近年來，由於高通量定序以及酵母雙雜交(yeast-two-hybrid)技術的出現，產生了大量的生物網路，例如，蛋白質交互作用網路以及生物代謝網路。這些資料使科學家能更進一步的從系統的角度來研究生物現象。然而要從如此大量的資料中擷取出具有生物意義的資訊是很大的挑戰。因此分群演算法在系統生物學上就成為一個非常重要的研究方法。在本研究中，我們將會從三個方向來切入討論分群演算法在生物蛋白質交互網路上的應用，包含不同物種（網路）間的分群、單一物種（網路）內的分群以及探討如何使用分群演算法來比較不同物種間（網路）的差異。網路比對演算法即為其中一種最重要的跨網路分群演算法，其可基於蛋白質序列相似度與其在網路中的拓樸相似度，將不同物種間具有相似功能的蛋白質分群。由於越來越多的網路比對演算法被開發出來，我們發展了一個最佳化網路比對結果的的演算法，其可以優化任何來源的網路比對結果，並且只需花費相對低的成本。另一方面，從蛋白質交互作用網路預測蛋白質複合體的問題，則屬於網路內的分群問題。由於現有的蛋白質複合體預測演算法皆會大幅度的受蛋白質交互作用網路資料的實驗誤差影響，因此我們結合網路比對與一有效量化蛋白質間群聚程度的計算方法，發展出一有效的演算法來克服蛋白質交互作用網路先天上的實驗誤差。此外，我們還將全域多網路比對演算法應用在比對基因體相近似微生物的代謝網路上，並重建其系統發生樹。我們的方法可以更高的解析度分辨出這些基因體相似的微生物，其在代謝網路與行為上的差異。我們的實驗結果證明了我們以上所提出的演算法皆能有效的達成其各自的目的，並有能力發掘出系統生物學中具有生物意義的重要發現。

Nowadays, thanks for the high throughput sequencing and yeast-two-hybrid techniques, more and more biological network data are available, such as protein-protein interaction (PPI) or metabolic network data. Such data can help scientists to further study the biological phenomena in systems-level. However, to extract meaningful information from the huge amount of data is quite a challenge. Thus, developing clustering algorithms is very important in systems biology. In this dissertation, we introduce our studies on clustering algorithms from the inter-species perspective as well as the intra-species perspective. Network alignment algorithms are one of the most important inter-species clustering techniques in the study of systems biology, and it focuses on collecting the functionally similar proteins of different species’ networks based on not only sequence similarity but also topology similarity. When more and more network alignment algorithms have been published, we develop an efficient network alignment booster which can refine the alignment results from any source with low cost. On the other hand, discovering protein complexes from a PPI network concentrates on clustering proteins that have highly connectivity between each other in one single network. In recent years, many approaches have been developed to solve this problem but the edge loss in PPI network is still the natural limitation. For this purpose, we develop a new algorithm which combines multiple network alignment with a new efficient connectivity measurement, NECC, to conquer this limitation. Also, we apply global multiple network alignment algorithm to the metabolic networks of bacteria and reconstruct the phyletic relationships between them and separate the genetically similar species into different groups based on their metabolic behavior. Furthermore, we try to identify the dissimilarity of metabolic pathways between close species. All in all, we demonstrate the effectiveness for each of the proposed clustering algorithms, which also reveals the important biological findings in systems biology.

