Statistical approaches to genome-wide biological networks

Biochip Journal

Volumn 3, Issue 3, 2009, Pages 190-202

  Do, Jin Hwan ^a,b   Miyano, Satoru ^b   Choi, Dong Kug ^a  

^a Konkuk University   (South Korea)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

Bayesian networks; Boolean networks; Flux balance analysis; Petri nets; State space approach

Indexed keywords

EID: 70350339696     PISSN: 19760280     EISSN: 20927843     Source Type: Journal    
DOI: None     Document Type: Article

References (61)

1. Kitano, H. Systems biology: a brief overview. Science 295, 1662-1664(2002).
2. Dhar, P.K., Zhu, H. & Mishra, S.K. Computational approach to systems biology: from fraction to integration and beyond. IEEE Trans. Nanobiosci. 3, 144-152(2004).
3. Westerhoff, H.V. & Palsson, B.O. The evolution of molecular biology into systems biology. Nat. Biotechnol. 22, 1249-1252(2004).
4. Prifti, E., Zucker, J.-D., Clement, K. & Henegar, C. FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics 24, 2636-2638(2008).
5. Junker, B.H., Koschützki, D. & Schreiber, F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 7, 219(2006).
6. Shen-Orr, S.S., Milo, R, Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 64-68(2002).
7. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824-827(2002).
8. Mangan, S. & Alon, U. Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. 100, 11980-11985(2003).
9. Wolf, D.M. & Arkin, A.P. Motifs, modules and games in bacteria. Curr. Opin. Microbiol. 6, 125-134(2003).
10. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113(2004).
11. Yu, H. & Gerstein, M. Genomic analysis of the hierarchical structure of regulatory networks. Proc. Natl. Acad. Sci. 103, 14724-14731(2006).
12. Martínez-Antonio, A., Janga, S.C. & Thieffry, D. Functional organization of Escherichia coli transcriptional regulatory network. J. Mol. Biol. 381, 238-247(2008).
13. Albert, R. Scale-free networks in cell biology. J. Cell Sci. 118, 4947-4957(2005).
14. Aldana, M., Balleza, F., Kauffman, S. & Resendiz, O. Robustness and evolvability in genetic regulatory networks. J. Theor. Biol. 245, 433-448(2007).
15. Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. & Guthke, R. Gene regulatory network inference: data integration in dynamic models-A review. BioSystems doi:10.1016/j.biosystems(2009).
16. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A. & Nolan, G.P. Casual protein-signaling networks derived from multiparameter single-cell data. Science 308, 523-529(2005).
17. Sachs, K., Itani, S., Fitzgerald, J., Wilie, L. & Schoeberl, B. Learning cyclic signaling pathway structures while minimizing data requirements. Pac. Symp. Biocomput. 63-74(2009).
18. Kauffman, S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437-467(1969).
19. Martin, S., Zhang, Z., Martino, A. & Faulon, J.L. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23, 866-874(2007).
20. Li, P., Perkins, E.J., Gong, P. & Deng, Y. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics 8(suppl. 7), S13(2007).
21. Lähdesmäki, H., Hautaniemi, S., Shmulevich, I. & Yli-Harja, O. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Processing 86, 814-834(2006).
22. Hashimoto, R.F. et al. Growing genetic regulatory networks from seed genes. Bioinformatics 20, 1241-1247 (2004).
23. Pal, R., Datta, A., Bittner, M.L. & Dougherty, E.R. Intervention in context-sensitive probabilistic Boolean networks. Bioinformatics 21, 1211-1218(2005).
24. Li, H. & Zhan, M. Systematic intervention of transcription for identifying network response to disease and cellular phenotypes. Bioinformatics 22, 96-102(2006).
25. Wu, F.-X. Gene regulatory network modeling: a statespace approach. Int. J. Data Min. and Bioinform. 2, 1-14(2008).
26. Chen, C.T. Linear system theory and design. 3rd ed., Oxford University Press, New York(1999).
27. Hirose, O. et al. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics 24, 932-942(2008).
28. Chen, K.C., Wang, T.Y., Tseng, H.H., Huang, C.Y. & Kao, C.Y. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 21, 2883-2890 (2005).
29. Climescu-Haulica, A. & Quirk, M.D. A stochastic differential equation model for transcriptional regulatory networks. BMC Bioinformatics 8(suppl. 5), S4(2007).
30. Hazbun, T.R. & Fields, S. Networking proteins in yeast. Proc. Natl. Acad. Sci. 98, 4277-4278(2001).
31. Thomas, A., Cannings, R., Monk, N. & Cannings, C. On the structure of protein-protein interactions networks. Biochem. Soc. Trans. 31, 1491-1496 (2003).
