Learning multiple evolutionary pathways from cross-sectional data.
We introduce a mixture model of trees to describe evolutionary processes that are characterized by the ordered accumulation of permanent genetic changes. The basic building block of the model is a directed weighted tree that generates a probability distribution on the set of all patterns of genetic events. We present an EM-like algorithm for learning a mixture model of K trees and show how to determine K with a maximum likelihood approach. As a case study, we consider the accumulation of mutations in the HIV-1 reverse transcriptase that are associated with drug resistance. The fitted model is statistically validated as a density estimator, and the stability of the model topology is analyzed. We obtain a generative probabilistic model for the development of drug resistance in HIV that agrees with biological knowledge. Further applications and extensions of the model are discussed.
Beerenwinkel
Niko
N
Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany.
Rahnenführer
Jörg
J
Däumer
Martin
M
Hoffmann
Daniel
D
Kaiser
Rolf
R
Selbig
Joachim
J
Lengauer
Thomas
T
D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology*; D005075:Biological Evolution; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D024882:Drug Resistance, Viral / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics; D054303:HIV Reverse Transcriptase / Q000037:antagonists & inhibitors; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics*; D006801:Humans; D007858:Learning; D016013:Likelihood Functions; D008957:Models, Genetic; D016296:Mutagenesis / Q000235:genetics; D009154:Mutation; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted
D019380:Anti-HIV Agents; D054303:HIV Reverse Transcriptase