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。

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.

Ambro V: The functions of animal microRNAs, Nature, 2004, 431:350-355.
Aranha MM, Santos DM, Xavier JM, Low WC, Steer CJ, Sola S, and Rodrigues CM: Apoptosis-associated microRNAs are modulated in mouse, rat and human neural differentiation, BMC Genomics, 2010, 11:514.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, and Sherlock G: Gene Ontology: Tool for the unification of biology, The Gene Ontology Consortium, Nature Genetics, 2000, 25(1):25-29.
Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, 2004, 116:281-297.
Bracken AP, Ciro M, Cocito A and Helin K: E2F target genes: unraveling the biology, Trends Biochem. Sci., 2004, 29:409–417
Chari R, Coe BP, Vucic EA, Lockwood WW, and Lam WL: An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer, BMC Systems biology, 2010, 4:67.
Chen H, Shalom-Feuerstein R, Riley J, Zhang SD, Tucci P, Agostini M, Aberdam D, Knight RA, Genchi G, Nicotera P, Melino G, Vasa-Nicotera M: miR-7 and miR-214 are specifically expressed during neuroblastoma differentiation, cortical development and embryonic stem cells differentiation, and control neurite outgrowth in vitro, Biochemical and Biophysical Research Communications, 2010, 394, 921–927
Devarenne I, Mabed H, and Caminada A: Adapative Tabu Tenure computation in local search, Evolutionary Computation in Combinatorial Optimization, 2008, 4972:1-12
Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, and Conklin BR: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data, Genome Biology, 2003, 4:R7.
Duval B and Hao JK: Advances in metaheuristic for gene selection and classification of micro array data, Brief Bioinform, 2010, 11(1): 127-141.
Enright AJ, John B, Gaul U, Tuschl T, Sander C, and Marks DS: MicroRNA targets in Drosophila, Genome Biol., 2003, 5(1):R1.
Faghihi MA, Zhang M, Huang J, Modarresi F, Van der Brug MP, Nalls MA, Cookson MR, St-Laurent G III, and Wahlestedt C: Evidence for natural antisense transcript-mediated inhibition of microRNA function, Genome Biol., 2010, 11(5):R56.
Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, and Bartel DP: The widespread impact of mammalian microRNAs on mRNA repression and evolution, Science, 2005, 310(5755):1817–1821.
Friedman RC, Farh KK, Burge CB, and Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs, Genome Res, 2009, 19(1):92-105
Glover F: Tabu search-Part I. ORSA Journal on Computing, 1989, 1(3):190-206.
Glover F: Tabu search-part II. ORSA Journal on Computing, 1990, 2(1):4-32.
Glover F and Laguna M: TABU SEARCH, 1997.
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, and Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature, Nucleic Acids Res., 2006, 34 (Database issue):D140–D144.
Guo Z, Wang L, Li Y, Gong X, Yao C, Ma W, Wang D, Li Y, Zhu J, Zhang M, Yang D, Rao S, and Wang J: Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network, Bioinformatics, 2007, 23(16): 2121-2128.
Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP, and Vidal M: Evidence for dynamically organized modularity in the yeast protein–protein interaction network, Nature, 2004, 430(6995):88-93.
Huang da W, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, and Lempicki RA: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists, Gene Biology, 2007, 8(9):R183.
Huang JC, Babak T, Corson TW, Chua G, Khan S, Gallie BL, Hughes TR, Blencowe BJ, Frey BJ, and Morris QD: Using expression profiling data to identify human microRNA targets. Nat Meth, 2007, 4(12):1045–1049.
Ideker T, Ozier O, Schwikowski B and Siegel AF: Discovering regulatory and signaling circuits in molecular interac-tion networks, Bioinformatics, 2002, 18(Suppl. 1):S233-S240.
Jasmin JF, Yang M, Iacovitti L and Lisanti MP: Genetic ablation of caveolin-1 increases neural stem cell proliferation in the subventricular zone (SVZ) of the adult mouse brain, Cell cycle Georgetown Tex , 2009, 8(23):3978-3983.
Joshi A, Van de Peer Y, and Michoel T: Analysis of a Gibbs sampler method for model-based clustering of gene expression data, Bioinformatics, 2008, 24(2):176-183.
Joshi A, De Smet R, Marchal K, Van de Peer Y, and Michoel T: Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, 2009, 25(4):490-496.
Joung JG and Fei Z: Identification of microRNA regulatory modules in arabidopsis via a probabilistic graphical model, Bioinformatics, 2009, 25(3):387–393.
Joung JG, Hwang KB, Nam JW, Kim SJ, and Zhang BT: Discovery of microRNA-mRNA modules via population-based probabilistic learning, Bioinformatics, 2007, 23(9): 1141–1147.
Kanehisa M, Goto S, Kawashima S, Okuno Y, and Hattori M: The KEGG resource for deciphering the genome, Nucleic Acids Research, 2004, 32(Database issue):D277-D280.
Kertesz M, Iovino N, Unnerstall U, Gaul U and Segal E: The role of site accessibility in microRNA target recognition, Nature Genetics, 2007, 39:1278 - 1284.
Kim S, Choi M, and Cho KH: Identifying the target mRNAs of microRNAs in colorectal cancer, Comput Biol Chem., 2009, 33(1):94-99
Kiriakidou M, Nelson PT, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos Z, and Hatzigeorgiou A: A combined computational–experimental approach predicts human microRNA targets, Genes Dev., 2004, 18(10):1165–1178
Kitano H: Systems biology: A brief overview, Science, 2002, 295(5560):1662–1664.
