Multivariate Analysis and Reverse Engineering of Signal Transduction Pathways

This thesis presents mathematical and computational approaches to studying signal transduction pathways. The biology of cellular signalling is introduced, highlighting the importance of signalling pathways in regulating the processes of cell division and proliferation. A brief history of mathematical approaches to such systems is presented. Two multivariate analysis methods, principal component analysis and clustering, are introduced and applied to both gene expression and simulated protein concentration data. Several recent reverse engineering methods that have been used to study genetic networks are introduced. Finally, a reverse engineering method intended to elucidate the structure of genetic networks is adapted to the study of signal transduction systems. ii Contents Abstract i