Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

Journal of Clinical Bioinformatics

Volumn 1, Issue 1, 2011, Pages

  Dimitrakopoulou, Konstantina ^a   Tsimpouris, Charalampos ^b   Papadopoulos, George ^b   Pommerenke, Claudia ^c   Wilk, Esther ^c   Sgarbas, Kyriakos N ^b   Schughart, Klaus ^c,d   Bezerianos, Anastasios ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c HELMHOLTZ CENTRE FOR INFECTION RESEARCH   (Germany)

^d UNIVERSITY OF VETERINARY MEDICINE HANNOVER   (Germany)

Author keywords

Gene Regulatory Network; Immune System; Influenza A; Time Varying Dynamic Bayesian Network

Indexed keywords

EID: 84873074998     PISSN: None     EISSN: 20439113     Source Type: Journal    
DOI: 10.1186/2043-9113-1-27     Document Type: Article

References (59)

1. Gardner TS, Faith JJ. Reverse-engineering transcription control networks. Physics of Life Reviews 2005, 2:65-88.
2. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol 2007, 3:78. 1828749, 17299415.
3. Markowetz F, Spang R. Inferring cellular networks-a review. BMC Bioinformatics 2007, 8(Suppl 6):S5. 10.1186/1471-2105-8-S6-S5, 2230506, 18269699.
4. Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R. Gene regulatory network inference: Data integration in dynamic models-A review. Bio Systems 2008, 96:86-103.
5. Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Brief Bioinform 2009, 10:408-423.
6. Shmulevich I, Dougherty E, Zhang W. From boolean to probabilistic boolean networks as models of genetic regulatory networks. Proc IEEE 2002, 90:1778-1792.
7. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. J Comp Biol 2000, 7:601-620.
8. Perrin BE, Ralaivola L, Mazurie A, Bottani S, Mallet J, d'Alché-Buc F. Gene networks inference using dynamic bayesian networks. Bioinformatics 2003, 19:ii138-48. 10.1093/bioinformatics/btg1071, 14534183.
9. Yu J, Smith V, Wang P, Hartemink A, Jarvis E. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004, 20:3594-603. 10.1093/bioinformatics/bth448, 15284094.
10. D'haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury. Proc Pacific Symp Biocomputing 1999, 41-52.
11. Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ. Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell 2004, 15:3841-3862. 10.1091/mbc.E03-11-0794, 491841, 15169868.
12. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nature Reviews Molecular Cell Biology 2008, 9:770-780. 10.1038/nrm2503, 18797474.
13. Bansal M, Della Gatta G, di Bernardo D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 2006, 22:815-22. 10.1093/bioinformatics/btl003, 16418235.
14. Greenfield A, Madar A, Ostrer H, Bonneau R. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models. PLoS One 2010, 5:e13397. 10.1371/journal.pone.0013397, 2963605, 21049040.
15. Hirose O, Yoshida R, Imoto S, Yamaguchi R, Higuchi T, Charnock-Jones DS, Print C, Miyano S. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics 2008, 24:932-42. 10.1093/bioinformatics/btm639, 18292116.
16. Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran E, Gaiba A, Wild DL, Falciani F. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics 2004, 20:1361-72. 10.1093/bioinformatics/bth093, 14962938.
17. Opgen-Rhein R, Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 2007, 1:37. 10.1186/1752-0509-1-37, 1995222, 17683609.
18. Shimamura T, Imoto S, Yamaguchi R, Fujita A, Nagasaki M, Miyano S. Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Syst Biol 2009, 3:41. 10.1186/1752-0509-3-41, 2686685, 19386091.
19. Zoppoli P, Morganella S, Ceccarelli M. TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 2010, 11:154. 10.1186/1471-2105-11-154, 2862045, 20338053.
