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Invited Keynote Talk: Integrative Viral Molecular Epidemiology: Hepatitis C Virus Modeling

  • James Lara
  • Zoya Dimitrova
  • Yuri Khudyakov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)

Abstract

Traditional molecular epidemiology of viral infections is based on identifying genetic markers to assist in epidemiological investigation. The limitations of early molecular technologies led to preponderance of analytical methodology focused on the viral agent itself. Computational analysis was almost exclusively used for phylogenetic inference. Embracing the approaches and achievements of the traditional molecular epidemiology, integrative molecular epidemiology of viral infections expands into a comprehensive analysis of all factors involved into defining outcomes of exposure of a person(s) to viral infections. The major emphasis of this scientific discipline is on the development of predictive models that can be used in different clinical and public health settings. The current paper briefly reviews a few examples that illustrate a new trend in integrative molecular epidemiology striving to quantitatively define viral properties and parameters using primary structure of viral genomes.

Keywords

Human Immunodeficiency Virus Type Molecular Epidemiology Bayesian Network Model Replication Fitness Public Health Setting 
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

  1. 1.
    Schulte, P.A., Perera, F.A.: Molecular Epidemiology: Principles and Practice. Academic Press, London (1993)Google Scholar
  2. 2.
    Weck, K.: Molecular methods of hepatitis C genotyping. Expert. Rev. Mol. Diagn. 5, 507–520 (2005)CrossRefGoogle Scholar
  3. 3.
    Inudoh, M., Nyunoya, H., Tanaka, T., Hijikata, M., Kato, N., Shimotohno, K.: Antigenicity of hepatitis C virus envelope proteins expressed in Chinese hamster ovary cells. Vaccine 14, 1590–1596 (1996)CrossRefGoogle Scholar
  4. 4.
    Khudyakov, Y.E., Dou, X.-G., Chang, J., Fields, H.A.: Impact of Sequence Heterogeneity on Antigenic Properties of the Hepatitis C Virus (HCV) Proteins. Margolis, Alter, Conlon, Dienstag, Liang (eds.), pp. 381–385. International Medical Pressm, London, UK (2002)Google Scholar
  5. 5.
    Engelman, D.M., Steitz, T.A., Goldman, A.: Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annu. Rev. Biophys. Biophys. Chem. 15, 321–353 (1986)CrossRefGoogle Scholar
  6. 6.
    Schneider, G., Wrede, P.: Artificial neural networks for computer-based molecular design. Prog. Biophys. Mol. Biol. 70, 175–222 (1998)CrossRefGoogle Scholar
  7. 7.
    Creighton, T.E.: Proteins: Structures and Molecular Properties. W.H. Freeman and Company, New York (1993)Google Scholar
  8. 8.
    White, J.V., Stultz, C.M., Smith, T.F.: Protein classification by stochastic modeling and optimal filtering of amino-acid sequences. Math. Biosci. 119, 35–75 (1994)zbMATHCrossRefGoogle Scholar
  9. 9.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations of back-propagation erros. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  10. 10.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing, MIT, Cambridge (1986)Google Scholar
  11. 11.
    Kolaskar, A.S., Kulkarni-Kale, U.: Prediction of three-dimensional structure and mapping of conformational epitopes of envelope glycoprotein of Japanese encephalitis virus. Virology 261, 31–42 (1999)CrossRefGoogle Scholar
  12. 12.
    Kulkarni-Kale, U., Bhosle, S., Kolaskar, A.S.: CEP: A conformational epitope prediction server. Nucleic Acids Res. 33, 168–171 (2005)CrossRefGoogle Scholar
  13. 13.
    Kolaskar, A.S., Tongaonkar, P.C.: A semi-empirical method for prediction of antigenic determinants on protein antigens. Febs Lett. 276, 172–174 (1990)CrossRefGoogle Scholar
  14. 14.
