Introduction to biological networks

Introduction to Biological Networks

Volumn , Issue , 2016, Pages 1-312

  Raval, Alpan ^a   Ray, Animesh ^b  

^a CLAREMONT GRADUATE UNIVERSITY   (United States)

^b KECK GRADUATE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85051689931     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (208)

1. Albert, R. and Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics 74:47 (2002).
2. Albert, R. and Othmer, H. G. The topology of the regulatory interactions predicts the expression pattern of the Drosophila segment polarity genes. Journal of Theoretical Biology 223:1 (2003).
3. Aldana, M. and Cluzel, P. A natural class of robust networks. Proceedings of the National Academy of Sciences USA 100:8710 (2003).
4. Aloy, P. and Russell, R. B. Interrogating protein interaction networks through structural biology. Proceedings of the National Academy of Sciences USA 99:5896 (2002).
5. Ashburner, M., Ball, C. A., Blake, J. A., et al. Gene ontology: tool for the unification of biology. Nature Genetics 25:25 (2000).
6. Astsaturov, I., Ratushny, V., Sukhanova, A., et al. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Science Signaling 3:ra67 (2010).
7. Avery, L. and Wasserman, S. Ordering gene function: The interpretation of epistasis in regulatory hierarchies. Trends in Genetics 8:312 (1992).
8. Bader, G. D. and Hogue, C. W. V. Analyzing protein-protein interaction data obtained from different sources. Nature Biotechnology 20:991 (2002).
9. Bailey, J., Gu, Z., Clark, R., Reinert, K., and Samonte, R. Recent segmental duplications in the human genome. Science 297:1003 (2002).
10. Bandyopadhyay, S., Mehta, M., Kuo, D., et al. Rewiring of genetic networks in response to DNA damage. Science 330:1385 (2010).
11. Barabási, A.-L. Network medicine -from obesity to the "diseasome." New England Journal of Medicine 357:404 (2007).
12. Barabási, A.-L. and Albert, R. Emergence of scaling in random net-works. Science 286:509 (1999).
13. Barbie, D. A., Tamayo, P., Boehm, J. S., et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108 (2009).
14. Basso, K., Margolin, A., Stolovitzky, G., et al. Reverse engineering of regulatory networks in human B cells. Nature Genetics 37:382 (2005).
15. Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C., and Wild, D. L. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21:349 (2005).
16. Becker, S. A., Feist, A. M., Mo, M. L., et al. Quantitative prediction of cellular metabolism with constraint-based models: The COBRA toolbox. Nature Protocols 2:727 (2007).
17. Blanc, G., Barakat, A., Guyot, R., Cooke, R., and Delseny, M. Extensive duplication and reshuffling in the Arabidopsis genome. Plant Cell 12:1093 (2000).
18. Bollobás, B. Random Graphs. Cambridge University Press, Cambridge, 2nd ed. (2001).
19. Carter, G. W., Galas, D. J., and Galitski, T. Maximal extraction of biological information from genetic interaction data. PLoS Computational Biology 5:e10000347 (2009).
20. Chen, K. C., Calzone, L., Csikasz-Nagy, A., et al. Integrative analysis of cell cycle control in budding yeast. Molecular Biology of the Cell 15:3841 (2004).
21. Chung, F. and Lu, L. Complex Graphs and Networks. American Mathematical Society, Providence, RI (2006).
22. Collins, S. R., Schuldiner, M., Krogan, N. J., and Weismann, J. S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biology 7:R63 (2006).
23. Cooper, G. M. The Cell: A Molecular Approach. Sunderland, MA: Sinauer Associates, 2nd ed. (2000).
24. Cordell, H. J. Epistasis: What it means, what it doesn't mean, and statistical methods to detect it in humans. Human Molecular Genetics 11:2463 (2002).
25. Court, D. L., Oppenheim, A. B., and Adhya, S. L. A new look at bacteriophage -genetic networks. Journal of Bacteriology 189:298 (2007).
