Multivariate statistical analysis applied to an IL6 signal transduction model in hepatocytes

Statistics in Medicine

Volumn 28, Issue 19, 2009, Pages 2401-2434

  McArdle, Alison ^a   Kruger, Uwe ^b   Hahn, Juergen ^c  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

^c Texas AandM University   (United States)

Author keywords

Canonical correlation analysis; IL6 signal transduction; Model reduction; Multivariate statistical analysis

Indexed keywords

INTERLEUKIN 6; JANUS KINASE; MITOGEN ACTIVATED PROTEIN KINASE; STAT3 PROTEIN;

ACCURACY; ARTICLE; CANONICAL ANALYSIS; CELL STIMULATION; CONTROLLED STUDY; GENETIC ALGORITHM; GENETIC TRANSCRIPTION; LIVER CELL; MATHEMATICAL MODEL; MICHAELIS MENTEN KINETICS; MULTIVARIATE ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION;

ANIMALS; BIOMETRY; HEPATOCYTES; HUMANS; INTERLEUKIN-6; LEAST-SQUARES ANALYSIS; LINEAR MODELS; MAP KINASE SIGNALING SYSTEM; MODELS, BIOLOGICAL; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; STAT3 TRANSCRIPTION FACTOR; SYSTEMS BIOLOGY;

EID: 69949083542     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3621     Document Type: Article

References (47)

1. Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science 2004; 306:640-643.
2. Kitano H. Systems biology: a brief overview. Science 2002; 295:1662-1664.
3. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annual Review of Geneomics and Human Genetics 2001; 2:343-372.
4. Gadkar KG, Varner J, Doyle III FJ. Model identification of signal transduction networks from data using a state regulator problem. Systems Biology 2005; 2(1):17-30.
5. Singh A, Jayaraman A, Hahn J. A case study representing signal transduction in liver cells as a feedback control problem. Chemical Engineering Education 2007; 41(3):177-182.
6. Janes KA, Yaffe MB. Data-driven modelling of signal-transduction networks. Nature Reviews: Molecular Cell Biology 2006; 7(11):820-828.
7. Kholodenko BN. Cell-signalling dynamics in time and space. Nature Reviews: Molecular Cell Biology 2006; 7(3):165-176.
8. Jaqaman K, Danuser G. Linking data to models: data regression. Nature Reviews: Molecular Cell Biology 2006; 7:813-819.
9. Burnham AJ, Viveros R, MacGregor JF. Frameworks for latent variable multivariate regression. Journal of Chemometrics 1996; 10(1):31-45.
10. Singh A, Jayaraman A, Hahn J. Modeling regulatory mechanisms in IL-6 signal transduction in hepatocytes. Biotechnology and Bioengineering 2006; 95(5):850-862.
11. Kushner I, Mackiewicz A, Baumann H. Acute Phase Proteins: Molecular Biology, Biochemistry and Clinical Applications. CRC Press: Boca Raton, 1993.
12. Baumann H, Gauldie J. The acute phase response. Immunology Today 1994; 15(2):74-80.
13. Cerra FB. The systemic septic response: concepts of pathogenesis. Journal of Trauma 1990; 30(12 Suppl.): S169-S174.
14. Beal AL, Cerra FB. Multiple organ failure syndrome in the 1990s. Systemic inflammatory response and organ dysfunction. Journal of the American Medical Association 1994; 271(3):226-233.
15. Pinilla JC, Hayes P, Laverty W, Arnold C, Laxdal V. The C-reactive protein to prealbumin ratio correlates with the severity of multiple organ dysfunction. Surgery 1998; 124:799-806.
16. Anderson TW. Estimating linear restrictions on regression coefficients for multivariate normal distributions. The Annals of Mathematical Statistics 1951; 22(3):327-351.
17. Hotelling H. The most predictable criterion. Journal of Educational Psychology 1935; 26:139-142.
18. Wold H. Estimation of principal components and related models by iterative least squares. In Multivariate Analysis, Krishnaiaah PR (ed.). Academic Press: New York, 1966; 391-420.
19. Chu Y, Jayaraman A, Hahn J. Parameter sensitivity analysis of IL-6 signaling pathways. IET Systems Biology 2007; 1(6):342-352.
20. Chu Y, Hahn J. Selection of parameter sets and design of experiments for estimation of nonlinear dynamic systems. Proceedings of the IFAC World Congress, Seoul, Korea, 2008; 5545-5550, to appear.
21. Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Analytica Chimica Acta 1986; 185:1-17.
22. Höskuldsson A. PLS regression methods. Journal of Chemometrics 1988; 2:211-228.
23. Golub GH. Matrix decompositions and statistical calculations. Statistical Computation. Academic Press: New York, 1969.
24. MacArthur RH. On the relative abundance of bird species. Proceedings of the National Academy of Science, U.S.A., vol. 43, 1957; 293-295.
25. Jackson DA. Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 1993; 74(8):2204-2214.
26. Cangelosi R, Goriely A. Component retention in principal component analysis with application to cDNA microarray data. Biology Direct 2007; 2(2). DOI:10.1186/1745-6150-2-2.
27. Imada K, Leonard WJ. The JAK-STAT pathway. Molecular Immunology 2000; 37:1-11.
28. Yamada S, Shiono S, Joo A, Yoshimura A. Control mechanism of JAK/STAT signal transduction pathway. Federation of Biochemical Societies (FEBS) Letters 2003; 534:190-196.
29. Kholodenko BN. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Federation of Biochemical Societies (FEBS) Letters 2000; 267:1583-1588.
30. Levy DE, Darnell Jr JE. STATS: transcriptional control and biological impact. Nature Reviews: Molecular Cell Biology 2002; 3:651-662.
31. Hensen MA, Seborg DE (eds). Nonlinear Process Control. Prentice-Hall: NJ, U.S.A., 1997.
32. Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnology 2002; 20:370-375.
33. ten Hoeve J, Ibarra-Sanchez M, Fu Y, Zhu W, Tremblay M, David M, Shuai K. Identification of a nuclear Stat1 protein tyrosine phosphatase. Molecular and Cellular Biology 2002; 22:5662-5668.
34. Lang R, Pauleau AL, Parganas E, Takahashi Y, Mages J, Ihle JN, Rutschman R, Murray PJ. SOCS3 regulates the plasticity of gp130 signaling. Nature Immunology 2003; 4(6):546-550.
35. Jackson JE. A User's Guide to Principal Components. Wiley: New York, 1991.
36. Holland J. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.
37. Bäck T, Hammel U, Schwefel H. Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation 1997; 1(1):3-17.
38. Wold S, Ruhe A, Wold H, Dunn WJ. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalised inverses. SIAM Journal of Scientific Statistical Computations 1984; 5(3):735-743.
39. Mardia KV, Kent JT, Bibby JM. Multivariate Analysis. Academic Press: London, 1989.
40. Anderson TW. An Introduction to Multivariate Statistical Analysis. Wiley: New York, 2003.
41. Muller KE. Understanding canonical correlation through the general linear model and principal components. The American Statistician 1982; 36(4):342-354.
42. Alonso A, Geys H, Molenberghs G, Kenward MG, Vangeneugden T. Validation of surrogate markers in multiple randomized clinical trials with repeated measurements: canonical correlation approach. Biometrics 2004; 60: 845-853.
43. Kowalski J, Tu XM, Jia G, Perlis M, Frank E, Crits-Christoph P, Kupfer DJ. Generalized covariance-adjusted canonical correlation analysis with application to psychiatry. Statistics in Medicine 2003; 22:595-610.
44. Dunn JW, Doeksen GA. Canonical correlation analysis of selected demographic and health personnel variables. Southern Journal of Agricultural Economics 1977; 9(1):95-99.
45. Dijksterhuis G (ed.). Multivariate Data Analysis in Sensory and Consumer Science. Wiley-Blackwell: Oxford, U.K., 2008.
46. Holland TR, Levi M, Beckett GE. Associations between violent and nonviolent criminality: a canonical contingency-table analysis. Multivariate Behavioral Research 1981; 16:237-241.
47. Huang Z, Chu Y, Senocak F, Jayaraman A, Hahn J. Model update of signal transduction pathways in hepatocytes based upon sensitivity analysis. Proceedings of the Foundations of Systems Biology and Engineering (FOSBE), 2007; 45-50.