Lewis BP, Burge CB, and Bartel DP: Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets, Cell, 2005, 120(1):15-20
Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, and Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs, Nature, 2005, 433(7027):769–773.
Liu B, Li J, and Tsykin A: Discovery of functional miRNAmRNA regulatory modules with computational methods, Journal of Biomedical Informatics, 2009a, 42(4):685–691.
Liu B, Li J, Tsykin A, Liu L, Gaur AB, and Goodall GJ: Exploring complex miRNA-mRNA interactions with bayesian networks by splitting-averaging strategy, BMC Bioinformatics, 2009b, 10:408.
Liu HC, Arias CR and Soo VW: BioIR: An approach to public domain resource integration of human protein-protein interaction, The proceeding of the Seventh Asia Pacific Bioinformatics Conference (APBC), 2009.
Meister G. and Tuschl T: Mechanisms of gene silencing by double-stranded RNA, Nature, 2004, 431:343-349.
Michoel T, Maere S, Bonnet E, Joshi A, Saeys Y, Van den Bulcke T, Van Leemput K, van Remortel P, Kuiper M, Marchal K, and Van de Peer Y: Validating module network learning algorithms using simu-lated data, BMC Bioinformatics, 2007, 8(Suppl. 2):S5.
Model F, Adorjan P, Olek A, and Piepenbrock C: Feature selection for DNA methylation based cancer classification, Bioinformatics, 2001, 17(Suppl. 1):S157-S164.
Moncini S, Salvi A, Zuccotti P, Viero G, Quattrone A, Barlati S, De Petro G, Venturin M and Riva P: The Role of miR-103 and miR-107 in Regulation of CDK5R1 Expression and in Cellular Migration, PLoS One, 2011, 6(5):e20038.
Nam S, Li M, Choi K, Balch C, Kim S, and Nephew KP: MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression, Nucleic Acids Re-search, 2009, 37(Web Server issue):W356–W362.
Neveu B, Trombettoni G, and Glover F: A candidate list strategy with a simple diversification device, In Proc. Constraint Prog., 2004, LNCS 3258:423–437.
Pachernik J, Horvath V, Kubala L, Dvorak P, Kozubik A, and Hampl A: Neural differentiation potentiated by the leukaemia inhibitory factor through STAT3 signalling in mouse embryonal carcinoma cells, Folia Biol. (Praha), 2007, 53(5): 157-163.
Peng X, Li Y, Walters KA, Rosenzweig ER, Lederer SL, Aicher LD, Proll S, and Katze MG: Computational identification of hepatitis c virus associated microRNA-mRNA regulatory modules in human livers, BMC Genomics, 2009, 10, 373.
Prieto C, De Las Rivas J: APID: Agile Protein Interaction DataAnalyzer, Nucleic Acids Research, 2006, 34(Web Server issue):W298-302.
Reich M, Ohm K, Angelo M, Tamayo P, and Mesirov JP: GeneCluster 2.0: an advanced toolset for bioarray analysis, Bioinformatics, 2004, 20(11):1797-1798.
Scott J, Ideker T, Karp RM, and Sharan R: Efficient algorithms for detecting signaling pathways in protein interaction networks, Ninth Annual International Conference on Research in Computational Molecular Biology, 2005, LNBI 3500:1-13.
Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, and Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nat Genet., 2003, 34(2): 166–176.
Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, and Rajewsky N: Widespread changes in protein synthesis induced by microRNAs, Nature, 2008 , 455(7209):58-63.
Sethupathy P, Corda B, and Hatzigeorgiou AG: TarBase: a comprehensive database of experimentally supported animal microRNA targets, RNA, 2006, 12(2):192-197.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proceedings of the National Academy of Sciences USA, 2005, 102(43):15545-15550
Tran DH, Satou K, and Ho TB: Finding microRNA regulatory modules in human genome using rule induction, BMC Bioinformatics, 2008, 9(Suppl. 12):S5.
Vincent I, Jicha G, Rosado M, and Dickson DW: Aberrant expression of mitotic cdc2/cyclin B1 kinase in degenerating neurons of Alzheimer's disease brain, J Neurosci. , 1997, 17(10):3588-3598
Walhout AJ: Unraveling transcription regulatory networks by proteinn-DNA and protein-protein interaction mapping, Genome Research, 2006, 16(12): 1445-1454.
Xin F, Li M, Balch C, Thomson M, Fan M, Liu Y, Hammond SM, Kim S, and Nephew KP: Computational analysis of microRNA profiles and their target genes significant involvement in breast cancer antiestrogen resistance, Bioinfor-matics, 2009, 25(4):430-434.
Xu N, Papagiannakopoulos T, Pan G, Thomson JA, and Kosik KS: MicroRNA-145 regulates OCT4, SOX2, and KLF4 and represses pluripotency in human embryonic stem cells, Cell, 2009, 137(4):647-658.
Yang Y, Kim AH, and Bonni A: The dynamic ubiquitin ligase duo: Cdh1-APC and Cdc20-APC regulate neuronal morphogenesis and connectivity, Curr Opin Neurobiol. , 2004, 20(1):92–99.
Yin KJ, Deng Z, Huang H, Hamblin M, Xie C, Zhang J, and Chen YE: miR-497 regulates neuronal death in mouse brain after transient focal cerebral ischemia, Neurobiol Dis., 2010, 38(1): 17–26.
Yoon S and De Micheli G: Prediction of regulatory modules comprising microRNAs and target genes, Bioinformatics, 2005, 21(Suppl. 2):ii93–100.
Zhao X, Wang RS, Chen L, and Aihara K: Automatic modeling of signal pathways from protein-protein interaction networks, Proceedings Trim Size, 2007, 3:42.