Date of Award
Doctor of Philosophy
Bioinformatics and Computational Biology
Kenneth J. Koehler
Function divergence after gene duplication has been considered to be an important mechanism for the evolution of new functions. Although gene expression profiles have been treated as an important indicator of gene function, large scale gene expression analysis has mostly focused on current relationships among genes, instead of their evolutionary relationships. By putting expression analysis into the framework of evolution, we make inferences about expression divergence after gene duplication. Using a Brownian-based model, gene expression of ancestral states can be inferred using the posterior distribution of ancestral gene expression profiles given observed current gene expression profiles. Since expression profiles measure the transcriptional activity of genes, expression divergence can be used to infer function divergence. Consequently, we put the analysis of gene expression profiles into the context of gene evolution and some strategies are given for distance-based phylogenetic analysis of microarray data. Finally, we examine the correlation between regulatory motif structure and gene expression profile in yeast. Our results suggest that duplicate genes tend to be co-expressed but the correlation between motif content and expression similarity is generally weak, only about 2--3% of expression variation can be explained by the motif divergence. Our observations suggest that, in addition to the (cis)-regulatory motif structure in the upstream region of the gene, multiple trans acting factors in the gene network may significantly influence the pattern of gene expression.
Digital Repository @ Iowa State University, http://lib.dr.iastate.edu
Zhang, Zhongqi, "Statistical analysis of gene expression profiles" (2004). Retrospective Theses and Dissertations. 1135.