20. Talih M, Hengartner N. Structural learning with time-varying components: Tracking the crosssection of financial time series. J Royal Stat Soc 2005, B67:321-341.
21. Hanneke S, Xing EP. Discrete temporal models of social networks. In Workshop on Statistical Network Analysis 2006, ICML06.
22. Guo F, Hanneke S, Fu W, Xing EP. Recovering temporally rewiring networks: A model-based approach. The 24th International Conference of Machine Learning, 2007, New York, Association for Computing Machinery
23. Xuan X, Murphy K. Modeling changing dependency structure in multivariate time series. In Proceedings of the 24th International Conference on Machine Learning 2007, 1055-1062. Corvallis, OR, USA.
24. Robinson J, Hartemink A. Non-stationary dynamic bayesian networks. 2008, 1369-1376. NIPS '08: Neural Information Processing Systems.
25. Ahmed A, Xing EP. Recovering time-varying networks of dependencies in social and biological studies. PNAS 2009, 106:11878-11883. 10.1073/pnas.0901910106, 2704856, 19570995.
26. Song L, Kolar M, Xing E. Time-varying dynamic Bayesian networks. Advances in Neural Information Processing Systems 22 (NIPS 2009)
27. Tumpey TM, Garcia-Sastre A, Taubenberger JK, Palese P, Swayne DE, Pantin-Jackwood MJ, Schultz-Cherry S, Solorzano A, Van Rooijen N, Katz JM, Basler CF. Pathogenicity of Influenza viruses with genes from the 1918 pandemic virus: functional roles of alveolar macrophages and neutrophils in limiting virus replication and mortality in mice. J Virol 2005, 79:14933-14944. 10.1128/JVI.79.23.14933-14944.2005, 1287592, 16282492.
28. Kash JC, Tumpey TM, Proll SC, Carter V, Perwitasari O, Thomas MJ, Basler CF, Palese P, Taubenberger JK, Garcia-Sastre A, Swayne DE, Katze MG. Genomic analysis of increased host immune and cell death responses induced by 1918 Influenza virus. Nature 2006, 443:578-581. 2615558, 17006449.
29. Vidal SM, Malo D, Marquis JF, Gros P. Forward genetic dissection of immunity to infection in the mouse. Annu Rev Immunol 2008, 26:81-132. 10.1146/annurev.immunol.26.021607.090304, 17953509.
30. Srivastava B, Blazejewska P, Hessmann M, Bruder D, Geffers R, Mauel S, Gruber AD, Schughart K. Host genetic background strongly influences the response to influenza a virus infections. PLoS One 2009, 4:e4857. 10.1371/journal.pone.0004857, 2654507, 19293935.
31. Alberts R, Srivastava B, Wu H, Viegas N, Geffers R, Klawonn F, Novoselova N, do Valle TZ, Panthier JJ, Schughart K. Gene expression changes in the host response between resistant and susceptible inbred mouse strains after influenza A infection. Microbes Infect 2010, 12:309-18. 10.1016/j.micinf.2010.01.008, 20114087.
32. Song L, Kolar M, Xing E. KELLER: estimating time-varying interactions between genes. Bioinformatics 2009, 25:i128-i136. 10.1093/bioinformatics/btp192, 2687946, 19477978.
33. Luscombe N, Babu M, Yu H, Snyder M, Teichmann S, Gerstein M. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 2004, 431:308-312. 10.1038/nature02782, 15372033.
34. Guthke R, Möller U, Hoffmann M, Thies F, Töpfer S. Dynamic Network Reconstruction from Gene Expression Data Applied to Immune Response during Bacterial Infection. Bioinformatics 2005, 21:1626-1634. 10.1093/bioinformatics/bti226, 15613398.
35. Inoue LY, Neira M, Nelson C, Gleave M, Etzioni R. Cluster-based network model for time-course gene expression data. Biostatistics 2007, 8:507-525.
36. Shiraishi Y, Kimura S, Okada M. Inferring cluster-based networks from differently stimulated multiple time-course gene expression data. Bioinformatics 2010, 26:1073-1081. 10.1093/bioinformatics/btq094, 2853688, 20223837.
37. Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nature Reviews Genetics 2004, 5:101-113. 10.1038/nrg1272, 14735121.
