北京生物醫(yī)學(xué)工程

___________基于基因芯片數(shù)據(jù)的代謝網(wǎng)絡(luò)重構(gòu)及其應(yīng)用_________

Reconstruction of metabolic network based on microarray data and its application

作者：衛(wèi)超鄭浩然

單位：           中國(guó)科學(xué)技術(shù)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院(合肥230027)    

關(guān)鍵詞：代謝網(wǎng)絡(luò)重構(gòu);微陣列數(shù)據(jù);基因芯片;干細(xì)胞

分類號(hào)：

出版年·卷·期（頁(yè)碼）：2013·32·3（255-260）

摘要：

目的基因組尺度的代謝網(wǎng)絡(luò)重構(gòu)提供了一種從系統(tǒng)層面深入觀察生物體的方法，由此重構(gòu)得到的網(wǎng)絡(luò)是個(gè)體的“全基因組網(wǎng)絡(luò)”。鑒于這種網(wǎng)絡(luò)不能反映出不同環(huán)境條件下細(xì)胞內(nèi)的動(dòng)態(tài)變化過(guò)程，本文給出一種從基因芯片數(shù)據(jù)出發(fā)對(duì)生物體的實(shí)時(shí)“工作網(wǎng)絡(luò)”進(jìn)行重構(gòu)的方法。方法通過(guò)對(duì)基因芯片數(shù)據(jù)使用dChip軟件計(jì)算探針的P-A call后可得到基因的表達(dá)譜，然后在所整合的多源數(shù)據(jù)庫(kù)的輔助下經(jīng)由“基因表達(dá)譜→酶→反應(yīng)→代謝網(wǎng)絡(luò)”的過(guò)程進(jìn)行“工作網(wǎng)絡(luò)”的自動(dòng)化重構(gòu)。結(jié)果對(duì)來(lái)源于14種組織的182個(gè)干細(xì)胞樣本進(jìn)行工作網(wǎng)絡(luò)重構(gòu)的結(jié)果表明，所有干細(xì)胞之間具有較高的相似性，但不同組織來(lái)源的干細(xì)胞之間仍存在一定差異性。結(jié)論以基因芯片數(shù)據(jù)為數(shù)據(jù)源的代謝網(wǎng)絡(luò)重構(gòu)方法可有效用于生物體的“工作網(wǎng)絡(luò)”重構(gòu)。

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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