknopf, H.R. Park, and H.M. Kuerer. Merging diagnostics with therapeutic proteomics. IVD Technology, 9(1): 1–6, 2003.Google Scholar
  24. 24.
    A. Gorg, W. Postel, and S. Gunther. The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis, 9: 531–546, 1988.PubMedCrossRefGoogle Scholar
  25. 25.
    S.G. Grant, W.P. Blackstock. Proteomics in neuroscience: from protein to network. Journal of Neuroscience, 21(21): 8315–8, 2001.PubMedGoogle Scholar
  26. 26.
    S.G.N. Grant and H. Husi. Proteomics of multiprotein complexes: answering fundamental questions in neuroscience. Trends in Biotechnology, 19(10 Suppl):S49–54, 2001.PubMedCrossRefGoogle Scholar
  27. 27.
    S. Graslund, R. Falk, E. Brundell, C. Hoog, and S. Stahl. A high-stringency proteomics concept aimed for generation of antibodies specific for cDNA-encoded proteins. Biotechnology and Applied Biochemistry, 35(Pt 2): 75–82, 2002.PubMedCrossRefGoogle Scholar
  28. 28.
    S.P. Gygi, B. Rist, S.A. Gerber, F. Turecek, M.H. Gelb, and R. Aebersold. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnology, 17: 994–9, 1999.PubMedCrossRefGoogle Scholar
  29. 29.
    S.P. Gygi and R. Aebersold. Mass spectrometry and proteomics. Current Opinion in Chemical Biology, 4: 489494, 2000.CrossRefGoogle Scholar
  30. 30.
    S. Hanash. HUPO initiatives relevant to clinical proteomics. Molecular and Cellular Proteomics, 3: 298–301, 2004.PubMedCrossRefGoogle Scholar
  31. 31.
    S. Hanash. Disease proteomics. Nature, 422(6928): 226–232, 2003.PubMedCrossRefGoogle Scholar
  32. 32.
    D.F. Hochstrasser, J.C. Sanchez, and R.D. Appel. Proteomics and its trends facing nature’s complexity. Proteomics, 2(7): 807–812, 2002.PubMedCrossRefGoogle Scholar
  33. 33.
    H. Husi and S.G. Grant. Proteomics in neuroscience: from protein to network. Journal of Neuroscience, 21(21): 8315–8318, 2001.Google Scholar
  34. 34.
    P. James. Chips for proteomics; a new tool or just hype? Biotechniques, Suppl:4–10, 2002PubMedGoogle Scholar
  35. 35.
    D. Janssen. Major approaches to identifying key PTMs. Genomics and Proteomics, 3(1): 38–41, 2003.Google Scholar
  36. 36.
    P. Jungblut, B. Thiede, U. Zimny-Arndt, E.C. Muller, C. Scheler, and B. Wittmann-Liebold. Resolution power of two-dimensional electrophoresis and identification of proteins from gels. Electrophoresis, 17: 839–847, 1996.PubMedCrossRefGoogle Scholar
  37. 37.
    H. Kitano. Systems biology: a brief overview. Science, 295(5560): 1662–4, 2002.PubMedCrossRefGoogle Scholar
  38. 38.
    H. Kitano. Computational systems biology. Nature, 420(6912): 206–10, 2002.PubMedCrossRefGoogle Scholar
  39. 39.
    H. Kitano. Perspectives on systems biology. New Generation Computing, 18(3): 199–216, 2000.Google Scholar
  40. 40.
    B. Kosko. Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24: 65–75, 1986.CrossRefGoogle Scholar
  41. 41.
    W. Kusnezow and J.D. Hoheisel. Antibody microarrays: promises and problems. Bio Techniques, 33(suppl.): 1423, 2002.Google Scholar
  42. 42.
    P.F. Lemkin. Comparing Two-dimensional Electrophoretic Gel Images Across the Internet. Electrophoresis, 18: 2759–2773, 1997.PubMedCrossRefGoogle Scholar
  43. 43.
