Dynamic model of HIV infection with immune system response of T-lymphocytes, B-cells and dendritic cells: a review

TL;DR

This paper reviews a dynamic, compartmental ODE model of HIV infection that incorporates immune responses of T-lymphocytes, B-cells, and dendritic cells, and demonstrates its potential for simulating infection progression and therapy effects.

Contribution

It introduces a detailed compartmental ODE model of HIV infection including immune cell states and cytokine interactions, implemented in an accessible simulation tool.

Findings

HIV concentration drops below 10^-10 virus/μL after 2 years

Quiescent lymphocytes reach a lower equilibrium, acting as a virus reservoir

Model can simulate effects of antiviral therapies

Abstract

A dynamic model of non-lineal time-dependent ordinary differential equations (ODE) has been applied to the interactions of a HIV infection with the immune system cells. This model has been simplified into two compartments: lymph node and peripheral blood. The model includes CD4 T-lymphocytes in several states (quiescent Q, naive N and activated T), cytotoxic CD8 T-cells, B-cells and dendritic cells. Cytokines and immunoglobulins specific for each antigen (i.e. gp41 or p24) have been also included in the model, modelling the atraction effect of CD4 T-cells to the infected area and the reduction of virus concentration by immunoglobulins. HIV virus infection of CD4 T-lymphocytes is modelled in several stages: before fusion as HIV-attached (H) and after fusion as non-permissive / abortively infected (M), and permissive / latently infected (L) and permissive / actively infected (I). These equations have been implemented in a C++/Python interface application, called Immune System app, which runs Open Modelica software to solve the ODE system through a 4th order Runge-Kutta numerical approximation. Results of the simulation show that although HIV virus concentration in both compartments is lower than $10^{-10}$ virus/$\mu L$ after t=2 years, quiescent lymphocytes reach an equilibrium with a concentration lower than the initial conditions, due to the latency state, which serves as a reservoir in time of virus production. As a conclusion, this model can provide reliable results in other conditions, such as antiviral therapies.