一种基于代谢网络分析最小化基因组的方法及其在大肠杆菌中的应用

Genome minimization method based on metabolic network analysis and its application to Escherichia coli

摘要:

最小生命体的合成是合成生物学研究的重要方向。最小化基因组的同时而又不对细胞生长产生影响是代谢工程研究的一个重要目标。文中提出了一种从基因组尺度代谢网络模型出发，通过零通量反应删除及对非必需基因组合删除计算获得基因组最小化代谢网络模型的方法，利用该方法简化了大肠杆菌经典代谢网络模型iAF1260，由起始的1 260个基因简化得到了312个基因，而最优生物质生成速率保持不变。基因组最小化代谢网络模型预测了在细胞正常生长的前提下包含最少基因的代谢途径，为大肠杆菌获得最小基因组的湿实验设计提供了重要参考。

关键词:基因组尺度代谢网络，基因组最小化，组合算法，大肠杆菌

Abstract:

The minimum life is one of the most important research topics in synthetic biology. Minimizing a genome while at the same time maintaining an optimal growth of the cells is one of the important research objectives in metabolic engineering. Here we propose a genome minimization method based on genome scale metabolic network analysis. The metabolic network is minimized by first deleting the zero flux reactions from flux variability analysis, and then by repeatedly calculating the optimal growth rates after combinatorial deletion of the non-essential genes in the reduced network. We applied this method to the classic E. coli metabolic network model ---iAF1260 and successfully reduced the number of genes in the model from 1 260 to 312 while maintaining the optimal growth rate unaffected. We also analyzed the metabolic pathways in the network with the minimized number of genes. The results provide some guidance for the design of wet experiments to obtain an E. coli minimal genome.

Key words:genome-scale metabolic network, genome minimization, combination algorithm, Escherichia coli