Agent-based simulation of host-virus interaction: Application to Epstein-Barr virus

Reinhard Laubenbacher (Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University)

(February 21, 2006 10:30 AM - 11:30 AM)

Agent-based simulation of host-virus interaction: Application to Epstein-Barr virus


The focus of this talk is a 3-dimensional, stochastic, rule-based model of immune response to viral pathogens. In its present form, the PathSim model focuses on Epstein-Barr virus (EBV) infection of the Waldeyer's tonsilar ring. EBV is a ubiquitous and sometimes pathogenic human herpesvirus that establishes a life-long infection in B cells despite an aggressive immune response. EBV is an ideal model system for studying persistent infection because: 1) sites of infection are accessible; 2) levels of infected cells, viral shedding, anti-viral antibody, and T cell responses can be measured in parallel; and 3) infection can be studied from an extreme state of perturbation (mononucleosis) into persistence. Mechanisms underlying the establishment and maintenance of persistence are complex and, given the lack of animal models, we seek to better understand them using modeling strategies.A multi-scale anatomical viewer helps to visualize infection model dynamics. Preliminary results qualitatively match clinical data. Furthermore, simulations reveal that persistence appears very dependent on access to the circulation of latently infected B cells; when access to the circulation is blocked,the infection is cleared. One factor that dramatically affects the course of infection is the percentage of latently infected cells triggered to begin viral replication upon returning from the blood.

Rule-based models are well-suited for the simulation of dynamics resulting from a large number of spatially distributed, interacting entities, such as virions and immune cells. One of their shortcomings, however, is the relative lack of mathematical tools available to analyze model dynamics and, in particular, to formulate and solve optimal control problems. We will describe an approach to develop a mathematical foundation for PathSim, which allows the development of control theoretic methods.