Journal Article

A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance

Patricia Buendia, Brice Cadwallader and Victor DeGruttola

in Bioinformatics

Volume 25, issue 19, pages 2522-2529
Published in print October 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp466
A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance

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Motivation: Modern HIV-1, hepatitis B virus and hepatitis C virus antiviral therapies have been successful at keeping viruses suppressed for prolonged periods of time, but therapy failures attributable to the emergence of drug resistant mutations continue to be a distressing reminder that no therapy can fully eradicate these viruses from their host organisms. To better understand the emergence of drug resistance, we combined phylogenetic and statistical models of viral evolution in a 2-phase computational approach that reconstructs mutational pathways of drug resistance.

Results: The first phase of the algorithm involved the modeling of the evolution of the virus within the human host environment. The inclusion of longitudinal clonal sequence data was a key aspect of the model due to the progressive fashion in which multiple mutations become linked in the same genome creating drug resistant genotypes. The second phase involved the development of a Markov model to calculate the transition probabilities between the different genotypes. The proposed method was applied to data from an HIV-1 Efavirenz clinical trial study. The obtained model revealed the direction of evolution over time with greater detail than previous models. Our results show that the mutational pathways facilitate the identification of fast versus slow evolutionary pathways to drug resistance.

Availability: Source code for the algorithm is publicly available at http://biorg.cis.fiu.edu/vPhyloMM/

Contact: pbuendia@miami.edu

Journal Article.  6661 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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