Title: Stochastics and networks in genomic data
Description: This dissertation presents novel contributions that further our understanding of stochastics and networks in genomic data. Biological processes were once typecast as molecular machines that cranked out identical products uniformly. As our experimental techniques have improved, evidence has shown that biological processes are inherently stochastic. Additionally, our understanding of the basis of disease processes, in particular cancer, has also evolved significantly to include the recognition that it is not single genes, but rather complex networks of genes, gene products, and other small molecules that, when disregulated, ultimately lead to disease development and progression. In Chapter 2 we provide a simple model for transcript levels based on Poisson statistics and provide supporting experimental evidence for a set of nine genes. Our validation experiments confirm that these data fit our model. We also demonstrate that despite using data collected from a small number of cells we can still detect echoes of the stochastic effects that influence single cells. In so doing, we also present a general strategy called Mesoscopic Biology that opens up a potential new approach that can be used to assess the natural variability of processes occurring at the cellular level in biological systems.

In Chapter 3 we present two normalization methods for high-throughput quantitative real-time reverse transcriptase polymerase chain (qPCR) data. These methods are completely data-driven and therefore represent robust alternatives to existing methods which rely on a priori assumptions that housekeeping genes will perform reliably as appropriate control genes. Our methods directly and efficiently address the need to correct for technical variation in high-throughput qPCR data so that reliable measures of expression can be acquired. In Chapter 4 we propose and validate a hypothesis that explains the convergent behavior observed in gene expression state space trajectories that were originally described in Huang et al. (2005). This work provides a framework for understanding the role networks play in cell fate transitions.