中文摘要 2
Abstract 3
Chapter 1 Introduction 10
Chapter 2 Inter-species Clustering 14
2.1 Background 15
2.2 Methods 18
2.2.1 Problem Formulation 18
2.2.2 Algorithm 20
2.2.3 Running-time Analysis 25
2.3 Results 27
2.3.1 Implementation 31
2.3.2 Performance of 2-Opt and 3-Opt 33
2.3.3 Refining GRAAL, IsoRank and PATH 35
2.3.4 Robustness 38
2.4 Discussion 41
2.4.1 PISwap 41
2.4.2 Detailed Setting 42
2.4.3 Evolutionary Model 43
2.5 Figures and Tables 46
Figure 2-1. Evaluation of the refinement of the initial mappings obtained by GRAAL, IsoRank and PATH; each of the blue-series and red-series bars, respectively, represents the result before and after refinement by PISwap (2010 PPI data) 46
Figure 2-2. Evaluation of the refinement of the initial mappings obtained by GRAAL, IsoRank and PATH; each of the blue-series and red-series bars, respectively, represents the result before and after refinement by PISwap (2008 PPI data) 47
Figure 2-3. Simulation experiments for robustness of PISwap; each of the blue-series and red-series bars, respectively, represents the result before and after refinement by PISwap 48
Table 2-1. Evaluation of alignments based on the initial mappings produced by Hungarian algorithm (2010 PPI data) 49
Table 2-2. Evaluation of alignments based on the initial mappings produced by Hungarian algorithm (2008 PPI data) 49
Chapter 3 Intra-species Clustering 50
3.1 Background 51
3.2 Methods 56
3.2.1 New Edge Clustering Coefficient 56
3.2.2 Weighted Edge Density 58
3.2.3 Functional Orthologs 58
3.2.4 Redundant Complex Filtering 59
3.2.5 Main Algorithm 59
3.2.6 Performance Comparison Metrics 61
3.3 Results 63
3.3.1 Datasets 63
3.3.2 Reference Sets 64
3.3.3 Quality of Complex Identification 64
3.3.4 Performance Comparison 65
3.3.5 Further Comparison with ClusterONE 66
3.3.6 Robustness and Error-tolerance 68
3.3.7 Orthology Complexes 70
3.4 Discussion 72
3.4.1 Effect of Weighted Edge Density Thresholds 72
3.4.2 Performance of Different Functional Orthology Relationships 73
3.5 Figures and Tables 75
Figure 3-1. An example illustrating one of the output clusters derived by our algorithm: the NECC complex extracted from the PPI network of human was appended by two orthology complexes from yeast and fly. 75
Figure 3-2. Simulation experiments for the robustness of our algorithm, 76
Figure 3-3. The protein complexes in human that can only be discovered by our algorithm with functional orthology information: the pink nodes represent the matched proteins in the reference complexes by our algorithm. 77
Figure 3-4. The protein complexes in yeast that can only be discovered by our algorithm with functional orthology information: the pink nodes represent the matched proteins in the reference complexes by our algorithm. 78
Figure 3-5. The protein complexes in fly that can only be discovered by our algorithm with functional orthology information: the pink nodes representthe matched proteins in the reference complexes by our algorithm. 79
Figure 3-6. Effect of weighted edge density thresholds 80
Table 3-1. Performance comparison 81
Table 3-2. Comparison between our algorithm and ClusterONE under our reference set 82
Table 3-3. Comparison between our algorithm and ClusterONE under the MIPS reference set 83
Table 3-4. Performance of orthology complexes identification 84
Table 3-5. Performance comparison of our algorithm by using different orthology relationships between species 85
Chapter 4 Discovering Dissimilarity Between Species with Multiple Network Alignment 86
4.1 Background 87
4.2 Results 89
4.2.1 Phylum-scale Classification 90
4.2.2 Lactobacillus 91
4.2.3 Prochlorococcus and Synechococcus 92
4.2.4 Green Sulfur and Green Nonsulfur Bacteria 92
4.3 Methods 93
4.4 Discussion 95
4.6 Figures and Tables 100
Figure 4-1. Phylum-scale classification 100
Figure 4-2. Comparison of reconstructed phylogenic trees 101
Figure 4-3. Differences between our tree and the tree generated by Zhang et al. 102
Figure 4-4. Lactobacillus 103
Figure 4-5. Prochlorococcus and Synechococcus 104
Figure 4-6. Green sulfur and green nonsulfur bacteria 105
Figure 4-7. Differences between our tree and the tree generated by Chang et al. 106
Figure 4-8. Statistics for KEGG pathways between three pairs of organisms 107
Figure 4-9. Statistics for KEGG pathways between two pairs of organisms in Lactobacillus: 108
Figure 4-10. Statistics for KEGG pathways between two pairs of organisms of Prochlorococcus and Synechococcus 109
Table 4-1. Organisms used in this study 110
Algorithm 4-1. The IsoRankN algorithm 114
Chapter 5 Conclusions 116
Acknowledgement 117
References 118

1. Lindqvist Y., and Schinelder, G.: Circular permutations of natural protein sequences: structural evidence. Curr Opin Struct Biol 1997, 7:422-427.
