北京生物醫(yī)學(xué)工程

融合基因芯片數(shù)據(jù)上下文特定網(wǎng)絡(luò)的重構(gòu)

Context-specific network reconstruction of integrating microarray data

作者：陳孝旭鄭浩然

單位：                      中國科學(xué)技術(shù)大學(xué)計算機科學(xué)與技術(shù)學(xué)院(合肥230027)        

關(guān)鍵詞：基因芯片；上下文特定網(wǎng)絡(luò)；重構(gòu)；代謝系統(tǒng)；組織特異性

分類號：

出版年·卷·期（頁碼）：2014·33·2（125-130）

摘要：

目的在特定類型細胞或特定環(huán)境條件下，已有的全基因組代謝網(wǎng)絡(luò)中的部分反應(yīng)并不參與到實際的代謝過程中。為反映特定條件下生物體代謝系統(tǒng)的工作狀態(tài)，本文提出一種重構(gòu)上下文特定網(wǎng)絡(luò)的新方法，用來構(gòu)建特定條件下的代謝網(wǎng)絡(luò)。方法首先根據(jù)基因芯片數(shù)據(jù)中蘊含的PA Calls信息，由基因-酶-反應(yīng)之間的調(diào)控關(guān)系，計算出在表達層面上為“Absent”狀態(tài)的反應(yīng)集合。然后使用一個混合整數(shù)規(guī)劃模型，從全基因代謝網(wǎng)絡(luò)中刪除表達為“Absent”狀態(tài)的反應(yīng)及其關(guān)聯(lián)反應(yīng)，在滿足計量平衡等約束條件下，尋找與表達層面吻合最優(yōu)的網(wǎng)絡(luò)，即為特定條件下的上下文特定網(wǎng)絡(luò)。結(jié)果重構(gòu)了肝臟和心臟兩種組織的上下文特定網(wǎng)絡(luò)。實驗結(jié)果表明，兩種組織的上下文特定網(wǎng)絡(luò)存在很大差異性，尤其在某些具有組織特異性的代謝功能上，如膽汁酸合成等。結(jié)論融合基因芯片數(shù)據(jù)信息的上下文特定網(wǎng)絡(luò)重構(gòu)方法，能夠有效構(gòu)建反映特定條件下生物體代謝系統(tǒng)狀態(tài)的上下文特定網(wǎng)絡(luò)。

Objective Some reactions in a genome-scale network are inactive in a specific cell type or under a particularly environmental condition.In order to characterize the current state of metabolic system of organism,we propose a new method to reconstruct the context-specific network.Methods The probeset detection information(PA Calls) of microarray data was adopted to infer ‘Absent’ reactions by gene-protein-reaction relationships and a mixed integer linear programming model was applied to remove ‘Absent’ reactions and cascaded reactions from the genome-scale network directly.The context-specific network thus generated satisfied stoichiometric constraints and directional constraints.Results Applying this method to human metabolism,we reconstructed the context-specific networks of normal heart and liver.The results showed that the context-specific networks of the two tissues were significantly different,especially on some tissue-specific metabolic functions such as bile acid biosynthesis.Conclusions By integrating microarray data,this method reconstructed context-specific network that reflected the state of metabolic system under a particular condition.

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