26. Covert, M. W., Schilling, C. H., and Palsson, B. Regulation of gene expression in flux balance models of metabolism. Journal of Theoretical Biology 213:73 (2001).
27. Dandekar, T., Snel, B., Huynen, M., and Bork, P. Conservation of gene order: A fingerprint of proteins that physically interact. Trends in Biochemical Sciences 23:324 (1998).
28. Dartnell, L., Simeonidis, E., Hubank, M., et al. Robustness of the p53 network and biological hackers. FEBS Letters 579:3037 (2005).
29. Deane, C., Salwinski, L., Xenarios, I., and Eisenberg, D. Protein interactions: Two methods for assessment of the reliability of high throughput observations. Molecular Cell Proteomics 1:349 (2002).
30. Deeds, E., Ashenberg, O., and Shakhnovich, E. A simple physical model for scaling in protein-protein interaction networks. Proceedings of the National Academy of Sciences USA 103:311 (2006).
31. Delbrück, M. A physicist looks at biology. In Phage and the Origins of Molecular Biology, pp. 9. Cold Spring Harbor, NY (1966).
32. Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39:1 (1977).
33. Deng, M., Mehta, S., Sun, F., and Chen, T. Inferring domain-domain interactions from protein-protein interactions. Genome Research 12:1540 (2002).
34. d'Haeseleer, P. and Church, G. M. Estimating and improving protein interaction error rates. In Computational Systems Bioinformatics (CSB), pp. 216-223 (2004).
35. Dodd, I. B., Shearwin, K. E., and Egan, J. B. Revisited gene regulation in bacteriophage -. Current Opinion in Genetics and Development 15:145 (2005).
36. Dojer, N., Gambin, A., Mizera, A., Wilczyński, B., and Tiuryn, J. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics 7:249 (2006).
37. Dorogovtsev, S., Mendes, J., and Samukhin, A. Structure of growing networks with preferential linking. Physical Review Letters 85:4633 (2000).
38. Dunn, R., Dudbridge, F., and Sanderson, C. M. The use of edgebetweenness clustering to investigate biological function in protein interaction networks. BMC Bioinformatics 6:39 (2005).
39. Erdös, P. and Renyi, A. On random graphs. i. Publicationes Mathematicae 6:290 (1959).
40. Erdös, P. and Renyi, A. On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences 5:17 (1960).
41. Evlampiev, K. and Isambert, H. Modeling protein network evolution under genome duplication and domain shuffling. BMC Systems Biology 1:49 (2007).
42. Evlampiev, K. and Isambert, H. Conservation and topology of protein interaction networks under duplication-divergence evolution. Proceedings of the National Academy of Sciences USA 105:9863 (2008).
43. Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., and Wierzbowski, J. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology 5:e8 (2007).
44. Fernandez, A. Molecular basis for evolving modularity in the yeast protein interaction network. PLoS Computational Biology 3:e226 (2007).
45. Ferrell, J. E., Jr. Self-perpetuating states in signal transduction: Positive feedback, double-negative feedback and bistability. Current Opinion in Cell Biology 14:140 (2002).
46. Fishel, R., Lescoe, M. K., Rao, M. R. S., et al. The human mutator gene homolog MSH2 and its association with hereditary nonpolyposis colon cancer. Cell 75:1027 (1993).
47. Force, A., Lynch, M., Pickett, F., et al. The preservation of duplicate genes by complementary degenerative mutations. Genetics 151:1531 (1999).
48. Formstecher, E., Aresta, S., Collura, V., Hamburger, A., and Meil, A. Protein interaction mapping: A Drosophila case study. Genome Research 15:376 (2005).
49. Fortunato, S. Community detection in graphs. Physics Reports 486:75 (2010).
50. Fraser, H. B. and Plotkin, J. B. Using protein complexes to predict phenotypic effects of gene mutation. Genome Biology 8:R252 (2007).