2. Schena M, Shalon, D., Davis, R.W., Brown, P.O.: Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray. Science 1995, 270(5235):467-470.
3. Han, J.-D. et al.: Effect of sampling on topology predictions of protein–protein interaction networks. Nat Biotechnol 2005, 23:839-844.
4. Xu, X. et al.: The tandem affinity purification method: An efficient system for protein complex purification and protein interaction identification. Protein Expression and Purification 2010, 72(2):149-156.
5. Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R.M., Ideker, T.: Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 2005, 102(6):1974-1979.
6. Singh, R., Xu, J., and Berger, B.: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc Natl Acad Sci USA 2008, 105(35):12763-12768.
7. Kelley, B.P. et al.: Pathblast: a tool for alignment of protein interaction networks. Nucleic Acids Res 2004, 32:83-88.
8. Kalaev, M., Smoot, M., Ideker, T., and Sharan, R.: NetworkBLAST: comparative analysis of protein networks. Bioinformatics 2008, 24(4):594-596.
9. Koyutürk M. et al.: Pairwise alignment of protein interaction networks. J Comput Biol 2006, 13:182-199.
10. Flannick, J., Novak, A., Srinivasan, B.S., McAdams, H.H., and Batzoglou, S.: Graemlin: general and robust alignment of multiple large interaction networks. Genome Res 2006, 16(9):1169-1181.
11. Flannick, J., Novak, A., Do, C.B., Srinivasan, B.S., and Batzoglou, S.: Automatic parameter learning for multiple local network alignment. J Comput Biol 2009, 16(8):1001-1022.
12. Liao, C.-S., Lu, K., Baym, M., Singh, R., and Berger, B.: IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 2009, 25(12):i253-258.
13. Kuchaiev, O., Milenkovic, T., Memisevic, V., Hayes, W., Pržulj, N.: Topological network alignment uncovers biological function and phylogeny. J R Soc Interface 2010, 7(50):1341-1354.
14. Sahraeian, S.M.E., and Yoon, B.-J.: SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks. PLoS ONE 2013, 8(7):e67995.
15. Alkan, F. and Erten, C.: BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks. Bioinformatics 2014, 30(4):531-539.
16. Jeong, H. and Yoon, B.-J.: Accurate multiple network alignment through context-sensitive random walk. BMC Systems Biology 2015, 9(Suppl. 1):S7.
17. van Dongen, S.: Graph clustering by flow simulation. PhD Thesis. Utrecht, The Netherlands: University of Utrecht; 2000.
18. Bader, G.D. and Hogue, C.WV.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003, 4:2.
19. King, A.D., Pržulj, N., and Jurisica, I.: Protien complex prediction via cost-based clustering. Bioinformatics 2004, 20(17):3013-3020.
20. Altaf-UI-Amin M. et al.: Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 2006, 7:207.
21. Adamcsek, B. et al.: Cfinder:locating cliques and overlapping modules in biological networks. Bioinformatics 2006, 22:1021-1023.
22. Chua, H.N. et al.: Using indirect protein-protein interactions for protein complex prediction. J Bioinform Comput Biol 2008, 6:435-466.
23. Liu, G., Wong, L., and Chua, H.N.: Complex discovery from weighted PPI networks. Bioinformatics 2009, 25(15):1891–1897.