    Wilkins, M.R., Gasteiger, E., Bairoch, A., Sanchez, J.C., Williams, K.L., Appel, R.D., Hochstrasser, D.F.: Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531–552 (1999)Google Scholar
  15. 15.
    Bhaskaran, R., Ponnuswamy, P.K.: Dynamics of amino acid residues in globular proteins. Int. J. Pept. Protein Res. 24, 180–191 (1984)Google Scholar
  16. 16.
    Hopp, T.P., Woods, K.R.: Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl. Acad. Sci. U.S.A. 78, 3824–3828 (1981)CrossRefGoogle Scholar
  17. 17.
    Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105–132 (1982)CrossRefGoogle Scholar
  18. 18.
    Eisenberg, D., Schwarz, E., Komaromy, M., Wall, R.: Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J. Mol. Biol. 179, 125–142 (1984)CrossRefGoogle Scholar
  19. 19.
    Cooper, G., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  20. 20.
    Masso, M., Vaisman, I.I.: Accurate prediction of enzyme mutant activity based on a multibody statistical potential. Bioinformatics 23, 3155–3161 (2007)CrossRefGoogle Scholar
  21. 21.
    Franco, S., Parera, M., Aparicio, E., Clotet, B., Martinez, M.A.: Genetic and catalytic efficiency structure of an HCV protease quasispecies. Hepatology 45, 899–910 (2007)CrossRefGoogle Scholar
  22. 22.
    Rognvaldsson, T., You, L.: Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinformatics 20, 1702–1709 (2004)CrossRefGoogle Scholar
  23. 23.
    Lara, J., Gao, F.X., Xia, G., Nainan, O., Khudyakov, Y.: Bayesian networks for evaluation of dependencies between epidemiological, virological and molecular features of the hepatitis C virus infections. J. Clin. Virol. 36 (suppl. 2), pp. S119 (2006)Google Scholar
  24. 24.
    Sprites, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. MIT Press, Cambridge (2000)Google Scholar
  25. 25.
    Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 19, 191–201 (1995)zbMATHCrossRefGoogle Scholar
  26. 26.
    Rabinowitz, M., Myers, L., Banjevic, M., Chan, A., Sweetkind-Singer, J., Haberer, J., McCann, K., Wolkowicz, R.: Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization. Bioinformatics 22, 541–549 (2006)CrossRefGoogle Scholar
  27. 27.
    Lengauer, T., Sander, O., Sierra, S., Thielen, A., Kaiser, R.: Bioinformatics prediction of HIV coreceptor usage. Nat. Biotechnol. 25, 1407–1410 (2007)CrossRefGoogle Scholar
  28. 28.
    Sierra, S., Kaiser, R., Thielen, A., Lengauer, T.: Genotypic coreceptor analysis. Eur. J. Med. Res. 12, 453–462 (2007)Google Scholar
  29. 29.
    Skrabal, K., Low, A.J., Dong, W., Sing, T., Cheung, P.K., Mammano, F., Harrigan, P.R.: Determining human immunodeficiency virus coreceptor use in a clinical setting: Degree of correlation between two phenotypic assays and a bioinformatic model. J. Clin. Microbiol. 45, 279–284 (2007)CrossRefGoogle Scholar
  30. 30.
    Dykes, C., Demeter, L.M.: Clinical significance of human immunodeficiency virus type 1 replication fitness. Clin. Microbiol. Rev. 20, 550–578 (2007)CrossRefGoogle Scholar
  31. 31.
    Wu, H., Huang, Y., Dykes, C., Liu, D., Ma, J., Perelson, A.S., Demeter, L.M.: Modeling and estimation of replication fitness of human immunodeficiency virus type 1 in vitro experiments by using a growth competition assay. J. Virol. 80, 2380–2389 (2006)CrossRefGoogle Scholar
  32. 32.
    Lin, E., Hwang, Y., Wang, S.C., Gu, Z.J., Chen, E.Y.: An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics 7, 1017–1024 (2006)CrossRefGoogle Scholar
  33. 33.
    Lin, E., Hwang, Y., Chen, E.Y.: Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C. Pharmacogenomics 8, 1327–1335 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • James Lara
    • 1
  • Zoya Dimitrova
    • 1
  • Yuri Khudyakov
    • 1
  1. 1.Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral HepatitisCenters for Disease Control and PreventionAtlantaUSA

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