Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Fröhlich, Holger ^a   Sahin, Özgür ^a   Arlt, Dorit ^a   Bender, Christian ^a   Beißbarth, Tim ^a,b  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX BIOLOGICAL SYSTEMS; HUMAN BREAST CANCER CELLS; MULTIPLE INTERVENTIONS; PROTEIN EXPRESSIONS; PROTEIN SIGNALING NETWORKS; REVERSE PHASE PROTEIN ARRAYS; RNA INTERFERENCE; SIGNALING NETWORKS;

BAYESIAN NETWORKS; COMPLEX NETWORKS; MONOCLONAL ANTIBODIES; NUCLEIC ACIDS; PERTURBATION TECHNIQUES; SIGNALING;

PROTEINS;

CYCLIN D1; CYCLIN DEPENDENT KINASE 4; CYCLIN DEPENDENT KINASE 6; CYCLIN E; EPIDERMAL GROWTH FACTOR RECEPTOR; EPIDERMAL GROWTH FACTOR RECEPTOR 2; EPIDERMAL GROWTH FACTOR RECEPTOR 3; ESTROGEN RECEPTOR ALPHA; MITOGEN ACTIVATED PROTEIN KINASE 1; MITOGEN ACTIVATED PROTEIN KINASE KINASE 1; MYC PROTEIN; PROTEIN KINASE B; PROTEIN P21; PROTEIN P27; SOMATOMEDIN C RECEPTOR; TRASTUZUMAB; PROTEIN;

ARTICLE; BAYESIAN LEARNING; CANCER CELL; COMPUTER PROGRAM; CYTOLYSIS; GENE INTERACTION; HUMAN; HUMAN CELL; MATHEMATICAL MODEL; NETWORK LEARNING; PROBABILITY; PROTEIN DATABASE; PROTEIN EXPRESSION; PROTEIN MICROARRAY; PROTEIN PHOSPHORYLATION; RNA INTERFERENCE; SENSITIVITY AND SPECIFICITY; SIGNAL TRANSDUCTION; SIMULATION; TRANSCRIPTION REGULATION; BAYES THEOREM; BIOLOGY; CHEMISTRY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; METABOLISM; METHODOLOGY;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; PROTEINS; SIGNAL TRANSDUCTION;

EID: 71749088202     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-322     Document Type: Article

References (44)

