Statistical methods for mining Chinese hamster ovary cell 'omics data: from differential expression to integrated multilevel analysis of the biological system

Abstract

Publication of Chinese hamster ovary (CHO) cell line and Chinese hamster genomes is accelerating efforts to increase the efficiency of biopharmaceutical manufacturing through greater understanding of CHO cell biology. It is hoped that this knowledge will lead to more predictable bioprocesses through the identification of biomarkers for culture monitoring and engineering of the CHO cell itself. If we are to translate the potential of the CHO systems biology era to industrial practice, the extraction of knowledge from complex genomic, proteomic, transcriptomic and metabolomic datasets will be critical. In this manuscript, we review the methods utilized to analyze expression profiling data and highlight the role of advanced statistics as we generate larger scale datasets and move toward integrated multi-omic analyses of the biological system.