Please use this identifier to cite or link to this item: https://doi.org/10.1016/B978-0-444-63234-0.50031-2
Title: Genome-scale metabolic network reconstruction and in silico analysis of Methanococcus maripaludis S2
Authors: Goyal, N.
Widiastuti, H.
Karimi, I.A. 
Zhou Zhi, G.
Keywords: Genome-scale metabolic model
GPR association
Methanococcus maripaludis S2
Methanogenesis
Methanogenic bacteria
Issue Date: 2013
Source: Goyal, N.,Widiastuti, H.,Karimi, I.A.,Zhou Zhi, G. (2013). Genome-scale metabolic network reconstruction and in silico analysis of Methanococcus maripaludis S2. Computer Aided Chemical Engineering 32 : 181-186. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-444-63234-0.50031-2
Abstract: Methane (Natural gas) is an important energy source for heating and electricity. Its production by methanogenic bacteria using different carbon substrates is widely known in nature. Methanococcus maripaludis S2, a methanogen is an excellent laboratory strain for which robust genetic tools are available, but a systems biology model to complement these tools is absent in the literature. To understand methanogenesis and to maximize the yield of methane by using carbon dioxide captured from a flue gas, a constraint-based genome-scale metabolic model of fully sequenced hydrogenotrophic methanogenic strain M. maripaludis S2 has been developed. This model serves to predict the effects of any perturbations and/or gene knockouts on different metabolic processes, which can guide or expedite experimental efforts. Genome-scale metabolic model of M. maripaludis S2 was reconstructed using pathway databases such as KEGG, METACYC and SEED to identify gene-protein-reaction (GPR) associations. Gap filling was done by reviewing published experimental literature and using basic biological understanding of the intracellular functions in methanogenic species. The model was validated using biomass growth data, and metabolic phenotypes predicted by our model are consistent with experimental observations. © 2013 Elsevier B.V.
Source Title: Computer Aided Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/63972
ISSN: 15707946
DOI: 10.1016/B978-0-444-63234-0.50031-2
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