基因组规模代谢网络模型自动化修正

Auto-Refinement of Genome-Scale Metabolic Network Model

摘要:

基于KEGG在线数据库以及6个蛋白质区间预测数据库，对基因组规模代谢网络模型进行了自动化修正。作者提出了蛋白质区间预测结果的权重打分机制，同时利用图像处理算法确定可信度高的特异性反应。上述修正的研究均在Spathaspora passalidarum NRRL Y-27907基因组规模代谢网络精炼过程中得到运用实施，对于提高模型构建效率意义重大。

Abstract:

KEGGonline database and six subcellular prediction databaseshave been studied for the process of auto-refinement. The weighted scoring mechanism was proposed to analyze the results of subcellular prediction databases，using image processing algorithm to determine high credibility specific reaction.As an illustration example，all of the automatic methods were implemented in the process of genome-scale metabolic network refinement of Spathasporapassalidarum NRRLY- 27907，whichconfirmed that these methods can improve the efficiency of model reconstruction.