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Open Access Highly Accessed Correspondence

Systems mapping of HIV-1 infection

Wei Hou12, Yihan Sui1, Zhong Wang3, Yaqun Wang3, Ningtao Wang3, Jingyuan Liu3, Yao Li4, Maureen Goodenow5, Li Yin5, Zuoheng Wang6 and Rongling Wu13*

Author affiliations

1 Center for Computational Biology, Beijing Forestry University, Beijing, 100081, China

2 Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA

3 Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, 17033, USA

4 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA

5 Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, 32610, USA

6 Division of Biostatistics, Yale University, New Haven, CT, 06510, USA

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Citation and License

BMC Genetics 2012, 13:91  doi:10.1186/1471-2156-13-91

Published: 23 October 2012


Mathematical models of viral dynamics in vivo provide incredible insights into the mechanisms for the nonlinear interaction between virus and host cell populations, the dynamics of viral drug resistance, and the way to eliminate virus infection from individual patients by drug treatment. The integration of these mathematical models with high-throughput genetic and genomic data within a statistical framework will raise a hope for effective treatment of infections with HIV virus through developing potent antiviral drugs based on individual patients’ genetic makeup. In this opinion article, we will show a conceptual model for mapping and dictating a comprehensive picture of genetic control mechanisms for viral dynamics through incorporating a group of differential equations that quantify the emergent properties of a system.