24. Wu, M., Li, X., Kwoh, C.K., and Ng, S.K.: A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinformatics 2009, 10:169.
25. Nepusz, T., Yu, H., and Paccanaro, A.: Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012, 9:471-472.
26. Peng, W., Wang, J., Zhou, B., and Wang, L.: Identification of Protein Complexes Using Weighted PageRank-Nibble Algorithm and Core-Attachment Structure. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2014, 12(1):179-192.
27. Barabási, A.L. and Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat Rev 2004, 5:101-113.
28. Guo, X. and Hartemink, A.J.: Domain-oriented edge-based alignment of protein interaction networks. In: Proceedings of the International Conference on Intelligent Systems in Molecular Biology 2009; Stockholm, Sweden. 240-246.
29. Kelley, B.P. et al.: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci USA 2003, 100:11394-11399.
30. Zaslavskiy, M. et al.: Global alignment of protein–protein interaction networks by graph matching methods. In: Proceedings of the International Conference on Intelligent Systems in Molecular Biology; Stockholm, Sweden. 2009: 259-267.
31. Formont-Racine, M. et al.: Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nat Genet 1997, 16(277-282).
32. Ito, T. et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001, 98:4569-4574.
33. Uetz, P. et al.: A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:623-627.
34. Aebersold, R. and Mann, M.: Mass spectrometry-based proteomics. Nature 2003, 422:198-207.
35. Bader, G.D. and Hogue, W.V.: Analyzing yeast protein–protein interaction data obtained from different sources. Nat Biotechnol 2002, 20:991-997.
36. Ho, Y. et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415:180-183.
37. Gavin, A.C. et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415:141-147.
38. Tan, K., Ideker, T.: Protein Interaction Networks. In: Biological Networks, Complex Systems and Interdisciplinary Science. Edited by Képès F, vol. 3. Singapore: World Scientific Publishing Co. Pte. Ltd; 2007.
39. Aladag˘, A.E. and Erten, C.: SPINAL: scalable protein interaction network alignment. Bioinformatics 2013, 29:917-924.
40. Berg, J. and Lässig, M.: Cross-species analysis of biological networks by Bayesian alignment. Proc Natl Acad Sci USA 2006, 103:10967-10972.
41. Dutkowski, J. and Tiuryn, J.: Identification of functional modules from conserved ancestral protein-protein interactions. Bioinformatics 2007, 23:149-158.
42. Patro, R. and Kingsford, C.: Global network alignment using multiscale spectral signatures. Bioinformatics 2012, 28:3105-3114.
43. Srinivasan, B.S. et al.: Integrated protein interaction networks for 11 microbes. In: Research in Computational Molecular Biology. Edited by Alberto Aea, vol. 3909. Berlin/Heidelberg: Springer; 2006: 1-14.
44. Chindelevitch, L. et al.: Local optimization for global alignment of protein interaction networks. Proc Pac Symp Biocomput 2010(15):123-132.
45. Hubbard, T.J. et al.: Ensembl 2009. Nucleic Acids Res 2009, 37:D690-D697.
46. Sahni, S. and Gonzales, T.: P-complete approximation problems. J ACM 1976, 23:555-565.
47. Croes, G.A.: A method for solving traveling salesman problems. Oper Res 1958, 6:791-812.
48. Lawler, E.L. et al.: The Traveling Salesman Problem. Chichester: John Wiley & Sons; 1985.
49. Johnson, D.S. and McGeoch, L.A.: The traveling salsman problem: a case study in local optimization. In: Local Search in Combinatorial Optimization. Edited by Aarts EHLaL, J.K. London: John Wiley & Sons; 1997: 215-310.
50. Papadimitriou, C.H. and Steiglitz, K.: On the complexity of local search for the traveling salesman problem. SIAM J Computing 1977, 6:76-83.