32. Poyatos, J. & Hurst, L. How biologically relevant are interaction based modules in protein networks. Genome Biol. 5, R93(2004).
33. Gagneur, J., David, L. & Steinmetz, L. Capturing cellular machines by systematic screens of protein complexes. Trends Microbio. 14, 336-339(2006).
34. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449-453(2003).
35. Segal, E., Wang, H. & Koller, D. Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(suppl. 1), i264-i271(2003).
36. Chiang, T., Scholtens, D., Sarkar, D. & Gentleman, R. Coverage and error models of protein-protein interaction data by directed graph analysis. Genome Biol. 8, R186(2007).
37. Nariai, N., Tamada, Y., Imoto, S. & Miyano, S. Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 21(suppl. 2), ii206-ii212(2005).
38. Kanehisa, M. et al. From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res. 34, 354-357(2006).
39. Green, M.L. &Karp, P.D. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5, 76(2004).
40. Geng, B. et al. Comparison of reversible-jump Markovchain-Monte-Carlo learning approach with other methods for missing enzyme identification. J. Biomed. Inform. 41, 272-281(2008).
41. Price, N.D., Reed, J.L. & Palsson, B.O. Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886-897 (2004).
42. Bonarius, H.P.J., Schmid, G. & Tramper, J. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol. 15, 308-314(1997).
43. Kauffman, K.J., Prakash, P. & Edwards, J.S. Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491-496(2003).
44. Herrgård, M.J., Fong, S.S. & Palsson B. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS. Comput. Biol. 2, e72(2006).
45. Kestler, H.A., Wawra, C., Kracher, B. & Kühl, M. Network modeling of signal transduction: establishing the global view. BioEssays 30, 1110-1125(2008).
46. Yang, L., Lin, C. & Liu, Z.R. P68 RNA helicase mediates PDGF-induced epithelial mesenchymal transition by displacing Axin from beta-catenin. Cell 127, 139-155(2006).
47. Choi, C. et al. Consistent re-modeling of signal pathways and its implementation in the TRANSPATH database. Genome Inform. 15, 244-254(2004).
48. Froehlich, H., Fellmann, M., Sueltmann, H., Poustka, A. & Beissbarth, T. Large scale statistical inference of signaling pathways from RNAi and microarray data. BMC Bioinformatics 8, 386(2007).
49. Matsuno, H., Li, C. & Miyano, S. Petri net based descriptions for systematic understanding of biological pathways. IEICE Trans Fundam Electron Commun Comput. Sci. E89-A, 3166-3174(2006).
50. Matsuno, H., Doi, A., Nagasaki, M. & Miyano, S. Hybrid Petri net representation of gene regulatory network. Pac. Symp. Biocomput. 338-352(2000).
51. Matsuno, H. et al. Biopathway representation and simulation on hybrid functional Petri net. In Silico Biology 3, 389-404(2003).
52. Matsuno. H. et al. Boundary formation by Notch signaling in Drosophila multicellular systems: experimental observations and a gene network modeling by genomic object net. Pac. Symp. Biocomput. 152-163(2003).
53. Peleg, M.P., Rubin, D. & Altman, R.B. Using Petri net tools to study properties and dynamics of biological systems. J. Am. Med. Inform. Assoc. 12, 181-199 (2005).
54. Nagasaki, M., Doi, A., Matsuno, H. & Miyano, S. Computational modeling of biological processes with Petri net-based architecture. In Bioinformatics and Technologies ed. Y.P. Chen. 179-242(2005).
55. Troncale, S., Tahi, F., Campard, D., Vannier, J.P. & Guespin, J. Modeling and simulation with hybrid functional Petri nets of the role of interleukin-6 in human early haematopoiesis. Pac. Symp. Biocomput. 427-438 (2006).
56. Gilbert, D. & Hiner, M. From Petri nets to differential equations-An integrative approach for biochemical network analysis. Proc. ICATPN. LNCS 4024, 181-200 (2006).
57. Nagasaki, M., Doi, A., Matsuno, H. & Miyano, S. A versatile Petri net based architecture for modeling and simulation of complex biological process. Genome Informatics 15, 180-197(2004).
58. Kristensen, L., Christensen, S. & Jensen, K. The practitioner's guide to coloured Petri nets. Int. J. Software Tools Technol. Transfer 2, 98-132(1998).
59. Saito, A., Nagasaki, M., Doi, A., Ueno, K. & Miyano, S. Cell fate simulation model of gustatory neurons with micro RNAs double-negative feedback loop by hybrid functional Petri net with extension. Genome Inform. 17, 100-111(2006).
60. Sackman, A., Heiner, M. & Koch, I. Application of Petri net based analysis techniques to signal transduction pathways. BMC Bioinformatics 7, 482(2006).
61. Han, J.-D. Understanding biological functions through molecular networks. Cell Res. 18, 224-237(2008).