Max-Planck-Institut für Informatik
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Bioinformatics for HIV

HIV/AIDS is a leading cause of worldwide infectious mortality, which will soon have more deaths than any other disease epidemic in recorded history. We work on several aspects of this disease.

  • Assessing the efficacy of a drug combination is challenging, because many factors come into play, such as patient compliance, drug bioavailability, drug toxicity, virus phenotype and viral load. These factors are often unknown. We have developed a method (THEO - THErapy Optmizer) for estimating the activity of a combination therapy in terms of its ability to reduce the viral load below the limit of detection. Predictions are based on the viral DNA, which codes for the moeluclar targets of the corresponding drugs, and a statistical version of the genetic barrier to drug resistance, which estimates the risk of the virus to escape the selected regimen.
  • The viral evasion of the host cytotoxic T lymphocyte (CTL) response through mutation is a major challenge both for vaccine and naturally induced immune control of HIV-1. We are examining the influence of HLA genotypes on the development of resistance mutations in HIV-1.
  • Recombination plays an important role in the evolution of pathogens, including HIV. We are developing fast and novel methods for the analysis of recombining sequences, which model recombination events explicitly.
  • New entry inhibitor drugs target one of two chemokine receptors, CCR5 and CXCR4. The coreceptor usage of a virus varies depending on the type of virus a patient is infected with. Before and during drug treatment with a coreceptor antagonist, it is important to find out about the coreceptor usage of the virus population in the patient. As an alternative to time-consuming and expensive experimental methods, bioinformatics methods for predicting coreceptor usage directly from the viral sequences are being developed.


  1. André Altmann, Niko Beerenwinkel, Tobias Sing, Igor Savenkov, Martin Däumer, Rolf Kaiser, Soo-Yon Rhee, W Jeffrey Fessel, Robert W Shafer, Thomas Lengauer
    Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance
    Antiviral Therapy 2007; 12: 169-178. (Abstract)
  2. Niko Beerenwinkel, Martin Däumer, Tobias Sing, Jörg Rahnenführer, Thomas Lengauer, Joachim Selbig, Daniel Hoffmann, Rolf Kaiser
    Estimating HIV evolutionary pathways and the genetic barrier to drug resistance.
    The Journal of Infectious Diseases 2005 Jun 1;191(11):1953-60. (Abstract)
  3. Niko Beerenwinkel, Jörg Rahnenführer, Rolf Kaiser, Daniel Hoffmann, Joachim Selbig, Thomas Lengauer
    Mtreemix: a software package for learning and using mixture models of mutagenetic trees.
    Bioinformatics. 2005 Jan 18; [Epub ahead of print] (Abstract)
  4. N. Beerenwinkel, Däumer, M. Oette, K. Korn, D. Hoffmann, R. Kaiser, T. Lengauer, J. Selbig, H. Walter
    Geno2pheno: estimating phenotypic drug resistance from HIV-1 genotypes
    Nucleic Acids Research 2003, 31(13), 3850-3855. (Paper at NAR)
  5. N. Beerenwinkel, B. Schmidt, H. Walter, R. Kaiser, T. Lengauer, D. Hoffmann, K. Korn, J. Selbig
    Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype
    Proceedings of the National Academy of Sciences U.S.A. 2002, 99(12), 8271-8276. (Paper at PNAS Online)
  6. N. Beerenwinkel, B. Schmidt, H. Walter, R. Kaiser, T. Lengauer, D. Hoffmann, K. Korn, J. Selbig
    Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests
    IEEE Intelligent Systems in Biology 2001, 16(6), 35-41.
  7. N. Beerenwinkel, J. Rahnenführer, M. Däumer, D. Hoffmann, R. Kaiser, J. Selbig, T. Lengauer
    Learning multiple evolutionary pathways from cross-sectional data
    Proc. 8th Ann. Int. Conf. on Res. in Comput. Biol. (RECOMB '04), March 27-31, 2004, pp. 36-44. (PDF)
  8. N. Beerenwinkel, T. Lengauer, M. Däumer, R. Kaiser, H. Walter, K. Korn, D. Hoffmann, J. Selbig
    Methods for optimizing antiviral combination therapies
    Bioinformatics 2003, 19(1) (ISMB '03), i16-i25. (Paper at ISMB)
  9. T. Sing, N. Beerenwinkel, T. Lengauer, Learning Mixtures of Localized Rules by Maximizing the Area Under the
    ROC Curve
    , Int. Workshop on ROC Analysis in Artificial Intelligence (ROCAI 2004: 89-96)