51. Friedman, N. Learning belief networks in the presence of missing values and hidden variables. In Proceedings of the 14th International Conference on Machine Learning, pp. 125-133. Nashville, TN (1997).
52. Friedman, N., Murphy, K., and Russell, S. Learning the structure of dynamic probabilistic networks. In Proceedings of the Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI), pp. 139-147. Morgan Kaufmann (1998).
53. Fuhrman, S. and Somogyi, R. Distributivity, a general information theoretic network measure, or why the whole is more than the sum of its parts. In Proceedings of the International Workshop on Information Processing in Cells and Tissues (IPCAT) (1997).
54. Gavin, A.-C., Bösche, M., Krause, R., et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415:141 (2002).
55. Gevers, D., Vandepoele, K., Simillion, C., and van de Peer, Y. Gene duplication and biased functional retention of paralogs in bacterial genomes. Trends in Microbiology 12:148 (2004).
56. Giacomantonio, C. E. and Goodhill, G. J. A Boolean model of the gene regulatory network underlying mammalian cortical area development. PLoS Computational Biology 6:e1000936 (2010).
57. Gilbert, S. F. Developmental Biology. Sinauer Associates, Sunderland, MA, 6th ed. (2000).
58. Glaab, E., Baudot, A., Krasnogor, N., and Valencia, A. Extending pathways and processes using molecular interaction networks to analyse cancer genome data. BMC Bioinformatics 11:597 (2010).
59. Goh, K.-I., Cusick, M. E., Valle, D., et al. The human disease network. Proceedings of the National Academy of Sciences USA 104:8685 (2007).
60. Goldberg, D. S. and Roth, F. P. Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences USA 100:4372 (2003).
61. Gray, M. W., Burger, G., and Lang, B. F. Mitochondrial evolution. Science 283:1476 (1999).
62. Green, R. A., Kao, H.-L., Audhya, A., et al. A high-resolution C. elegans essential gene network based on phenotypic profiling of a complex tissue. Cell 145:470 (2011).
63. Guimarães, K. S., Jothi, R., Zotenko, E., and Przytycka, T. M. Predicting domain-domain interactions using a parsimony approach. Genome Biology 7:R104 (2006).
64. Guimera, R. and Amaral, L. A. N. Functional cartography of complex metabolic networks. Nature 433:895 (2005).
65. Guimera, R., Sales-Pardo, M., and Amaral, L. A. N. Module identification in bipartite and directed networks. Physical Review E 76:036102 (2007).
66. Hahn, M. W. and Kern, A. D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Molecular Biology and Evolution 22:803 (2005).
67. Han, J., Bertin, N., Hao, T., Goldberg, D., and Berriz, G. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430:88 (2004).
68. Hart, G. T., Ramani, A. K., and Marcotte, E. M. How complete are current yeast and human protein-interaction networks? Genome Biology 7:120 (2006).
69. Herrgard, M. J., Swainston, N., Dobson, P., et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology 26:1155 (2008).
70. Ho, Y., Gruhler, A., Heilbut, A., et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415:123 (2002).
71. Huang, H., Jedynak, B. M., and Bader, J. S. Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps. PLoS Computational Biology 3:e214 (2007).
72. Husmeier, D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19:2271 (2003).
73. Iliopoulos, I., Enright, A. J., Poullet, P., and Ouzounis, C. A. Mapping functional associations in the entire genome of Drosophila melanogaster using fusion analysis. Comparative and Functional Genomics 4:337 (2003).
74. Ingolia, N. T. Topology and robustness in the Drosophila segment polarity network. PLoS Biology 2:e123 (2004).
75. Ito, T., Chiba, T., Ozawa, R., et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences USA 98:4569 (2001).
76. Jansen, R., Yu, H., Greenbaum, D., et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302:449 (2003).
77. Jeong, H., Mason, S., Barabási, A.-L., and Oltvai, Z. Lethality and centrality in protein networks. Nature 411:41 (2001).
78. Jeong, H., Tombor, B., Albert, R., Oltvai, Z., and Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407:651 (2000).
