Modeling Immune Response to Influenza A Infection by Integrating Quantitative/Computational Technologies

Hulin Wu
Department of Biostatistics and Computational Biology
Division of Biomedical Modeling and Informatics
Center for Biodefense Immune Modeling
University of Rochester School of Medicine and Dentistry

n this new era of advanced technologies, improved understanding of biological processes can be more effectively gained by the use of highly developed mathematical/ computational approaches. Such work requires multidisciplinary collaboration between biological/biomedical researchers and quantitative/computational scientists. Building and managing a multidisciplinary team presents a number of unique challenges related to overcoming barriers of integrating disparate knowledge, technologies and approaches. To meet this challenge, in 2004 at the University of Rochester, we formed the Division of Biomedical Modeling and Informatics, comprised of biomathematicians, biostatisticians, biophysicists, bioengineers and biocomputing scientists within the Department of Biostatistics & Computational Biology. Our Division scientists, collaborating with biologists and biomedical investigators, are currently working on several projects to develop mathematical models, statistical methods, computer simulation systems, and application software for HIV infection dynamics, AIDS clinical study design and analysis, influenza infection and immune response to other pathogens. In particular, as one of the four centers for Biodefense Immune Modeling sponsored by NIAID/NIH, our research focuses on development of mathematical models and computer simulations for influenza infection and other potential biodefense pathogens. In this talk, I will share our experience on how to overcome collaboration and communication barriers among the diverse disciplines of quantitative scientists and biomedical experimentalists to efficiently partner to achieve our goals: 1) develop mathematical models for immune response to influenza A virus infections; 2) develop statistical methods for parameter estimation and model validation; and 3) develop user-friendly computer simulation and estimation software tools to address fundamental biological questions.

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