Identifying biological network structure, predicting network behavior, and classifying network state with high dimensional model representation (HDMR)

PLoS ONE

Volumn 7, Issue 6, 2012, Pages

  Miller, Miles A ^a,b   Feng, Xiao Jiang ^a   Li, Genyuan ^a   Rabitz, Herschel A ^a  

^a PRINCETON UNIVERSITY   (United States)

^b Massachusetts Institute of Technology Cambridge   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PHOSPHATIDYLINOSITOL 3,4 BISPHOSPHATE; PHOSPHATIDYLINOSITOL 3,4,5 TRISPHOSPHATE; PROTEIN KINASE C;

ACCURACY; ALGORITHM; ARTICLE; CALCULATION; CONTROLLED STUDY; INTERMETHOD COMPARISON; PREDICTION; PROTEIN PHOSPHORYLATION; RANDOM SAMPLING HIGH DIMENSIONAL MODEL REPRESENTATION; SENSITIVITY ANALYSIS; TRANSCRIPTION REGULATION;

ALGORITHMS; CYTOKINES; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PHOSPHOPROTEINS; REPRODUCIBILITY OF RESULTS; SIGNAL TRANSDUCTION; T-LYMPHOCYTES;

EID: 84862499505     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0037664     Document Type: Article

References (77)