79. Jones, S. and Thornton, J. M. Analysis of protein-protein interaction sites using surface patches. Journal of Molecular Biology 272:121 (1997a).
80. Jones, S. and Thornton, J. M. Prediction of protein-protein interaction sites using patch analysis. Journal of Molecular Biology 272:133 (1997b).
81. Jonsson, P. F. and Bates, P. A. Global topological features of cancer proteins in the human interactome. Bioinformatics 22:2291 (2006).
82. Jothi, R., Cherukuri, P. F., Tasneem, A., and Przytycka, T. M. Coevolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions. Journal of Molecular Biology 362:861 (2006).
83. Juan, D., Pazos, F., and Valencia, A. High-confidence prediction of global interactomes based on genome-wide coevolutionary net works. Proceedings of the National Academy of Sciences USA 105:934 (2008).
84. Kaelin, W. G., Jr. The concept of synthetic lethality in the context of anticancer therapy. Nature Reviews Cancer 5:689 (2005).
85. Kaizu, K., Ghosh, S., Matsuoka, Y., et al. A comprehensive molecular interaction map of the budding yeast cell cycle. Molecular Systems Biology 6:415 (2010).
86. Kanehisa, M., Araki, M., Goto, S., et al. KEGG for linking genomes to life and the environment. Nucleic Acids Research 36:480 (2008).
87. Keller, E. Revisiting "scale-free" networks. BioEssays 27:1060 (2005).
88. Kelley, R. and Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nature Biotechnology 23:561 (2005).
89. Kellis, M., Birren, B., and Lander, E. Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae. Nature 428:617 (2004).
90. Kim, I., Liu, Y., and Zhao, H. Bayesian methods for predicting interacting protein pairs using domain information. Biometrics 63:824 (2007).
91. Kim, S., Imoto, S., and Miyano, S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. BioSystems 75:57 (2004).
92. Kim, W. K. and Marcotte, E. M. Age-dependent evolution of the yeast protein interaction network suggests a limited role of gene duplication and divergence. PLoS Computational Biology 4:e1000232 (2008).
93. King, O. D. Comment on "Subgraphs in random networks." Physical Review E 70:058101 (2004).
94. Kitano, H. and Oda, K. Self-extending symbiosis: A mechanism for increasing robustness through evolution. Biological Theory 1:61 (2006).
95. Köhler, S., Bauer, S., Horn, D., and Robinson, P. N. Walking the interactome for prioritization of candidate disease genes. American Journal of Human Genetics 82:949 (2008).
96. Krastev, D. B., Slabicki, M., Paszkowski-Rogacz, M., et al. A systematic RNAi synthetic interaction screen reveals a link between p53 and snoRNP assembly. Nature Cell Biology 13:809 (2011).
97. Krogan, N. J., Cagney, G., Yu, H., et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440:637 (2006).
98. Kuntzer, J., Backes, C., Blum, T., et al. BNDB -The biochemical network database. BMC Bioinformatics 8:367 (2007).
99. Larhammer, D., Lundin, L., and Hallbook, F. The human Hox-bearing chromosome regions did arise by block or chromosome (or even genome) duplications. Genome Research 12:1910 (2002).
100. Lauritzen, S. L. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 19:191 (1995).
101. Lawrence, M. C. and Colman, P. M. Shape complementarity at protein/ protein interfaces. Journal of Molecular Biology 234:946 (2003).
102. Lee, D.-S., Park, J., Kay, K. A., et al. The implications of human metabolic network topology for disease comorbidity. Proceedings of the National Academy of Sciences USA 105:9880 (2008a).
103. Lee, H., Deng, M., Sun, F., and Chen, T. An integrated approach to the prediction of domain-domain interactions. BMC Bioinformatics 7:269 (2006).
104. Lee, J. M., Gianchandani, E. P., Eddy, J. A., and Papin, J. A. Dynamic analysis of integrated signaling, metabolic and regulatory networks. PLoS Computational Biology 4:e1000086 (2008b).
