Statistical Methods for High Dimensional ODE Models with Applications to Dynamic Gene Regulatory Network Identification

Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and Computational Biology, 2011.Gene regulation is a complicated systematic process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological process in a systematic way. However, the construction of such a complete dynamic network system is challenging mainly due to the computational constraint of a high-dimensional network system. In this dissertation we propose to use a set of ordinary differential equations (ODE), coupled with a series of cutting-edge statistical methods and techniques, to model the dynamic gene regulatory network (GRN). The ODE models allow us to quantify both positive and negative regulations as well as feedback effects of one set of genes on the dynamic expression changes of other genes