1. El-Rehim D, Ball G, Pinder S, Rakha E, Paish C, et al. (2005) High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer 116: 340-350.
2. Fischer E, Zamboni N, Sauer U, (2004) High-throughput metabolic ux analysis based on gas chromatography-mass spectrometry derived 13 C constraints. Anal Biochem 325: 308-316.
3. Fahlgren N, Howell M, Kasschau K, Chapman E, Sullivan C, et al. (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2: e219.
4. Frenkel-Morgenstern M, Cohen A, Geva-Zatorsky N, Eden E, Prilusky J, et al. (2010) Dynamic Proteomics: a database for dynamics and localizations of endogenous uorescently-tagged proteins in living human cells. Nucleic Acids Res 38: D508-D512.
5. Faith J, Hayete B, Thaden J, Mogno I, Wierzbowski J, et al. (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5: e8.
6. Ciaccio M, Wagner J, Chuu C, Lauffenburger D, Jones R, (2010) Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods 7: 148-155.
7. Vignali D, (2000) Multiplexed particle-based ow cytometric assays. J Immunol Methods 243: 243-255.
8. Perez O, Nolan G, (2002) Simultaneous measurement of multiple active kinase states using polychromatic ow cytometry. Nat Biotechnol 20: 155-162.
9. Bendall S, Simonds E, Qiu P, Amir E, Krutzik P, et al. (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332: 687-696.
10. Janes K, Albeck J, Peng L, Sorger P, Lauffenburger D, et al. (2003) A High-throughput Quantitative Multiplex Kinase Assay for Monitoring Information Flow in Signaling Networks Application to Sepsis-Apoptosis. Mol Cell Proteomics 2: 463-473.
11. Miller MA, Barkal L, Jeng K, Herrlich A, et al. (2011) Proteolytic Activity Matrix Analysis (PrAMA) for simultaneous determination of multiple protease activities. Integr Biol 3: 422-438.
12. Brauer M, Yuan J, Bennett B, Lu W, Kimball E, et al. (2006) Conservation of the metabolomic response to starvation across two divergent microbes. Proc Natl Acad Sci USA 103: 19302-19307.
13. Schmelzle K, Kane S, Gridley S, Lienhard G, White F, (2006) Temporal Dynamics of Tyrosine Phosphorylation in Insulin Signaling. Diabetes 55: 2171-2179.
14. Gardner T, di Bernardo D, Lorenz D, Collins J, (2003) Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling. Science 301: 102-105.
15. di Bernardo D, Thompson M, Gardner T, Chobot S, Eastwood E, et al. (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23: 377-383.
16. Dhaeseleer P, Wen X, Fuhrman S, Somogyi R, (1999) Linear modeling of mRNA expression levels during CNS development and injury. Pacific Symposium on Biocomputing 4: 41-52.
17. Tegner J, Yeung M, Hasty J, Collins J, (2003) Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100: 5944-5949.
18. van Someren E, Vaes B, Steegenga W, Sijbers A, Dechering K, et al. (2006) Least absolute regression network analysis of the murine osteoblast differentiation network. Bioinformatics 22: 477-484.
19. Yu J, Smith V, Wang P, Hartemink A, Jarvis E, (2004) Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20: 3594-3603.
20. Woolf P, Prudhomme W, Daheron L, Daley G, Lauffenburger D, (2005) Bayesian analysis of signaling networks governing embryonic stem cell fate decisions. Bioinformatics 21: 741-753.
21. Dhaeseleer P, Liang S, Somogyi R, (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707-726.
22. Eisen M, Spellman P, Brown P, Botstein D, (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863-14868.
23. Greenfield A, Madar A, Ostrer H, Bonneau R, (2010) Dream4: Combining genetic and dynamic information to identify biological networks and dynamical models. PloS one 5: e13397.
24. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, et al. (2006) ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics 7: S7.
25. Sachs K, Perez O, Pe'er D, Lauffenburger D, Nolan G, (2005) Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. Science 308: 523-529.
26. Friedman J, (1991) Multivariate adaptive regression splines. The Annals of Statistics 19: 1-67.
27. Imoto S, Sunyong K, Goto T, Aburatani S, Tashiro K, et al. (2002) Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Bioinformatics Conference, 2002 Proceedings IEEE Computer Society pp. 219-227.
28. Srividhya J, Crampin EJ, McSharry PE, Schnell S, (2007) Reconstructing biochemical pathways from time course data. Proteomics 7: 828-838.
29. Li G, Rosenthal C, Rabitz H, (2001) High dimensional model representations. J Phys Chem A 105: 7765-7777.
30. Wang S, Georgopoulos P, Li G, Rabitz H, (2003) Random Sampling-High Dimensional Model Representation (RS-HDMR) with Nonuniformly Distributed Variables: Application to an Integrated Multimedia/Multipathway Exposure and Dose Model for Trichloroethylene. J Phys Chem A 107: 4707-4716.
31. Rabitz H, Al Ö, Shorter J, Shim K, (1999) Efficient input-output model representations. Computer Physics Communications 117: 11-20.
32. Shim K, Rabitz H, (1998) Independent and correlated composition behavior of material properties: Application to energy band gaps for the Gaα In1-α Pβ As1-β and Gaα In1-α Pβ Sbγ As1-β-γ alloys. Phys Rev B 58: 1940-1946.
33. Shorter J, Ip P, Rabitz H, (1999) An e_cient chemical kinetics solver using high dimensional model representation. J Phys Chem A 103: 7192-7198.
34. Feng XJ, Hooshangi S, Chen D, Li G, Weiss R, et al. (2004) Optimizing genetic circuits by global sensitivity analysis. Biophys J 87: 2195-2202.
35. Li G, Artamonov M, Rabitz H, Wang S, Georgopoulos P, et al. (2003) High-dimensional model representations generated from low order terms-lp-RS-HDMR. J Comput Chem 24: 647-656.
36. Prill R, Saez-Rodriguez J, Alexopoulos L, Sorger P, Stolovitzky G, (2011) Crowdsourcing network inference: The dream predictive signaling network challenge. Sci Signal 4: mr7.
37. Prill R, Marbach D, Saez-Rodriguez J, Sorger P, Alexopoulos L, et al. (2010) Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PloS one 5: e9202.
38. Guex N, Migliavacca E, Xenarios I, (2010) Multiple Imputations Applied to the DREAM3 Phosphoproteomics Challenge: A Winning Strategy. PLoS ONE 5: e8012.
39. Saez-Rodriguez J, Alexopoulos L, Epperlein J, Samaga R, Lauffenburger D, et al. (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol 5: 331.