105. LeMeur, N. and Gentleman, R. Modeling synthetic lethality. Genome Biology 9:10 (2008).
106. Li, F., Long, T., Lu, Y., Ouyang, Q., and Tang, C. The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences USA 101:4781 (2004).
107. Liang, S., Fuhrman, S., and Somogyi, R. REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium in Biocomputing 3:18 (1998).
108. Lim, J., Hao, T., Shaw, C., et al. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125:801 (2006).
109. Lin, N., Wu, B., Jansen, R., Gerstein, M., and Zhao, H. Information assessment on predicting protein-protein interactions. BMC Bioinformatics 5:154 (2004).
110. Lin, Y. C., Jhunjhunwala, S., Benner, C., et al. A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate. Nature Immunology 11:635 (2010).
111. Little, J. W. and Shepley, D. P. Integrated analysis of multiple data sources reveals modular structure of biological networks. Biochemical and Biophysical Research Communications 345:302 (2006).
112. Little, J. W., Shepley, D. P., and Wert, D. W. Robustness of a gene regulatory circuit. EMBO Journal 18:4299 (1999).
113. Liu, Y., Kim, I., and Zhao, H. Protein interaction predictions from diverse sources. Drug Discovery Today 13:409 (2008).
114. Liu, Y., Liu, N., and Zhao, H. Inferring protein-protein interactions through high-throughput data from diverse organisms. Bioinformatics 21:3279 (2005).
115. Lu, H., Shi, B., Wu, G., et al. Integrated analysis of multiple data sources reveals modular structure of biological networks. Biochemical and Biophysical Research Communications 345:302 (2006).
116. Lu, X., Jain, V., Finn, P. W., and Perkins, D. L. Hubs in biological interaction networks exhibit low changes in expression in experimental asthma. Molecular Systems Biology 3:98 (2007).
117. Luo, J., Emanuele, M. J., Li, D., et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137:835 (2009).
118. Lynch, M. and Conery, J. S. The evolutionary fate and consequences of duplicate genes. Science 290:1151 (2000).
119. Lynch, M. and Force, A. The probability of duplicate-gene preservation by subfunctionalization. Genetics 154:459 (2000).
120. Ma, W., Lai, L., Ouyang, Q., and Tang, C. Robustness and modular design of the Drosophila segment polarity network. Molecular Systems Biology 2:70 (2006).
121. Margolin, A. A., Basso, K., Wiggins, C., et al. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7:S7 (2006).
122. Markson, G., Kiel, C., Hyde, R., et al. Analysis of the human E2 ubiquitin conjugating enzyme protein interaction network. Genome Research 19:1905 (2009).
123. Matthews, L. R., Vaglio, P., Reboul, J., et al. Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs." Genome Research 11:2120 (2001).
124. McLysaght, A., Hokamp, K., and Wolfe, K. Extensive genomic duplication during early chordate evolution. Nature Genetics 31:200 (2002).
125. Middendorf, M., Ziv, E., and Wiggins, C. Inferring network mechanisms: The Drosophila melanogaster protein interaction network. Proceedings of the National Academy of Sciences USA 102:3192 (2005).
126. Miller, G. Information Theory in Psychology II-B, chap. Note on the bias of information estimates, pp. 95-100. Glencoe, IL: Free Press (1955).
127. Milo, R., Shen-Orr, S., Itzkovitz, S., et al. Network motifs: Simple building blocks of complex networks. Science 298:824 (2002).
128. Molloy, M. and Reed, B. A critical point for random graphs with a given degree sequence. Random Structures and Algorithms 6:161 (1995).
129. Molloy, M. and Reed, B. The size of the giant component of a random graph with a given degree sequence. Combinatorics, Probability and Computing 7:295 (1998).
130. Montoya, J. and Solé, R. Topological properties of food webs: From real data to community assembly models. Oikos 102:614 (2003).
