高等学校化学学报

高等学校化学学报

• 研究简报 • 上一篇    下一篇

基元通量模式预测酵母生长现象

蒋达1, 王永华2, 李燕1, 张述伟1, 杨胜利2, 杨凌2   

    1. 大连理工大学化学工程系, 大连 116012;
    2. 中国科学院大连化学物理研究所, 药用资源开发研究组, 大连 116023
  • 收稿日期:2005-07-26 修回日期:1900-01-01 出版日期:2006-09-10 发布日期:2006-09-10
  • 通讯作者: 杨凌

Prediction of Saccharomyces Cerevisiae Growth Phenotypes Based on Elementary Flux Mode Analysis

JIANG Da1, WANG Yong-Hua2, LI Yan1, ZHANG Shu-Wei1, YANG Sheng-Li2, YANG Ling2   

    1. Department of Chemical Engineering, Dalian University of Technology, Dalian 116012, China;
    2. Lab of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
  • Received:2005-07-26 Revised:1900-01-01 Online:2006-09-10 Published:2006-09-10
  • Contact: YANG Ling

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被引次数

3

摘要： 本文通过EFM预测了基因突变后的酵母细胞生长现象, 模拟预测结果和实验结果吻合很好; 与FBA方法得到的模拟结果相比较, EFM方法能更好地把基因突变和其表型(生长)联系起来.

关键词: 预测, 基元通量模式, 啤酒酵母, 生长现象

Abstract: The purpose of this work is to illustrate the relationship between genotype and phenotype in the complex cellular network of saccharomyces cerevisiae. As a structureoriented method, using elementary flux mode(EFM) analysis can obtain its popularity in analysis of the robustness of the central metabolism, as well as network function of some organisms. However, this method has not been widely used for modeling gene deletion phenotype. By enumerating all the metabolic pathways, the EFM analysis presented herein can be used to identify the functional features and predict the growth phenotype of the S. cerevisiae. In comparison with the flux balance analysis(FBA), the performance of EFM analysis was superior to FBA in prediction of gene deletion phenotype. EFM analysis is demonstrated to be an effective tool for bridging the gap between metabolic network and growth phenotype.

Key words: Prediction, Elementary flux mode, Saccharomyces cerevisiae, Growth phenotype

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