    B. Lu and T. Chen. A suffix tree approach to the interpretation of tandem mass spectra: applications to peptides of non-specific digestion and post-translational modifications. Bioinformatics, 19(Suppl 2): II113–II121, 2003.PubMedGoogle Scholar
  44. 44.
    G. Lubec, K. Krapfenbauer, and M. Fountoulakis. Proteomics in brain research: potentials and limitations. Progress in Neurobiology, 69(3): 193–211, 2003.PubMedCrossRefGoogle Scholar
  45. 45.
    P. Mendes. GEPASI: a software for modeling the dynamics, steady states and control of biochemical and other systems. Computer Applications in the Biosciences, 9(5): 563–571, 1993.PubMedGoogle Scholar
  46. 46.
    P. Mendes. Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends in Biochemical Sciences, 22: 361–363, 1997.PubMedCrossRefGoogle Scholar
  47. 47.
    H.E. Meyer, J. Klose, and M. Hamacher. HBPP and the pursuit of standardisation. Lancet Neurology, 2(11): 657–658, 2003.PubMedCrossRefGoogle Scholar
  48. 48.
    M.D. Moody. Array-based ELISAs for high-throughput analysis of human cytokines. Biotechniques, 31: 186–194, 2001.PubMedGoogle Scholar
  49. 49.
    W.F. Patton. Detection technologies in proteome analysis. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 771(1–2): 3–31, 2002.PubMedCrossRefGoogle Scholar
  50. 50.
    J. Peng, J.E. Elias, C.C. Thoreen, L.J. Licklider, and S.P. Gygi. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large scale protein analysis: The yeast proteome. Journal of Proteome Research, 2: 43–50, 2003.PubMedCrossRefGoogle Scholar
  51. 51.
    B.R. Pike, J. Flint, J.R. Dave, X.-C. Lu, K.K.W. Wang, F.C. Tortella, and R.L. Hayes. Accumulation of calpain and caspase-3 proteolytic fragments of brain-derived αII-spectrin in CSF after middle cerebral artery occlusion in rats. Journal of Cerebral Blood Flow & Metabolism, 24(1): 98–106, 2004.CrossRefGoogle Scholar
  52. 52.
    B.R. Pike, J. Flint, S. Dutta, E. Johnson, K.K.W. Wang, and R.L. Hayes. Accumulation of non-erythroid aII-spectrin and calpain-cleaved aII-spectrin breakdown products in cerebrospinal fluid after traumatic brain injury in rats. Journal of Neurochemistry, 78: 1297–1306, 2001.PubMedCrossRefGoogle Scholar
  53. 53.
    A. Raabe, C. Grolms, and V. Seifert. Serum markers of brain damage and outcome prediction in patients after severe head injury. British Journal of Neurosurgery, 13: 56–59, 1999.PubMedCrossRefGoogle Scholar
  54. 54.
    R. Raghupathi, D.I. Graham, and T.K. Mcintosh. Apoptosis after traumatic brain injury. Journal of Neurotrauma, 17(10): 927–938, 2000.PubMedCrossRefGoogle Scholar
  55. 55.
    V.N. Reddy, M.L. Mavrovouniotis, and M. N. Liebman. Petri net representations in metabolic pathways. Proceedings of ISMB, pp. 328–336, 1993.Google Scholar
  56. 56.
    D.R. Reyes, D. Iossifidis, P.A. Auroux, and A. Manz. Micro total analysis systems. 1. Introduction, theory, and technology. Analytical Chemistry, 74(12): 2623–2636, 2002.PubMedCrossRefGoogle Scholar
  57. 57.
    B. Romner, T. Ingebrigtsen, P. Kongstad, S.E. Borgesen. Traumatic brain damage: serum S-100 protein measurements related to neuroradiological findings. Journal of Neurotrauma, 17(8): 641–647, 2000.PubMedCrossRefGoogle Scholar
  58. 58.