51. Lueker, G. manuscript, Princeton University, 1976.
52. Chandra, B., Karloff, H. and Tovey, C.: New results on the old k-opt algorithm for the TSP. In: In Proceedings of the 5th ACM-SIAM Symposium on Discrete Algorithm: 1994. 150-159.
53. Arya, V., Garg, N., Khandekar, R., Meyerson, A., Munagala, K. and Pandit, V. : Local search heuristics for k-median and facility location problems. SIAM J Computing 2004, 33(3):554-562.
54. Pardalos, P.M., Rendl, F., and Wolkowicz, H.: The Quadratic Assignment Problem: A Survey and Recent Developments. In Proceedings of the DIMACS Workshop on Quadratic Assignment Problems 1994, volume 16 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science:1-42.
55. Viger, F. and Latapy, M.: Efficient and simple generation of random simple connected graphs with prescribed degree sequence. In Proceedings of the International Computing and Combinatorics Conference 2005:440-449.
56. Taylor, R.: Constrained switchings in graphs. Combinatorial Mathematics 1980, 8:314-336.
57. Kuhn, H.W.: The Hungarian Method for the assignment problem. Naval Res Log Quart 1995, 2:83-97.
58. Kao, M.Y., Lam, T.W., Sung, W.K., and Ting, H.F.: A decomposition theorem for maximum weight bipartite matchings. SIAM J Computing 2001, 31:18-26.
59. Komili, S. et al.: Functional specificity among ribosomal proteins regulates gene expression. Cell 2007, 131:557-571.
60. Memišević, V. et al.: Complementarity of network and sequence information in homologous proteins. J Integr Bioinformatics 2010, 7:135.
61. Kuchaiev, O. and Pržulj, N.: Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 2010, 27:1390-1396.
62. Breitkreutz, B.J. et al.: The BioGRID Interaction Database. Nucleic Acids Res 2008, 36:D637-D640.
63. Salwinski, L. et al.: The database of interacting proteins: 2004 update. Nucleic Acids Res 2004, 32:D449-D451.
64. Keshava Prasad, T.S. et al.: Human protein reference database 2009 update. Nucleic Acids Res 2009, 37:D767-D772.
65. Park, D. et al.: IsoBase: a database of functionally related proteins across PPI networks. Nucleic Acids Res 2011, 39:D295-D300.
66. Hagberg, A.A. et al.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference: 2008; Pasadena, California. 11-15.
67. Galil, Z.: Efficient algorithms for finding maximum matchings in graphs. ACM Comput Surv 1986, 18:23-38.
68. Pržulj, N. et al.: Modeling interactome: scale-free or geometric? Bioinformatics 2004, 20:3508-3515.
69. Csardi, G. and Nepusz, T.: The igraph software package for complex network research. Int J Complex Syst 2006, 36:1695.
70. Zhang, Y., Li, S., Skogerbo, G., Zhang, Z., Zhu, X., Sun, S., Lu, H., Shi, B., and Chen, R.: Phylophenetic properties of metabolic pathway topologies as revealed by global analysis. BMC Bioinformatics 2006, 7:252.
71. Mano, A., Tuller, T., Beja, O., and Pinter, R.Y.: Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways. BMC Bioinformatics 2010, 11(Suppl 1):S38.
72. Ma, C.-Y. et al.: Reconstruction of phyletic trees by global alignment of multiple metabolic networks. BMC Bioinformatics 2013, 14(Suppl. 2):S12.
73. Rigaut, G. et al.: A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotech 1999, 17:1030-1032.
74. Berger, B., Peng, J., and Singh, M.: Computational solutions for omics data. Nat Rev Genet 2013, 14(5):333-346.
75. Tarassov, K. et al.: An in vivo map of the yeast protein interactome. Science 2008, 320(5882):1465-1470.
76. Li, X.L., Wu, M., Kwoh, C.K., and Ng, S.K.: Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics 2010, 11(Suppl. 1):S3.
