Computational Inference Methods for HIV-1 Selective Sweeps Shaped by Early Cytotoxic T-Lymphocyte Response

TL;DR

This paper introduces a stochastic HIV infection model and two computational inference methods to analyze how HIV escapes immune responses, highlighting the importance of mutation interactions and selection in HIV dynamics.

Contribution

It presents novel Bayesian and hypothesis testing methods for inferring HIV escape pathways from complex mutation data.

Findings

Effective modeling of HIV mutation pathways

Demonstrated importance of multiple mutant interactions

Applied methods to real patient datasets

Abstract

In this work we develop a stochastic model of acute HIV infection, based on the well-known standard model, that allows us to simulate the complex mutation pathways of HIV escape from multiple CTL responses. Under this model, we describe two computational inference methods. In one, we use a Bayesian approach to construct posteriors for the parameters of our model. In the second, we use hypothesis testing to determine the fit of the model to data. The methods are applied to two CHAVI datasets, demonstrating the importance of accounting for the interaction of multiple mutant variants and multi-directional selection in analysing HIV dynamics under CTL response.