131. Mukhtar, M. S., Carvunis, A.-R., Dreze, M., et al. Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 29:596 (2011).
132. Murphy, K. and Mian, S. Modelling Gene Expression Data Using Dynamic Bayesian Networks. Tech. report, University of California (1999).
133. Navlakha, S. and Kingsford, C. Network archaeology: Uncovering ancient networks from present-day interactions. PLoS Computational Biology 7:e1001119 (2011).
134. Nechaev, S. and Severinov, K. Inhibition of Escherichia coli RNA polymerase by bacteriophage T7 gene 2 protein. Journal of Molecular Biology 289:815 (1998).
135. Neidhardt, F. C., Curtiss, R., Ingraham, J. L., et al., Eds. Escherichia coli and Salmonella: Cellular and Molecular Biology. American Society for Microbiology (1996).
136. Newman, M., Strogatz, S. H., and Watts, D. J. Random graphs with arbitrary degree distributions and their applications. Physical Review E 64:026118 (2001).
137. Newman, M. E. J. Analysis of weighted networks. Physical Review E 70:056131 (2004).
138. Newman, M. E. J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences USA 103:8577 (2006).
139. Newman, M. E. J. Random graphs with clustering. Physical Review Letters 103:058701 (2009).
140. Newman, M. E. J. and Girvan, M. Finding and evaluating community structure in networks. Physical Review E 69:026113 (2004).
141. Nitsch, D., Goncalves, J. P., Ojeda, F., de Moor, B., and Moreau, Y. Candidate gene prioritization by network analysis of differential expression using machine learning approaches. BMC Bioinformatics 11:460 (2010).
142. Nitsch, D., Tranchevent, L.-C., Thienpont, B., et al. Network analysis of differential expression for the identification of disease-causing genes. PLoS ONE 4:e5526 (2009).
143. Oppenheim, A. B., Kobiler, O., Stavans, J., Court, D. L., and Adhya, S. Switches in bacteriophage lambda development. Annual Review of Genetics 39:409 (2005).
144. Orth, J. D., Thiele, I., and Palsson, B. O. What is flux balance analysis? Nature Biotechnology 28:245 (2010).
145. Oti, M., Snel, B., Huynen, M. A., and Brunner, H. G. Predicting disease genes using protein-protein interactions. Journal of Medical Genetics 43:691 (2006).
146. Paladugu, S. R., Zhao, S., Ray, A., and Raval, A. Mining protein networks for synthetic genetic interactions. BMC Bioinformatics 9:426 (2008).
147. Palsson, B. O. Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, Cambridge, MA (2006).
148. Papp, B., Pal, C., and Hurst, L. D. Dosage sensitivity and the evolution of gene families in yeast. Nature 424:194 (2003).
149. Park, C. Y., Hess, D. C., Huttenhower, C., and Troyanskaya, O. G. Simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway components. PLoS Computational Biology 6:e1001009 (2010).
150. Park, J., Lee, D.-S., Christakis, N. A., and Barabási, A.-L. The impact of cellular networks on disease comorbidity. Molecular Systems Biology 5:262 (2009).
151. Park, S., Yang, J.-S., Shin, Y.-E., et al. Protein localization as a principal feature of the etiology and comorbidity of genetic diseases. Molecular Systems Biology 7:494 (2011).
152. Pazos, F. and Valencia, A. In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins 47:219 (2002).
153. Perrin, B.-E., Ralaivola, L., Mazurie, A., et al. Gene networks inference using dynamic Bayesian networks. Bioinformatics 19, Suppl. 2:ii138 (2003).
154. Petti, A. and Church, G. M. A network of transcriptionally coordinated functional modules in Saccharomyces cerevisiae. Genome Research 15:1298 (2005).
155. Polya, G. Probabilities in proofreading. The American Mathematical Monthly 83:42 (1976).
156. Portis, T., Grossman, W. J., Harding, J. C., Hess, J. L., and Ratner, L. Analysis of p53 inactivation in a human T-cell leukemia virus type 1 Tax transgenic mouse model. Journal of Virology 75:2185 (2001).
