@article{BeerenwinkelSingLengaueretal.2005,
  author    = {Beerenwinkel, Niko and Sing, Tobias and Lengauer, Thomas and Rahnenfuhrer, Joerg and Roomp, Kirsten and Savenkov, Igor and Fischer, Roman and Hoffmann, Daniel and Selbig, Joachim and Korn, Klaus and Walter, Hauke and Berg, Thomas and Braun, Patrick and Faetkenheuer, Gerd and Oette, Mark and Rockstroh, Juergen and Kupfer, Bernd and Kaiser, Rolf and Daeumer, Martin},
  title     = {Computational methods for the design of effective therapies against drug resistant HIV strains},
  year      = {2005},
  abstract  = {The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data},
  language  = {en}
}
@article{GopalakrishnanMontazeriMenzetal.2014,
  author    = {Gopalakrishnan, Sathej and Montazeri, Hesam and Menz, Stephan and Beerenwinkel, Niko and Huisinga, Wilhelm},
  title     = {Estimating HIV-1 fitness characteristics from cross-sectional genotype data},
  series = {PLoS Computational Biology : a new community journal},
  volume    = {10},
  journal   = {PLoS Computational Biology : a new community journal},
  number    = {11},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1553-734X},
  doi       = {10.1371/journal.pcbi.1003886},
  pages     = {14},
  year      = {2014},
  abstract  = {Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.},
  language  = {en}
}