Gene Expression Models of Signaling Pathways

Abstract

Aberrant pathway activation is a hallmark of a range of diseases, from atherosclerosis to diabetes to cancer. The ability to easily measure the compendium of activated pathways in a biological sample would greatly impact the study of these diseases. To do so, methods have been developed recently that leverage the gene expression profile of a cell. While these profiles provide a quantitative mea- sure of the expression levels of every gene in the genome, they are also a reflection and amalgamation of all the processes, many of which require the concerted activity of a group of genes, that are activated in a biological sample. To interpret these profiles, methods have been developed over the last decade that can quantify the activation of individual processes. In short, each process is modeled as a gene expression signature, which is comprised of a set of genes whose expression levels are indicative of activation of the process. To score the activation of that process in a cell, computational algorithms have been developed that can compare the sig- nature against the gene expression profile of the cell. This chapter describes the development of signatures and signature databases, as well as computational approaches to predict pathway activation in gene expression profiles.