157. Przulj, N. and Higham, D. Modeling protein-protein interaction networks via a stickiness index. Journal of the Royal Society Interface 3:711 (2006).
158. Ptashne, M. Regulation of transcription: From lambda to eukaryotes. Trends in Biochemical Sciences 30:275 (2005).
159. Qi, Y., Balem, F., Faloutsos, C., Klein-Seetharaman, J., and Bar-Joseph, Z. Protein complex identification by supervised graph local clustering. Bioinformatics 24:i250 (2008).
160. Qi, Y., Bar-Joseph, Z., and Klein-Seetharaman, J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 63:490 (2006).
161. Rambaldi, D., Georgi, F. M., Capuani, F., Ciliberto, A., and Ciccarelli, F. D. Low duplicability and network fragility of cancer genes. Trends in Genetics 24:427 (2008).
162. Rangel, C., Angus, J., Ghahramani, Z., et al. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics 20:1361 (2004).
163. Raval, A. Some asymptotic properties of duplication graphs. Physical Review E 68:066119 (2003).
164. Reguly, T., Breitkreutz, A., Boucher, L., and Breitkreutz, B.-J. Comprehensive curation and analysis of global interaction networks in S. cerevisiae. Journal of Biology 5:11 (2006).
165. Riley, R., Lee, C., Sabatti, C., and Eisenberg, D. Inferring protein domain interactions from databases of interacting proteins. Genome Biology 6:R89 (2005).
166. Roach, J. C., Glusman, G., Smit, A. F. A., et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328:636 (2010).
167. Sawhill, B. K. Genetic Function Analysis. Santa Fe Institute Working Paper (1995).
168. Scholl, C., Fröhling, S., Dunn, I. F., et al. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 137:821 (2009).
169. Schuldiner, M., Collins, S. R., Thompson, N. J., et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123:507 (2005).
170. Segré, D., DeLuna, A., Church, G. M., and Kishony, R. Modular epistasis in yeast metabolism. Nature Genetics 37:77 (2005).
171. Segré, D., Vitkup, D., and Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences USA 99:15112 (2002).
172. Shannon, C. E. and Weaver, W. The Mathematical Theory of Communication. University of Illinois Press, Champaign (1963).
173. Sharan, R., Ideker, T., Kelley, B., Shamir, R., and Karp, R. M. Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Journal of Computational Biology 12:835 (2005).
174. Shou, C., Bhardwaj, N., Lam, H. Y. K., et al. Measuring the evolutionary rewiring of biological networks. PLoS Computational Biology 7:e1001050 (2011).
175. Skrabanek, L., Saini, H. K., Bader, G. D., and Enright, A. J. Computational prediction of protein-protein interactions. Molecular Biotechnology 38:1 (2008).
176. Smits, W. K., Kuipers, O. P., and Veening, J.-W. Phenotypic variation in bacteria: The role of feedback regulation. Nature Reviews Microbiology 4:259 (2006).
177. Snitkin, E. S. and Segré, D. Epistatic interaction maps relative to multiple metabolic phenotypes. PLoS Genetics 7:e1001294 (2011).
178. Solé, R. and Pastor-Satorras, R. Complex networks in genomics and proteomics. In S. Bornholdt and H. G. Schuster, Eds., Handbook of Graphs and Networks: From the Genome to the Internet. Wiley-VCH, Berlin (2003).
179. Stark, C., Breitkreutz, B. J., Reguly, T., et al. BIOGRID: A general repository for interaction datasets. Nucleic Acids Research 34:D535 (2012).
180. Stelzl, U., Worm, U., Lalowski, M., et al. A human protein-protein interaction network: A resource for annotating the proteome. Cell 122:957 (2005).
181. Taylor, I. W., Linding, R., Warde-Farley, D., et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnology 27:199 (2009).
