, 50:121 | Cite as

Toward genomic cell culture engineering

  • Katie F. Wlaschin
  • Gargi Seth
  • Wei-Shou HuEmail author
Original Paper


Genomic and proteomic based global gene expression profiling has altered the landscape of biological research in the past few years. Its potential impact on cell culture bioprocessing has only begun to emanate, partly due to the lack of genomic sequence information for the most widely used industrial cells, Chinese hamster ovary (CHO) cells. Transcriptome and proteome profiling work for species lacking extensive genomic resources must rely on information for other related species or on data obtained from expressed sequence tag (EST) sequencing projects, for which burgeoning efforts have only recently begun. This article discusses the aspects of EST sequencing in those industrially important, genomic resources-poor cell lines, articulates some of the unique features in employing microarray in the study of cultured cells, and highlights the infrastructural needs in establishing a platform for genomics based cell culture research. Recent experience has revealed that generally, most changes in culture conditions only elicit a moderate level of alteration in gene expression. Nevertheless, by broadening the conventional scope of microarray analysis to consider estimated levels of transcript abundance, much physiological insight can be gained. Examples of the application of microarray in cell culture are discussed, and the utility of pattern identification and process diagnosis are highlighted. As genomic resources continue to expand, the power of genomic tools in cell culture processing research will be amply evident. The key to harnessing the immense benefit of these genomic resources resides in the development of physiological understanding from their application.


cDNA library Data analysis Mammalian cell culture Microarray Proteome Transcriptome 



The support from Pfizer, Inc. and Bayer Healthcare for the cell culture research work in W-S. Hu’s laboratory are gratefully acknowledged. KFW was supported by the NIH Biotechnology Training Grant (GM08347). The bioinformatic support was provided by The Minnesota Supercomputing Institute.


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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Department of Chemical Engineering and Materials ScienceUniversity of MinnesotaMinneapolisUSA

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