vPhyloMM

vPhyloMM models viral evolutionary dynamics using phylogenetic and Markov statistical models to reconstruct mutational pathways and infer the emergence of drug-resistant genotypes under therapeutic pressure.

Key Features:

• Two-phase computational approach: Phase One models within-host evolutionary dynamics using longitudinal clonal sequence data to capture progressive accumulation and linkage of multiple mutations; Phase Two applies a Markov model to compute transition probabilities between viral genotypes.
• Phylogenetic and statistical integration: Integrates phylogenetic analysis with statistical models of viral evolution under drug pressure to analyze temporal and directional changes in genotypes.
• Probabilistic transition analysis: Computes likelihoods of specific mutational pathways via Markov transition probabilities to provide a probabilistic framework for evolutionary trajectories.
• Reconstruction of resistant genotypes: Reconstructs how drug-resistant genotypes develop over time from longitudinal sequence data and genotype linkage information.
• Application to clinical data: Applied to an HIV-1 Efavirenz clinical trial dataset to reveal directional changes in viral evolution over time.

Scientific Applications:

• Identification of evolutionary pathways: Distinguishes between fast and slow mutational pathways leading to drug resistance, informing rates of adaptation under therapy.
• Understanding emergence of drug resistance: Reconstructs mutational pathways to elucidate mechanisms behind therapy failure due to resistant mutations.
• Cross-pathogen applicability: Applicable to studying antiviral resistance and evolutionary dynamics in viruses beyond HIV-1, including hepatitis B virus (HBV) and hepatitis C virus (HCV).

Methodology:

Uses a two-phase computational framework combining phylogenetic analysis with Markov modeling on longitudinal clonal sequence data to model within-host viral evolution and compute genotype transition probabilities.