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Award Abstract #1349939

CAREER: Virus Infection and Immune Responses: Modeling, Analysis, and Implications

NSF Org: DMS
Division Of Mathematical Sciences
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Initial Amendment Date: May 22, 2014
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Latest Amendment Date: August 2, 2015
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Award Number: 1349939
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Award Instrument: Continuing grant
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Program Manager: Mary Ann Horn
DMS Division Of Mathematical Sciences
MPS Direct For Mathematical & Physical Scien
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Start Date: September 1, 2014
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End Date: August 31, 2019 (Estimated)
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Awarded Amount to Date: $156,820.00
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Investigator(s): Libin Rong rong2@oakland.edu (Principal Investigator)
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Sponsor: Oakland University
530 Wilson Hall
Rochester, MI 48309-4401 (248)370-4116
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NSF Program(s): SERVICE ENTERPRISE SYSTEMS,
MATHEMATICAL BIOLOGY,
MSPA-INTERDISCIPLINARY,
Division Co-Funding: CAREER
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Program Reference Code(s): 1045, 8007, 9251
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Program Element Code(s): 1787, 7334, 7454, 8048

ABSTRACT

The objectives of this project are to develop and analyze mathematical models to quantitatively investigate virus infection and immune responses and to demonstrate their implications for antiviral treatment and vaccination. Mathematical models, validated and informed by experimental data, have made important contributions to the understanding of interactions between virus, cells, and immune responses. However, new data emerge and update our understanding of the biology underlying these processes. Sometimes the analysis of new data generates conflicting results. This project will develop and analyze new models, compare them with experimental data, explain potential discrepancies generated by new data, and address the implications to therapy and vaccine development. The first part of this project develops multi-scale models to study hepatitis C virus (HCV) dynamics in patients treated with new antiviral drugs. These models include both intracellular viral replication and extracellular cell infection. The second part of the project develops comprehensive immune models to elucidate the mechanisms in controlling HIV, a virus that causes AIDS in humans. The modeling predictions will be compared with experimental data to determine the relative roles of target cell availability and specific immune mechanisms in viral control.

This project will generate modeling, computational, and data analysis methods in quantitative studies of virus infection and immune responses. The multi-scale virus models will establish a theoretical framework for studying viral dynamics in patients treated with new drugs and will investigate the emergence of drug resistance. This can help to develop more effective combination therapy and improve cure rates. Such combination strategies have achieved a great success in the management of HIV infection. The comprehensive immune models developed in this project will address fundamental questions in immunology, e.g., how the immune system controls viral load and why some monkeys can live with an HIV-like virus without progressing to AIDS. The results may suggest effective strategies for the development of future virus vaccines and immune-based therapies. This interdisciplinary research will provide extensive training opportunities to students from diverse backgrounds at levels from high school to graduate school. This project includes extensive collaborations with experimentalists which will provide mathematics students with a broad range of training opportunities in the biomedical field. The interdisciplinary collaboration will also help to improve the dissemination of the project's results to a broad scientific audience.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Wang, S., Rong, L.. "Stochastic population switch may explain the latent reservoir stability and intermittent viral blips in HIV patients on suppressive therapy," Journal of Theoretical Biology, v.360, 2014, p. 137.

Pawelek, K.A., Oeldorf-Hirsch, A., Rong, L.. "Modeling the impact of Twitter on influenza epidemics," Mathematical Biosciences and Engineering, v.11, 2014, p. 1337.

Shen, M., Xiao, Y., Rong, L.. "Global stability of an infection-age structured HIV-1 model linking within-host and between-host dynamics," Mathematical Biosciences, v.263, 2015, p. 37.

 

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