40. Chatterjee S, Price B, (1977) Regression analysis by example. Wiley New York.
41. Friedman J, Hastie T, Tibshirani R, (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9: 432-441.
42. Needham C, Bradford J, Bulpitt A, Westhead D, (2007) A primer on learning in Bayesian networks for computational biology. PLoS Comput Biol 3: e129.
43. Friedman N, (1998) The Bayesian structural EM algorithm. Proc UAI 98.
44. Neapolitan R, (2003) Learning bayesian networks. Prentice Hall Upper Saddle River, NJ.
45. Schwarz G, (1978) Estimating the Dimension of a Model. Ann Stat 6: 461-464.
46. Bogdan M, Ghosh J, Doerge R, (2004) Modifying the Schwarz Bayesian Information Criterion to Locate Multiple Interacting Quantitative Trait Loci. Genetics 167: 989-999.
47. Dhillon A, Pollock C, Steen H, Shaw P, Mischak H, et al. (2002) Cyclic AMP-Dependent Kinase Regulates Raf-1 Kinase Mainly by Phosphorylation of Serine 259. Mol Cell Biol 22: 3237-3246.
48. Zheng M, Zhang S, Zhu W, Ziman B, Kobilka B, et al. (2000) β2-Adrenergic Receptor-induced p38 MAPK Activation Is Mediated by Protein Kinase A Rather than by Gi or Gβγ in Adult Mouse Cardiomyocytes. J Biol Chem 275: 40635-40640.
49. Noh D, Shin S, Rhee S, (1995) Phosphoinositide-specific phospholipase C and mitogenic signaling. BBA Lib 1242: 99-113.
50. Brown K, Blay J, Irvine R, Heslop J, Berridge M, (1984) Reduction of epidermal growth factor receptor affinity by heterologous ligands: evidence for a mechanism involving the breakdown of phosphoinositides and the activation of protein kinase C. Biochem Biophys Res Commun 123: 377-384.
51. Alessi D, James S, Downes C, Holmes A, Gaffiney P, et al. (1997) Characterization of a 3-phosphoinositide-dependent protein kinase which phosphorylates and activates protein kinase B alpha. Curr Biol 7: 261-269.
52. Stokoe D, Stephens L, Copeland T, Gaffiney P, Reese C, et al. (1997) Dual Role of Phosphatidylinositol-3, 4, 5-trisphosphate in the Activation of Protein Kinase B. Science 277: 567-570.
53. Rana R, Hokin L, (1990) Role of phosphoinositides in transmembrane signaling. Physiol Rev 70: 115-164.
54. Greco S, Storelli C, Marsigliante S, (2006) Protein kinase C (PKC)-/- mediate the PKC/Aktdependent phosphorylation of extracellular signal-regulated kinases 1 and 2 in MCF-7 cells stimulated by bradykinin. J Endocrinol 188: 79-89.
55. Konishi H, Matsuzaki H, Tanaka M, Ono Y, Tokunaga C, et al. (1996) Activation of RAC-protein kinase by heat shock and hyperosmolarity stress through a pathway independent of phosphatidylinositol 3-kinase. Proc Natl Acad Sci US A 93: 7639-7643.
56. Ambrosini A, Tininini S, Barassi A, Racagni G, Sturani E, et al. (2000) cAMP cascade leads to Ras activation in cortical neurons. Mol Brain Res 75: 54-60.
57. Burgering B, Bos J, (1995) Regulation of Ras-mediated signalling: more than one way to skin a cat. Trends Biochem Sci 20: 18-22.
58. Crespo P, Xu N, Simonds W, Gutkind J, (1994) Ras-dependent activation of MAP kinase pathway mediated by G-protein bold beta big gamma subunits. Nature 369: 418-420.
59. Meyer P, Lafitte F, Bontempi G, (2008) Minet: A r/bioconductor package for inferring large transcriptional networks using mutual information. BMC bioinformatics 9: 461.
60. Honaker J, King G, Blackwell M, (2011) Amelia ii: A program for missing data. Journal of Statistical Software.
61. Mischak H, Seitz T, Janosch P, Eulitz M, Steen H, et al. (1996) Negative regulation of Raf-1 by phosphorylation of serine 621. Mol Cell Biol 16: 5409.
62. Fortino V, Torricelli C, Gardi C, Valacchi G, Rossi Paccani S, et al. (2002) ERKs are the point of divergence of PKA and PKC activation by PTHrP in human skin fibroblasts. Cell Mol Life Sci 59: 2165-2171.
63. Toker A, Newton A, (2000) Akt/Protein Kinase B Is Regulated by Autophosphorylation at the Hypothetical PDK-2 Site. J Biol Chem 275: 8271-8274.
64. Yano S, Tokumitsu H, Soderling T, (1998) Calcium promotes cell survival through CaM-K kinase activation of the protein-kinase-B pathway. Nature 396: 584-587.
65. Gringhuis S, de Leij L, Wayman G, Tokumitsu H, Vellenga E, (1997) The Ca2+/Calmodulindependent Kinase Type IV Is Involved in the CD5-mediated Signaling Pathway in Human T Lymphocytes. J Biol Chem 272: 31809-31820.
66. Fukuda R, Kelly B, Semenza G, (2003) Vascular Endothelial Growth Factor Gene Expression in Colon Cancer Cells Exposed to Prostaglandin E2 Is Mediated by Hypoxia-inducible Factor 1 1. Cancer Res 63: 2330-2334.
67. Alexopoulos L, Saez Rodriguez J, Cosgrove B, Lauffenburger D, Sorger P, (2010) Networks inferred from biochemical data reveal profound differences in TLR and inammatory signaling between normal and transformed hepatocytes. Mol Cell Proteomics 9: 1849-1865.
68. Friedman N, Linial M, Nachman I, Pe'er D, (2000) Using Bayesian Networks to Analyze Expression Data. J Comput Biol 7: 601-620.
69. Li G, Rabitz H, Wang SW, Georgopoulos PG, (2003) Correlation method for variance reduction of monte carlo integration in RS-HDMR. J Comput Chem 24: 277-283.
70. Li G, Rabitz H, (2011) General formulation of HDMR component functions with independent and correlated variables. J Math Chem pp 1-32.
71. Li G, Rabitz H, Yelvington P, Oluwole O, Bacon F, et al. (2010) Global sensitivity analysis for systems with independent and/or correlated inputs. J Phys Chem A 114: 6022-6032.
72. Li G, Rabitz H, (2010) D-morph regression: application to modeling with unknown parameters more than observation data. J Math Chem pp 1-26.
73. Deutsch F, (2001) Best approximation in inner product spaces. Springer Verlag.
74. Li G, Hu J, Wang SW, Georgopoulos PG, Schoendorf J, et al. (2006) Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions. J Phys Chem A 110: 2474-2485.
75. Wang K, Alvarez M, Bisikirska B, Linding R, Basso K, et al. (2009) Dissecting the interface between signaling and transcriptional regulation in human B cells. In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. NIH Public Access, pp 264-275.
76. Kumar N, Wolf-Yadlin A, White F, Lauffenburger D, (2007) Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data. PLoS Comput Biol 3: e4.
77. Ali{dotless}ş Ö, Rabitz H, (2001) Efficient Implementation of High Dimensional Model Representations. J Math Chem 29: 127-142.