    H. Schäfer, K. Marcus, A. Sickmann, M. Herrmann, J. Klose, and H.E. Meyer. Identification of phosphorylation and acetylation sites in alphaA-crystallin of the eye lens (mus musculus) after two-dimensional gel electrophoresis. Analytical and Bioanalytical Chemistry, 376(7): 966–972, 2003.CrossRefGoogle Scholar
  59. 59.
    D.C. Schwartz and M. Hochstrasser. A superfamily of protein tags: ubiquitin, SUMO and related modifiers. Trends in Biochemical Sciences, 28(6): 321–328, 2003.PubMedCrossRefGoogle Scholar
  60. 60.
    R.F. Service. Gold rush-High-speed biologists search for gold in proteins. Science, 294(5549): 2074–2077, 2001.PubMedCrossRefGoogle Scholar
  61. 61.
    R.D. Smith. Probing proteomes-seeing the whole picture? Nature Biotechnology, 18: 1041–1042, 2000.PubMedCrossRefGoogle Scholar
  62. 62.
    R. Somogyi and C.A. Sniegoski. Modeling the complexity of genetic networks: understanding multigenic and pleiotropic regulation. Complexity, 1: 45–63, 1996.Google Scholar
  63. 63.
    D.L. Tabb, W.H. McDonald, and J.R. Yates. DTASelect and contrast: Tools for assembling and comparing protein identifications from shotgun proteomics. Journal of Proteome Research, 1: 21–26, 2002.PubMedCrossRefGoogle Scholar
  64. 64.
    M. Unlu, M. E. Morgan, and J.S. Minden. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 18(11): 2071–2077, 1997.PubMedCrossRefGoogle Scholar
  65. 65.
    A. Wiesner. Detection of Tumor Markers with ProteinChip(R) Technology. Current Pharmaceutical Biotechnology, 5(1): 45–67, 2004.PubMedCrossRefGoogle Scholar
  66. 66.
    J.R. Yates III, E. Carmack, L. Hays, A.J. Link, and J.K. Eng. Automated Protein Identification using Microcolumn Liquid Chromatography-Tandem Mass Spectrometry. In A.J. Link, editor, 2-D Proteome Analysis Protocols, pages 553–569. Humana Press, Totowa, NJ, 1999.Google Scholar
  67. 67.
    J.R. Yates, S.F. Morgan, C.L. Gatlin, P.R. Griffin, and J.K. Eng. Method to compare collision-induced dissociation spectra of peptides: Potential for library searching and subtractive analysis. Analytical Chemistry, 70: 3557–3565, 1998.PubMedCrossRefGoogle Scholar
  68. 68.
    W.R. Zhang, S. Chen, W. Wang, and R.S. King. A cognitive map based approach to the coordination of distributed cooperative agents. IEEE Transactions on Systems, Man, and Cybernetics, 22: 103–114, 1992.CrossRefGoogle Scholar
  69. 69.
    W.R. Zhang, S. Chen, and J.C. Bezdek. Pool2: A generic system for cognitive map development and decision analysis. IEEE Transactions on Systems, Man, and Cybernetics, 19: 31–39, 1989.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Su-Shing Chen
    • 1
    • 6
  • William E. Haskins
    • 1
    • 2
    • 4
  • Andrew K. Ottens
    • 1
    • 2
    • 4
  • Ronald L. Hayes
    • 2
    • 3
    • 4
  • Nancy Denslow
    • 1
    • 5
  • Kevin K. W. Wang
    • 1
    • 2
    • 3
    • 4
  1. 1.Center of Neuroproteomics and Biomarkers ResearchUniversity of FloridaGainesville
  2. 2.Center for Traumatic Brain Injury StudiesUniversity of FloridaGainesville
  3. 3.Department of PsychiatryUniversity of FloridaGainesville
  4. 4.Department of NeuroscienceUniversity of FloridaGainesville
  5. 5.Interdisciplinary Center of Biomedical ResearchUniversity of FloridaGainesville
  6. 6.Computer and Information Science EngineeringUniversity of FloridaGainesville

Personalised recommendations