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  • Oral Presentation
  • Open Access

Enhanced production of recombinant proteins by systems biotechnological approaches

  • 1
Microbial Cell Factories20065 (Suppl 1) :S2

  • Published:


  • Recombinant Protein
  • Cellular Network
  • Metabolic Model
  • Omics Data
  • Strain Development

The recent development of high-throughput experimental techniques has resulted in rapid accumulation of a wide range of biological data and information at different levels: genome, transcriptome, proteome, metabolome, and fluxome data. This technology-driven discovery science is now allowing the identification of unprecedentedly large numbers of individual components and molecules of a biological system, thus providing a foundation for a profound understanding of biological processes. It is currently true that the identification of these components and molecules alone is not sufficient to characterize their functions and interactions in a global scale. However, even not truly global scale information and knowledge newly found from such omics studies can be successfully employed for strain development. Also, it is increasingly accepted that in silico analysis of the cellular network is promising to discover a knowledge map for deciphering the functions and characteristics of the biological systems. In silico genome-scale metabolic models can be used to understand the status of the complex cellular system and investigate inherent cellular properties. As such, hypothesis-driven in silico experiments can be invaluable to improve our ability to predict the cellular behavior of microorganisms under various genetic and environmental conditions. In this lecture, I will show some successful examples of "local engineering of cellular components based on global omics data" toward enhanced production of recombinant proteins. Also, systems biotechnological research cycle that allows efficient strain development by combining in silico and wet experiments will be discussed.



This work was supported by the Korean Systems Biology Program from the MOST, LG Chem Chair Professorship, IBM, Microsoft and BK21 project.

Authors’ Affiliations

Metabolic Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Department of BioSystems, BioProcess Engineering Research Center, and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea


© Lee; licensee BioMed Central Ltd. 2006

This article is published under license to BioMed Central Ltd.