77. Blasche, S. and Koegl, M.: Analysis of protein-protein interactions using LUMIER assays. Methods Mol Biol 2013, 1064:17-27.
78. Taipale, M. et al.: A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 2014, 158:434-448.
79. Sahni, N. et al.: Widespread macromolecular interaction perturbations in human genetic disorders. Cell 2015, 161:647-660.
80. Snider, J. et al.: Fundamentals of protein interaction network mapping. Mol Syst Biol 2015, 11(12):848.
81. Maraziotis, I.A., Dimitrakopoulou, K., and Bezerianos, A.: Growing functional modules form a seed protein via integration of protein interaction and gene expression data. BMC Bioinformatics 2007, 8:408.
82. Ulitsky, I. and Shamir, R.: Identification of functional modules using network topology and high-throughput data. BMC Systems Biology 2007, 1:8.
83. Jung, S.H. et al.: Protein complex prediction based on simultaneous protein interaciton network. Bioinformatics 2010, 26(3):385-391.
84. Singh Rea: Struct2Net: a web service to predict proteinVprotein interactions using a structure-based approach. Nucleic Acids Res 2010, 38:W508-W515.
85. Zhang, S.H., Ning, X.M., Liu, H.W., and Zhang, X.S.: Prediction of protein complexes based on protein interaction data and functional annotation data using kernel methods. LNBI 2006, 4115:514-524.
86. Cho, Y.R., Hwang, W., Ramanathan, M., and Zhang, A.: Semantic integration to identify overlapping functional modules in protein interaciton networks. BMC Bioinformatics 2007, 8:265.
87. Li, X.L., Foo, C.S., and Ng, S.K.: Discovering protein complexes in dense reliable neighborhoods of protein interaction networks. CSB 2007:157-168.
88. Cho, H., Bonnie, B., and Peng, J.: Diffusion component analysis: unraveling functional topology in biological networks. Proc of the 19th Research in Computational Molecular Biology (RECOMB), LNCS 2015, 9029:62-64.
89. Qi, Y. et al.: Protein complex identification by supervised graph local clustering. Bioinformatics 2008, 24:i250-i268.
90. Hirsh, E. and Sharan, R.: Identification of conserved protein complexes based on a model of protein network evolution. Bioinformatics 2006, 23(2):e170-e176.
91. Sharan, R. et al.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. RECOMB 2004:282-289.
92. Dost, B. et al.: QNet: A tool for querying protein interaction networks. RECOMB 2007:1-15.
93. Davis, D. et al.: Topology-function conservation in protein-protein interaction networks. Bioinformatics 2015, 31(10):1632-1639.
94. Zhao, N., Han, J.G., Shyu, C.R., and Korkin, D.: Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput Biol 2014, 10(5):e1003592.
95. Berg, J., Lässig, M., and Wagner, A.: Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications. BMC Evol Biol 2004, 4:51.
96. Wagner, A.: The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol Biol Evol 2001, 18:1283-1292.
97. Arabidopsis Interactome Mapping Consortium: Evidence for Network Evolution in an Arabidopsis Interactome Map. Science 2011, 333:601-607.
98. Soffer, S.N. and Vázquez, A.: Network clustering coefficient without degree-correlation biases. Physical Review E 2005, 71:057101.
99. Wang, J., Li, M.,Wang, H., and Pan, Y.: Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012, 9(4):1070-1080.
100. Li, M. et al.: Modifying the DPClus algorithm for identifying protein complexes based on new topology structures. BMC Bioinformatics 2008, 9:398.
101. Tomita, E., Tanaka, A., and Takahashi, H.: The worst-case time complexity for generating all maximal cliques and computational experiments. Theor Comput Sci 2006, 363:28-42.
102. Liu, G., Li, J., and Wong, L.: Assessing and predicting protein interactions using both local and global network topological metrics. In: Proceedings of the 19th International Conference on Genome Informatics; Gold Coast, Australia. 2008: 138-149.
