Since the completion of the rst genome sequencing of a microorganism [1], hundreds of genomes have been sequenced and archived in National Center for Biotechnology Information (NCBI) and many databases. e organisms are a complex system and their genomes are immense, and thus powerful technologies are being developed to meet the demands in analysis of thousands of genes and their products and functions. Compared to traditional methodologies which were typically based on one target in one experiment, recently developed omics technologies, including transcriptomics, proteomics, metabolomics, and physiomics are allowing us to generate large amounts of these data. Accessibility to these omics data is providing a foundation for in-depth understanding of living organisms. Genome-wide technologies were based on the two-dimensional experimental methods which were initially developed by O’Farrell et al. [2] for proteomics, and phenotypic and DNA microarrays by Bochner et al. [3] and Fodor et al. [4], respectively. anks to these methodologies, experiments are no longer limited to one by one type, and can be performed for hundreds to tens of thousands of targets and conditions simultaneously. e eort to miniaturize the experimental scales without the loss of validity and reproducibility

is also remarkable, and has contributed in widespread use of these technologies. Nowadays, these technologies have successfully been applied to biological research, and are providing new information on global cellular physiology and regulations of the cells (Figure 13.1). Together with computational analyses, these high-throughput omics technologies gave birth of systems biology [5]. In recent years, systems biotechnology [6], which allows development of improved strains and bioprocesses by taking systemslevel analytical approaches, also appeared. In this chapter, we review the advances in DNA microarray, phenotypic microarray and proteomics with their applications. Also, we describe the importance of combined analysis of these omics data within a systems biology framework for successful metabolic engineering.