Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network.
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.
Lee
Joungmin
J
Department of Chemical & Biomolecular Engineering (BK21 Program), BioProcess Engineering Research Center, Center for Systems and Synthetic Biotechnology and Institute for the BioCentury, KAIST, Daejeon, Republic of Korea.
Yun
Hongseok
H
Feist
Adam M
AM
Palsson
Bernhard Ø
BØ
Lee
Sang Yup
SY