103. Brohee, S. and van Helden, J.: Evaluation of clustering algorithms for proteinprotein interaction networks. BMC Bioinformatics 2006, 7:488.
104. Chatr-aryamontri, A. et al.: The BioGRID interaction database: 2013 update. Nucleic Acids Res 2013, 41:D816-D823.
105. Vinayagam, A. et al.: Protein complex-based analysis framework for highthroughput data sets. Sci Signal 2013, 6:rs5.
106. Ruepp, A. et al.: CORUM: the comprehensive resource of mammalian protein complexes. Nucleic Acids Res 2008, 36:D646-D650.
107. Ashburner, M. et al.: Gene Ontology: tool for the unification of biology. Nat Genet 2000, 25(1):25-29.
108. Luc, P.V. and Tempst, P.: PINdb: A database of nuclear protein complexes from human and yeast. Bioinformatics 2004, 20:1413-1415.
109. Kanehisa, M. et al.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012, 40:D109-D114.
110. Pu, S. et al.: Up-to-date catalogue of yeast protein complexes. Nucleic Acids Res 2009, 37(3):825-831.
111. Guruharsha, K.G. et al.: A Protein Complex Network of Drosophila melanogaster. Cell 2011, 147(3):690-703.
112. Mewes, H.W. et al.: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 2004, 32:D41-D44.
113. Krogan, N.J. et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440:637-643.
114. Gavin, A. et al.: Proteome survey reveals modularity of the yeast cell machinery. Cell 2006, 440:631-636.
115. Collins, S.R. et al.: Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae. Mol Cell Proteomics 2007, 6:439-450.
116. Delsuc, F., Brinkmann, H., and Philippe, H.: Phylogenomics and the reconstruction of the tree of life. Nat Rev Genet 2005, 6(5):361-375.
117. Woese, C.R., Kandler, O., and Wheelis, M.L.: Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc Natl Acad Sci USA 1990, 87(12):4576-4579.
118. Fukushima, M., Kakinuma, K., and Kawaguchi, R.: Phylogenetic analysis of Salmonella, Shigella, and Escherichia coli strains on the basis of the gyrB gene sequence. J Clin Microbiol 2002, 40(8):2779-2785.
119. Ciccarelli, F.D., Doerks, T., von Mering, C., Creevey, C.J., Snel, B., and Bork, P.: Toward
automatic reconstruction of a highly resolved tree of life. Science 2006, 311(5765):1283-1287.
120. Creevey, C.J., Doerks, T., Fitzpatrick, D.A., Raes, J., and Bork, P.: Universally distributed single-copy genes indicate a constant rate of horizontal transfer. PLoS One 2011, 6(8):e22099.
121. Forst, C.V. and Schulten, K.: Phylogenetic analysis of metabolic pathways. J Mol Evol 2001, 52(6):471-489.
122. Heymans, M. and Singh, A.K.: Deriving phylogenetic trees from the similarity analysis of metabolic pathways. Bioinformatics 2003, 19(Suppl 1):i138-146.
123. Aguilar, D., Aviles, F.X., Querol, E., and Sternberg, M.J.: Analysis of phenetic trees based on metabolic capabilites across the three domains of life. J Mol Biol 2004, 340(3):491-512.
124. Clemente, J.C., Satou, K., and Valiente, G.: Reconstruction of phylogenetic relationships from metabolic pathways based on the enzyme hierarchy and the gene ontology. Genome Inform 2005, 16(2):45-55.
125. Pinter, R.Y., Rokhlenko, O., Yeger-Lotem, E., and Ziv-Ukelson, M.: Alignment of metabolic pathways. Bioinformatics 2005, 21(16):3401-3408.
126. Oh, S.J., Joung, J.G., Chang, J.H., and Zhang, B.T.: Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks. BMC Bioinformatics 2006, 7:284.
127. Clemente, J.C., Satou, K., and Valiente, G.: Phylogenetic reconstruction from nongenomic data. Bioinformatics 2007, 23(2):e110-115.
