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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