Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network

Joungmin Lee, Hongseok Yun, Adam M. Feist, Bernhard Palsson, Sang Yup Lee

Rannsóknarafurð: Framlag til fræðitímaritsGreinritrýni

138 Tilvitnanir (Scopus)

Útdráttur

To understand the metabolic characteristics of Clostridium acetobutylicum and to examine the potential for enhanced butanol production, we reconstructed the genome-scale metabolic network from its annotated genomic sequence and analyzed strategies to improve its butanol production. The generated reconstructed network consists of 502 reactions and 479 metabolites and was used as the basis for an in silico model that could compute metabolic and growth performance for comparison with fermentation data. The in silico model successfully predicted metabolic fluxes during the acidogenic phase using classical flux balance analysis. Nonlinear programming was used to predict metabolic fluxes during the solventogenic phase. In addition, essential genes were predicted via single gene deletion studies. This genome-scale in silico metabolic model of C. acetobutylicum should be useful for genome-wide metabolic analysis as well as strain development for improving production of biochemicals, including butanol.

Upprunalegt tungumálEnska
Síður (frá-til)849-862
Síðufjöldi14
FræðitímaritApplied Microbiology and Biotechnology
Bindi80
Númer tölublaðs5
DOI
ÚtgáfustaðaÚtgefið - okt. 2008

Athugasemd

Funding Information:
Acknowledgments We wish to express thanks to Peter Schumann and Jong-Soon Choi for the determination of cell wall and amino acid composition, respectively. Also, we are grateful to Jin Young Lee and Yu Sin Jang for discussion on cell cultivation and analytical procedure. Finally, we thank Tae Yong Kim and Hyun Uk Kim for discussion on flux balance analysis. This work was supported by the Korea–Australia Collaborative Research Project on the Development of Sucrose-Based Bioprocess Platform (N02071165) from the Korean Ministry of Knowledge Economy. Further support by LG Chem Chair Professorship and Microsoft are appreciated.

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