128. Mazurie, A., Bonchev, D., Schwikowski, B., and Buck, G.A.: Phylogenetic distances are encoded in networks of interacting pathways. Bioinformatics 2008, 24(22):2579-2585.
129. Borenstein, E., Kupiec, M., Feldman, M.W., and Ruppin, E.: Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc Natl Acad Sci USA 2008, 105(38):14482-14487.
130. Chang, C.W., Lyu, P.C., and Arita, M.: Reconstructing phylogeny from metabolic substrate-product relationships. BMC Bioinformatics 2011, 12(Suppl 1):S27.
131. Kanehisa, M. and Goto, S.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28(1):27-30.
132. Sayers, E.W., Barrett, T., Benson, D.A., Bolton, E., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., Dicuccio, M., Federhen, S. et al.: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2012, 40(Database):D31-25.
133. Stiles, M.E. and Holzapfel, W.H.: Lactic acid bacteria of foods and their current taxonomy. Int J Food Microbiol 1997, 36(1):1-29.
134. Ljungh, A. and Wadstrom, T.: Lactobacillus molecular biology: from genomics to probiotics: Caister Academic Press; 2009.
135. Lee, C.C., Lo, W.C., Lai, S.M., Chen, Y.P., Tang, C.Y., Lyu, P.C.: Metabolic classification of microbial genomes using functional probes. BMC Genomics 2012, 13:157.
136. Canchaya, C., Claesson, M.J., Fitzgerald, G.F., van Sinderen, D., and O’Toole, P.W.: Diversity of the genus Lactobacillus revealed by comparative genomics of five species. Microbiology 2006, 152(Pt 11):3185-3196.
137. Makarova, K., Slesarev, A., Wolf, Y., Sorokin, A., Mirkin, B., Koonin, E., Pavlov, A., Pavlova, N., Karamychev, V., Polouchine, N. et al.: Comparative genomics of the lactic acid bacteria. Proc Natl Acad Sci USA 2006, 103(42):15611-11561.
138. Zhang, Z.G., Ye, Z.Q., Yu, L., and Shi, P.: Phylogenomic reconstruction of lactic acid bacteria: an update. BMC Evol Biol 2011, 11:1.
139. Rocap, G., Distel, D.L., Waterbury, J.B., and Chisholm, S.W.: Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S-23S ribosomal DNA internal transcribed spacer sequences. Appl Environ Microbiology 2002, 68(3):1180-1191.
140. Martiny, A.C., Kathuria, S., Berube, P.M.: Widespread metabolic potential for nitrite and nitrate assimilation among Prochlorococcus ecotypes. Proc Natl Acad Sci USA 2009, 106(26):10787-10792.
141. Blankenship, R.E.: Molecular mechanisms of photosynthesis: Blackwell Science; 2002.
142. Felsenstein, J.: PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics 1989, 5:164-166.
143. Huson, D.H., Richter, D.C., Rausch, C., Dezulian, T., Franz, M., and Rupp, R.: Dendroscope: An interactive viewer for large phylogenetic trees. BMC Bioinformatics 2007, 8:460.
144. Ay, F., Kellis, M., and Kahveci, T.: SubMAP: aligning metabolic pathways with subnetwork mappings. J Comput Biol 2011, 18(3):219-235.
145. Chindelevitch, L., Stanley, S., Hung, D., Regev, A., and Berger, B.: MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis. Genome Biol 2012, 13(1):r6.
146. Suthram, S., Sittler, T., and Ideker, T.: The Plasmodium protein network diverges from those of other eukaryotes. Nature 2005, 438(7064):108-112.
147. Agrafioti, I., Swire, J., Abbott, J., Huntley, D., Butcher, S., and Stumpf, M.P.: Comparative analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans protein interaction networks. BMC Evol Biol 2005, 5:23.
148. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J.: Basic local alignment search tool. J Mol Biol 1990, 215(3):403-410.