Abstract
The study of the cell cycle is important in order to aid in our understanding of the basic mechanisms of life, yet progress has been slow due to the complexity of the process and our lack of ability to study it at high resolution. Recent advances in microarray technology have enabled scientists to study the gene expression at the genome-scale with a manageable cost, and there has been an increasing effort to identify cell-cycle-regulated genes. In this chapter, we discuss the analysis of cell cycle gene expression data, focusing on modelbased Bayesian approaches. The majority of the models we describe can be fitted using freely available software.
Introduction
Cells reproduce by duplicating their contents and then dividing into two. The repetition of this process is called the cell cycle, and is the fundamental means by which all living creatures propagate. On the other hand, abnormal cell divisions are responsible for many diseases, most notably cancer. Therefore, studying cell cycle control mechanisms and the factors essential for the process is important in order to aid in our understanding of cell replication, malignancy, and reproductive diseases that are associated with genomic instability and abnormal cell divisions.
For decades, biologists have been studying the cell cycle, using the model organism budding yeast Saccharomyces cerevisiae. This focus on budding yeast is due to the fact that it exists as a free living, single cell, which has the same general architecture and control pathways as the cells of its highly complex, multicellular relatives (e.g., humans).