Dr. Hulin Wu
Department of Biostatistics and Computational Biology
University of Rochester
School of Medicine and Dentistry
Title: "Statistical Inference for Differential Equation Models
with Biomedical Applications"
In this new era of high technologies, many new quantitative and computational sciences have evolved from various disciplines to become major tools for biomedical research. These include biostatistics, biomathematics, bioinformatics, biomedical informatics, computational biology, mathematical biology and theoretical biology, biophysics, bioengineering etc. This also brings a great opportunity for statisticians to integrate the various quantitative/computational techniques with statistical methodologies to support biomedical discoveries and research. At the University of Rochester, we have formed a new division of Biomedical Modeling and Informatics consisting of biostatisticians, biomathematicians, biophysicists, bioengineers and biocomputing scientists in the Department of Biostatistics and Computational Biology. Our Division, collaborating with biomedical investigators, is currently working on development of mathematical models, statistical methods and computer simulation systems and software for HIV infections, AIDS clinical studies, influenza infections and immune response to various pathogens. In this talk, I will discuss our experience of interactions and collaborations among biostatisticians, biomathematicians, biophysicists, bioengineers and biocomputing scientists as well as biomedical investigators. In particular, my talk will focus on statistical estimation methods for the parameters in differential equation models derived from biomedical research projects. I will review the three components: (1) differential equation models for HIV viral fitness experiments, AIDS clinical biomarker data, immune response to influenza A virus infections; (2) identifiability study of differential equation models; (3) statistical methods for parameter estimation for differential equation models; (4) application data analysis. Finally I will discuss more challenges and opportunities for statisticians in this research area.