北京生物医学工程

___________基于基因芯片数据的代谢网络重构及其应用_________

Reconstruction of metabolic network based on microarray data and its application

作者：卫超郑浩然

单位：           中国科学技术大学计算机科学与技术学院(合肥230027)    

关键词：代谢网络重构;微阵列数据;基因芯片;干细胞

分类号：

出版年·卷·期（页码）：2013·32·3（255-260）

摘要：

目的基因组尺度的代谢网络重构提供了一种从系统层面深入观察生物体的方法，由此重构得到的网络是个体的“全基因组网络”。鉴于这种网络不能反映出不同环境条件下细胞内的动态变化过程，本文给出一种从基因芯片数据出发对生物体的实时“工作网络”进行重构的方法。方法通过对基因芯片数据使用dChip软件计算探针的P-A call后可得到基因的表达谱，然后在所整合的多源数据库的辅助下经由“基因表达谱→酶→反应→代谢网络”的过程进行“工作网络”的自动化重构。结果对来源于14种组织的182个干细胞样本进行工作网络重构的结果表明，所有干细胞之间具有较高的相似性，但不同组织来源的干细胞之间仍存在一定差异性。结论以基因芯片数据为数据源的代谢网络重构方法可有效用于生物体的“工作网络”重构。

Objective Genome-scale metabolic network reconstruction offers an easy way to get a deep insight into organism. The metabolic network obtained is the “whole genome-scale metabolic network” of an individual. Since this kind of network cannot reflect the intracellular dynamic changes in different environments，we introduce a new method to reconstruct organism’s real-time “working network” based on microarray data. Methods We first use dChip software to calculate the probe’s P-A call value to get the gene expression profile and then reconstruct the “working network” through the way “gene expression profile→enzyme→reaction→metabolic network” in the assist of multisource database. Results Reconstruction of metabolic networks for 182 samples of stem cells from 14 different tissues reflect the high similarity in all the stem cells，while there are still some differences in stem cells from different tissues. Conclusions The method proposed in this paper can reconstruct “working network” based on microarray data effectively.

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