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Title: Genome-scale modeling and in silico analysis of mouse cell metabolic network
Authors: Selvarasu, S.
Karimi, I.A. 
Ghim, G.-H.
Lee, D.-Y. 
Issue Date: 2009
Citation: Selvarasu, S., Karimi, I.A., Ghim, G.-H., Lee, D.-Y. (2009). Genome-scale modeling and in silico analysis of mouse cell metabolic network. Molecular BioSystems 6 (1) : 152-161. ScholarBank@NUS Repository.
Abstract: Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting.
Source Title: Molecular BioSystems
ISSN: 1742206X
DOI: 10.1039/b912865d
Appears in Collections:Staff Publications

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