Data mining methodologies for gene expression analysis: Application to strain improvement

Abstract

The advent of microarray technology has created a deluge of gene expression data by virtue of its ability to measure the expression levels of thousands of genes simultaneously. This data, when suitably mined, can provide understanding of the physiological state of cells and thus enable the identification of genetic targets for strain improvement. In this thesis, a data-driven framework is proposed for identifying genetic targets for strain improvement. The framework contains different methods for identifying differentially expressed genes, clustering of genes, cluster validation, and integration of complementary datasets to identify genetic targets for strain improvement. Novel methods based on multivariate statistics are proposed for each step of the proposed framework. An integrative case study involving improvement of Escherichia coli K12 strain producing recombinant protein by identifying genetic targets is used to illustrate the proposed framework.