Lee Joungmin, Yun Hongseok, Feist Adam M, Palsson Bernhard Ø, Lee Sang Yup
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.
Appl Microbiol Biotechnol. 2008 Oct;80(5):849-62. doi: 10.1007/s00253-008-1654-4. Epub 2008 Aug 29.
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.
为了解丙酮丁醇梭菌的代谢特性并研究提高丁醇产量的潜力，我们根据其注释的基因组序列重建了基因组规模的代谢网络，并分析了提高其丁醇产量的策略。生成的重建网络由502个反应和479种代谢物组成，并用作计算机模型的基础，该模型可以计算代谢和生长性能以与发酵数据进行比较。该计算机模型使用经典通量平衡分析成功预测了产酸阶段的代谢通量。非线性规划用于预测溶剂生成阶段的代谢通量。此外，通过单基因缺失研究预测了必需基因。丙酮丁醇梭菌的这种基因组规模的计算机代谢模型应有助于全基因组代谢分析以及用于改善包括丁醇在内的生化物质生产的菌株开发。