38. Petti AA, Church GM. A network of transcriptionally coordinated functional modules in Saccharomyces cerevisiae. Genome Research 2005, 15:1298-1306. 10.1101/gr.3847105, 1199545, 16109970.
39. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK. A comparison of background correction methods for two-colour microarrays. Bioinformatics 2007, 23:2700-2707. 10.1093/bioinformatics/btm412, 17720982.
40. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5:R80. 10.1186/gb-2004-5-10-r80, 545600, 15461798.
41. Fujita A, Sato JR, Ferreira CE, Sogayar MC. GEDI: a user-friendly toolbox for analysis of large-scale gene expression data. BMC Bioinformatics 2007, 8:457. 10.1186/1471-2105-8-457, 2194737, 18021455.
42. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc 2009, 4:44-57.
43. Filkov V. Identifying gene regulatory networks from gene expression data. Handbook of Computational Molecular Biology 2005, 27.1-27.29. CRC Press, Chapman & Hall, Aluru.
44. Chen G, Jaradat SA, Banerjee N, Tanaka TS, Ko MSH, Zhang MQ. Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data. Statistica Sinica 2002, 12:241-262.
45. Dunn J. Well separated clusters and optimal fuzzy partitions. J Cybernetics 1974, 4:95-104.
46. Fu WJ. Penalized regression: the Bridge versus the Lasso. Journal of Computational and Graphical Statistics 1998, 7:397-416.
47. Lin CY, Chin CH, Wu HH, Chen SH, Ho CW, Ko MT. Hubba: hub objects analyzer--a framework of interactome hubs identification for network biology. Nucleic Acids Res 2008, 36:W438-W443. 10.1093/nar/gkn257, 2447731, 18503085.
48. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-504. 10.1101/gr.1239303, 403769, 14597658.
49. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PloS Computational Biology 2007, 3:e59. 10.1371/journal.pcbi.0030059, 1853125, 17447836.
50. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008, 4:682-690. 10.1038/nchembio.118, 18936753.
51. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000, 28:27-30. 10.1093/nar/28.1.27, 102409, 10592173.
52. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature 1998, 393:440-442. 10.1038/30918, 9623998.
53. Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, Gardy JL, Roche FM, Chan TH, Shah N, Lo R, Naseer M, Que J, Yau M, Acab M, Tulpan D, Whiteside MD, Chikatamarla A, Mah B, Munzner T, Hokamp K, Hancock RE, Brinkman FS. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol Syst Biol 2008, 4:218. 2564732, 18766178.
54. Fulton DL, Sundararajan S, Badis G, Hughes TR, Wasserman WW, Roach JC, Sladek R. TFCat: the curated catalog of mouse and human transcription factors. Genome biology 2009, 10:R29. 10.1186/gb-2009-10-3-r29, 2691000, 19284633.
55. Honda K, Yanai H, Negishi H, Asagiri M, Sato M, Mizutani T, Shimada N, Ohba Y, Takaoka A, Yoshida N, Taniguchi T. IRF-7 is the master regulator of type-I interferon-dependent immune responses. Nature 2005, 434:772-777. 10.1038/nature03464, 15800576.
56. Filén S, Ylikoski E, Tripathi S, West A, Björkman M, Nyström J, Ahlfors H, Coffey E, Rao KV, Rasool O, Lahesmaa R. Activating transcription factor 3 is a positive regulator of human IFNG gene expression. J Immunol 2010, 184:4990-9. 10.4049/jimmunol.0903106, 20304822.
57. Yoneyama M, Fujita T. RNA recognition and signal transduction by RIG-I-like receptors. Immunol Rev 2009, 227:54-65. 10.1111/j.1600-065X.2008.00727.x, 19120475.
58. Kaisho T, Akira S. Toll-like receptors as adjuvant receptors. Biochim Biophys Acta 2002, 1589:1-13. 10.1016/S0167-4889(01)00182-3, 11909637.
59. Shaw MH, Reimer T, Kim YG, Nuñez G. NOD-like receptors (NLRs): bona fide intracellular microbial sensors. Curr Opin Immunol 2008, 20:377-82. 10.1016/j.coi.2008.06.001, 2572576, 18585455.