182. Taylor, J., Braasch, I., Frickey, T., Meyer, A., and van de Peer, Y. Genome duplication, a trait shared by 22,000 species of ray-finned fish. Genome Research 13:382 (2003).
183. Taylor, J. and Raes, J. Duplication and divergence: The evolution of new genes and old ideas. Annual Reviews in Genetics 38:615 (2004).
184. Tong, A. H. Y., Lesage, G., Bader, G. D., et al. Global mapping of the yeast genetic network. Science 303:808 (2004).
185. Uetz, P., Giot, L., Cagney, G., et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403:623 (2000).
186. van Dongen, S. Graph Clustering by Flow Simulation. Ph.D. thesis, University of Utrecht (2000).
187. van Driel, M. A., Bruggeman, J., Vriend, G., Brunner, H. G., and Leunissen, J. A. M. A text-mining analysis of the human phenome. European Journal of Human Genetics 14:535 (2006).
188. van Nimwegen, E. Scaling laws in the functional content of genomes. Trends in Genetics 9:479 (2003).
189. Varma, A. and Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wildtype Escherichia coli W3110. Applied and Environmental Microbiology 60:3724 (1994).
190. Vázquez, A., Flammini, A., Maritan, A., and Vespignani, A. Modeling of protein interaction networks. ComPlexUs 1:38 (2003).
191. Vision, T., Brown, D., and Tanksley, S. The origins of genomic duplication in Arabidopsis. Science 290:2114 (2000).
192. von Dassow, G., Meir, E., Munro, E. M., and Odell, G. M. The segment polarity network is a robust developmental module. Nature 406:188 (2000).
193. Wagner, A. The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Molecular Biology and Evolution 18:1283 (2001).
194. Wagner, A. How the global structure of protein interaction networks evolves. Proceedings of the Royal Society of London B 270:457 (2003).
195. Walhout, A. J., Sordella, R., Lu, X., et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287:116 (2000).
196. Wang, J., Zhang, S., Wang, Y., Chen, L., and Zhang, X.-S. Diseaseaging network reveals significant roles of aging genes in connecting genetic diseases. PLoS Computational Biology 5:e1000521 (2009).
197. Wang, W., Yu, H., and Long, M. Duplication-degeneration as a mechanism of gene fission and the origin of new genes in Drosophila species. Nature Genetics 36:523 (2004).
198. Wass, M. N., Fuentes, G., Pons, C., Pazos, F., and Valencia, A. Towards the prediction of protein interaction partners using physical docking. Molecular Systems Biology 7:469 (2011).
199. Watts, D. and Strogatz, S. Collective dynamics of 'small-world' networks. Nature 393:440 (1998).
200. Wilks, S. Mathematical Statistics. Wiley, New York (1962).
201. Wolfe, K. and Shields, D. Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387:708 (1997).
202. Wong, S. L., Zhang, L. V., Tong, A. H. Y., et al. Combining biological networks to predict genetic interactions. Proceedings of the National Academy of Sciences 101:15682 (2004).
203. Wu, G., Feng, X., and Stein, L. A human functional protein interaction network and its application to cancer data analysis. Genome Biology 11:R53 (2010).
204. Wu, X., Jiang, R., Zhang, M. Q., and Li, S. Network-based global inference of human disease genes. Molecular Systems Biology 4:189 (2008).
205. Xia, J., Sun, J., Jia, P., and Zhao, Z. Do cancer proteins really interact strongly in the human protein-protein interaction network? Computational Biology and Chemistry 35:121 (2011).
206. Yu, H., Luscombe, N. M., Lu, H. X., et al. Annotation transfer between genomes: Protein-protein interologs and protein-DNA regulogs. Genome Research 14:1107 (2004).
207. Zhu, X.-M., Yin, L., Hood, L., and Ao, P. Robustness, stability and efficiency of phage -genetic switch: Dynamical structure analysis. Journal of Bioinformatics and Computational Biology 2:785 (2004).
208. Zou, M. and Conzen, S. D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21:71 (2005).