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"

Abstract:
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.