The dynamics between virus populations and target cells in vitro and in vivo have been investigated extensively in the context of a variety of infections, both experimentally and with mathematical models. While this work has lead to many important insights into disease mechanisms and the efficacies of antiviral therapeutics, most of it has been based upon the assumption that individual cells are only infected with a single virus. In recent years, however, it has become clear that cells are frequently infected with multiple copies of the same virus in a variety of different infections. Such coinfection is likely to have a profound influence on viral dynamics and to influence the establishment of infection, viral spread, the course of disease and the response to antiviral drugs. The best experimental system to study coinfection is HIV, which is the focus of this proposal. We seek to provide a thorough and quantitative understanding of how virus replication kinetics and direct pathogenesis are influenced by coinfection, information which so far has been lacking. This will be done with HIV-1 in the context of 3 different target cell types in order to capture variation in viral replication and coinfection parameters. We subsequently aim to define how these replication kinetics translate into the dynamics of virus growth, as the virus spreads through its target cell population. This can only be achieved with the construction of mathematical models which capture the experimental data and make robust predictions regarding the dynamics of viral replication under different replication scenarios. Two fundamentally different modeling approaches will be considered, an ordinary differential equation model and an agent based model, and the relationship between them will be defined. This allows cross-validation between models and to overcome inherent weaknesses of individual modeling approaches. The model outcomes further define the experiments to be performed in order to test model predictions, which is a central component of our proposal. In addition to in vitro experiments, our analysis will be repeated using ex vivo lymphoid histoculture for comparison with cell monolayer monocultures, to provide higher clinical relevance of our studies.

Public Health Relevance

Understanding the dynamics of viral replication is critically important to our understanding of viral pathogenesis, immune responses and for the development of novel therapies. Mathematical and in silico models of viral dynamics are central to developing this understanding. When combined with experimentation mathematical analyses generate significant, and importantly, testable concepts and hypotheses concerning how viruses establish infection, replicate, persist, cause disease, and most importantly, how we may control or eliminate them.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Gezmu, Misrak
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University of California Irvine
Schools of Arts and Sciences
United States
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Wodarz, Dominik (2014) Modeling T cell responses to antigenic challenge. J Pharmacokinet Pharmacodyn 41:415-29
Wodarz, Dominik; Chan, Chi N; Trinité, Benjamin et al. (2014) On the laws of virus spread through cell populations. J Virol 88:13240-8
Wodarz, Dominik (2013) Computational modeling approaches to studying the dynamics of oncolytic viruses. Math Biosci Eng 10:939-57
Komarova, Natalia L; Wodarz, Dominik (2013) Virus dynamics in the presence of synaptic transmission. Math Biosci 242:161-71
Komarova, Natalia L; Anghelina, Daniela; Voznesensky, Igor et al. (2013) Relative contribution of free-virus and synaptic transmission to the spread of HIV-1 through target cell populations. Biol Lett 9:20121049
Trinite, Benjamin; Ohlson, Eric C; Voznesensky, Igor et al. (2013) An HIV-1 replication pathway utilizing reverse transcription products that fail to integrate. J Virol 87:12701-20
Komarova, Natalia L; Levy, David N; Wodarz, Dominik (2013) Synaptic transmission and the susceptibility of HIV infection to anti-viral drugs. Sci Rep 3:2103