利用禁忌搜尋法從基因體與外基因體資料與蛋白質網路來推論神經生成機制

Inferring Neurogenic Mechanisms from Genetic and Epigenetic Data Based on Protein Interaction Networks Using Adaptive Tabu Search

葉家瑄(Chia-Hsuan Yeh)

指導教授 ： 蘇豐文

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2011年)

https://doi.org/10.6843/NTHU.2011.00686

摘要

近期外基因體研究顯示，脫氧核糖核酸甲基化與微型核糖核酸在基因表現的調控與轉錄中扮演很重要的角色。對於在某個特定情況下的生物系統分子改變機制研究中，多樣性的基因數據，例如:訊息核糖核酸, 微型核糖核酸及脫氧核糖核酸甲基化，可以提供更全面的分析。然而要在大量的蛋白質互動網路、基因表現量資料與微型核糖核酸標的資料庫中，找出一個重要的調控網路是一個很大的挑戰。我們提出一個新穎的方法，藉由系統化的整合實驗數據、生物網路拓墣，以及禁忌搜尋法來推論具有生物意義的網路。
本篇的實驗數據是使用林口長庚醫院提供之人類的羊膜幹細胞與誘導分化之神經細胞。我們發現，在經由不同的微型核糖核酸標的資料庫找出的重要生物網路中，皆包含有細胞凋零因子以及與轉錄因子STAT關聯的神經生成功能生物途徑。進一步將實驗結果使用生物功能工具做驗證並擷取參與神經細胞反應的重要基因，結果顯示以我們方法比單獨分析基因表現量差異找出更多的神經生成基因。我們的方法也驗證比起利用單一表現量資料來推論網路，整合基因體與外基因體的表現量資料更可以擷取出重要的神經關聯網路。此外，使用我們方法找出的網路比隨機與隨機種子所挑選出的網路，更具有其生物意義。藉由網路中所含的神經關聯反應數量結果推論，整合TargetScan微型核糖核酸標的資料庫比整合PITA微型核糖核酸標的資料庫來的可信。
在資訊方法層面，我們成功的藉由禁忌搜尋法去跳脫貪婪演算法中的區域最佳解，尋找到更好的網路。同時，在禁忌搜尋法中加入生物網路拓樸的特性能幫助我們找出分數較高且更具有生物意義的網路。即使在基因體樣本數不夠的情況下，我們的方法依然可以藉由整合先前的生物知識，找出重要的生物調控網路來了解神經生成機制。我們提出一個具有條理與效率的方法，並成功的整合基因體與外基因體的表現量與蛋白質互動網路，推論出重要的生物調控網路。

關鍵字

外基因體 ； 脫氧核糖核酸甲基化 ； 微型核糖核酸 ； 訊息核糖核酸 ； 蛋白質互動網路 ； 禁忌搜尋法 ； 神經生成機制

並列摘要

Recently epigenetic study has shown that DNA methylation and miRNAs are highly relevant to the regulation of gene expressions and transcription. Multiple genomics data such as mRNA and miRNA expression and DNA methylation provide comprehensive views of the molecular changes in a biological system under a particular condition. With availability of large protein-protein interaction networks, expression data, and miRNA target databases to identify regulation networks that have significance changes in expression is a challenge problem. In this paper, we present a novel scoring method to perform integration of experimental data systematically, and develop a searching method for inferring maximum-scoring sub-networks using an adaptive Tabu search method based on the characteristics of biological network topology.
In the experiments, we tested the methods by applying them to the human amniotic membrane mesenchymal stem cells and its induced neural cell from Linkou Chang Gung Hospital. With mRNA, miRNA and DNA methylation Data, we found that apoptosis-associated factors and STAT related functional pathways participate in the differentiation process of neural cells cross public well-known miRNA target databases (Tarbase, TargetScan, miRanda, PITA and Diana microT). We validated our results related to the neural processes with p-value lower than 0.05 with functional enrichment toolkit and found more significant genes involved in our sub-networks than only differential expression changes between experimental and control data into consideration. We also show that integrating all of the three expression data can help us extract significant network which is strongly associated with neural–related processes. Our approach can extract functional higher-scoring sub-networks and outperforms than those extracted by randomized and searched from randomized seeds in terms of network scores. In our result, it denotes that the predicted targets of miRNA in TargetScan are more reliable than those in PITA database in the sense that it found more neural-process related processes.
In the computational side, our method has a better chance to escape the local optima from a greedy algorithm that leads to higher-scoring networks. We also add the biological characteristic of network topology in Tabu search, it help us to extract more biological significant sub-networks. With insufficient number of the genomics data supported, our approach still can discover the significant regulatory networks using prior biological knowledge integration. Our approach successfully integrates genetic and epigenetic expression and protein interaction networks to identify the regulatory networks in a coherent and efficiency manner.

並列關鍵字

Epigenetic ； DNA methylation ； miRNA ； mRNA ； Protein-protein interaction ； Tabu search ； Neurogenic Mechanism

參考文獻

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