A comprehensive stochastic computational model of HIV infection from DNA integration to viral burst

TL;DR

This paper presents a detailed stochastic computational model of HIV infection, capturing the complex, probabilistic steps from DNA integration to viral burst, to better understand infection variability and phenotypes.

Contribution

It introduces a comprehensive stochastic model of HIV lifecycle processes, integrating diverse molecular interactions and rare events to analyze infection dynamics.

Findings

Model captures stochastic variability in HIV infection stages

Dissects impact of rare events on infection outcomes

Provides insights into infection heterogeneity

Abstract

Multiple mechanisms in the HIV lifecycle play a role in its ability to evade therapy and become a chronic, difficult-to-treat infection. Within its major cellular target, the activated T cell, many steps occur between viral entry and viral burst, including reverse transcription of viral RNA, integration of the viral DNA in the host genome, viral transcription, splicing, translation, host and viral regulation, and viral packaging. These steps exploit complex networks of macromolecular interactions that exhibit various forms of stochastic behavior. While each of the steps of HIV infection have been individually studied extensively, the combinatorial contribution of rare events in each of the steps, and how series of these rare events lead to different infection phenotypes, are not well understood. The complexity of these processes render experimental study challenging. Therefore, we have built a comprehensive computational model of this large system, by collating the community's knowledge of the infection process. It is a stochastic model where rates of different events in the system are represented as probabilities of the event occurring in a timestep of the simulation. This model enables an understanding of the noise and variation in the system. The model also facilitates a dissected understanding of each small part of the large complex system, and its impact on the overall system dynamics.