1. Fire A, Xu S, Montgomery M, Kostas S, Driver S, Mello C. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998, 391:806-811. 10.1038/35888, 9486653.
2. Rung J, Schlitt T, Brazma A, Freivalds K, Vilo J. Building and analysing genome-wide gene disruption networks. Bioinformatics 2002, 18(Suppl 2):S202-S210.
3. Wagner A. How to reconstruct a large genetic network from n gene perturbations in fewer than n2 easy steps. Bioinformatics 2001, 17(12):1183-1197. 10.1093/bioinformatics/17.12.1183, 11751227.
4. Aho A, Garey M, Ullman J. The Transitive Reduction of a Directed Graph. SIAM Journal on Computing 1972, 1(2):131-137.
5. Tresch A, Beißbarth T, Sültmann H, Kuner R, Poustka A, Buness A. Discrimination of direct and indirect interactions in a network of regulatory effects. J Comput Biol 2007, 14(9):1217-1228. 10.1089/cmb.2007.0085, 17990974.
6. Sachs K, Perez O, Pe'er D, Lauffenburger D, Nolan G. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 208(5721):523-529.
7. Pe'er D, Regev A, Elidan G, Friedman N. Inferring subnetworks from perturbed expression profiles. Bioinformatics 2001, 17(Suppl 1):S215-S224.
8. Pearl J. Causality: Models, Reasoning and Inference 2000, Cambridge: Cambridge University Press.
9. Gat-Viks I, Tanay A, Raijman D, Shamir R. A probabilistic methodology for integrating knowledge and experiments. J Comp Biol 2006, 13(2):165-181.
10. Rogers S, Girolami M. A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics 2005, 21(14):3131-3137. 10.1093/bioinformatics/bti487, 15879452.
11. Driessche NV, Demsar J, Booth E, Hill P, Juvan P, Zupan B, Kuspa A, Shaulsky G. Epistasis Analysis with Global Transcriptional Phenotypes. Nature Genetics 2005, 37(5):471-477. 10.1038/ng1545, 15821735.
12. Nelander S, Wang W, Nilsson B, She QB, Pratilas C, Rosen N, Gennemark P, Sander C. Models from experiments: combinatorial drug perturbations of cancer cells. Mol Syst Biol 2008, 4:216. 10.1038/msb.2008.53, 2564730, 18766176.
13. Markowetz F, Bloch J, Spang R. Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics 2005, 21(21):4026-4032. 10.1093/bioinformatics/bti662, 16159925.
14. Markowetz F, Kostka D, Troyanskaya O, Spang R. Nested Effects Models for High-dimensional Phenotyping Screens. Bioinformatics 2007, 23:i305-i312. 10.1093/bioinformatics/btm178, 17646311.
15. Tresch A, Markowetz F. Structure Learning in Nested Effects Models. Statistical Applications in Genetics and Molecular Biology 2008, 7:Article 9.
16. Fröhlich H, Fellmann M, Sültmann H, Poustka A, Beißbarth T. Large Scale Statistical Inference of Signaling Pathways from RNAi and Microarray Data. BMC Bioinformatics 2007, 8:386. 10.1186/1471-2105-8-386, 2241646, 17937790.
17. Fröhlich H, Fellmann M, Sültmann H, Poustka A, Beißbarth T. Estimating Large Scale Signaling Networks through Nested Effect Models with Intervention Effects from Microarray Data. Bioinformatics 2008, 24:2650-2656. 10.1093/bioinformatics/btm634, 2579711, 18227117.
18. Zeller C, Fröhlich H, Tresch A. A Bayesian Network View on Nested Effects Models. EURASIP Journal on Bioinformatics and Systems Biology 2009, 195272:8.
19. Vaske CJ, House C, Luu T, Frank B, Yeang CH, Lee NH, Stuart JM. A factor graph nested effects model to identify networks from genetic perturbations. PLoS Comput Biol 2009, 5:el000274.
20. Fröhlich H, Tresch A, Beissbarth T. Nested Effects Models for Learning Signaling Networks from Perturbation Data. Biometrical Journal 2009, 2(51):304-323.
21. Anchang B, Sadeh MJ, Jacob J, Tresch A, Vlad MO, Oefner PJ, Spang R. Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models. Proc Natl Acad Sci USA 2009, 106(16):6447-6452. 10.1073/pnas.0809822106, 19329492.
22. Sahin O, Löbke C, Korf U, Appelhans H, Sültmann H, Poustka A, Wiemann S, Arlt D. Combinatorial RNAi for quantitative protein network analysis. Proc Natl Acad Sci USA 2007, 104(16):6579-6584. 10.1073/pnas.0606827104, 1849961,1849961, 17420474.
23. Tibes R, Qiu Y, Lu Y, Hennessy B, Andreeff M, Mills GB, Kornblau SM. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol Cancer Ther 2006, 5(10):2512-2521. 10.1158/1535-7163.MCT-06-0334, 17041095.
24. Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, Fehrenbacher L, Slamon DJ, Murphy M, Novotny WF, Burchmore M, Shak S, Stewart SJ, Press M. Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Glin Oncol 2002, 20(3):719-726.
25. Sahin O, Fröhlich H, Löebke C, Korf U, Burmester S, Majety M, Mattern J, Schupp I, Chaouiya C, Thieffry D, Poustka A, Wiemann S, Beißbarth T, Arlt D. Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Systems Biology 2009, 3:1. 10.1186/1752-0509-3-1, 2652436, 19118495.
26. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, 36:D480-D484. 10.1093/nar/gkm882, 2238879, 18077471.
27. Tikhonov A, Arsenin V. Solutions of ill-posed problems 1977, Washington: W.H. Winston & Sons.
28. Bolstad BM, A IR, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 2003, 19:185-193. 10.1093/bioinformatics/19.2.185, 12538238.
29. Davison A, Hinkley D. Bootstrap Methods and Their Application 1997, Cambridge, UK: Cambridge University Press.
30. Alroy I, Yarden Y. The ErbB signaling network in embryogenesis and oncogenesis: signal diversification through combinatorial ligand-receptor interactions. FEBS Lett 1997, 410:83-86. 10.1016/S0014-5793(97)00412-2, 9247128.
31. Zhan L, Xiang B, Muthuswamy SK. Controlled activation of ERBB1/ErbB2 heterodimers promote invasion of three-dimensional organized epithelia in an ERBB1-dependent manner: implications for progression of ErbB2-overexpressing tumors. Cancer Res 2006, 66(10):5201-5208. 10.1158/0008-5472.CAN-05-4081, 16707444.
32. Yu B, Lane ME, Pestell RG, Albanese C, Wadler S. Downregulation of Cyclin D1 alters cdk 4- and cdk 2-specific phosphorylation of retinoblastoma protein. Mol Cell Biol Res Gommun 2000, 3(6):352-359.
33. Bryja V, Pachernik J, Vondracek J, Soucek K, Cajanek L, Horvath V, Holubcova Z, Dvorak P, Hampl A. Lineage specific composition of cyclin D-CDK4/CDK6-p27 complexes reveals distinct functions of CDK4, CDK6 and individual D-type cyclins in differentiating cells of embryonic origin. Cell Prolif 2008, 41(6):875-893. 10.1111/j.1365-2184.2008.00556.x, 2659368, 19040567.
34. Tanno S, Tanno S, Mitsuuchi Y, Altomare DA, Xiao GH, Testa JR. AKT activation up-regulates insulin-like growth factor I receptor expression and promotes invasiveness of human pancreatic cancer cells. Cancer Res 2001, 61(2):589-593.
35. Zarkowska T, Mittnacht S. Differential phosphorylation of the retinoblastoma protein by G1/S cyclin-dependent kinases. J Biol Chem 1997, 272(19):12738-12746. 10.1074/jbc.272.19.12738, 9139732.
36. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001, 292(5518):929-934. 10.1126/science.292.5518.929, 11340206.
37. Dojer N, Gambin A, Mizera A, Wilczyński B, Tiuryn J. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics 2006, 7:249. 10.1186/1471-2105-7-249, 1513402, 16681847.
38. Friedman N. The Bayesian Structural EM Algorithm. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence 1998, 129-138. Cooper GF, Moral S, Morgan Kaufman.
39. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003, 34(2):166-176. 10.1038/ng1165, 12740579.
40. Fröhlich H, Beißbarth T, Tresch A, Kostka D, Jacob J, Spang R, Markowetz F. Analyzing gene perturbation screens with nested effects models in R and bioconductor. Bioinformatics 2008, 24(21):2549-2550. 10.1093/bioinformatics/btn446, 18718939.
41. Pearl J. Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning. Proceedings of the 7th Conference of the Cognitive Science Society 1985, 329-334.
42. Cowell R, Dawid A, Lauritzen S, Spiegelhalter D. Probabilistic Networks and Expert Systems 2007, New York, USA: Springer.
43. Gelman A, Carlin J, Stern H, Rubin D. Bayesian Data Analysis 2004, Boca Raton, Florida: Chapman & Hall/CRC.
44. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J Royal Statistical Soc, Series B 1977, 39:1-38.