Multi-way analysis of flux distributions across multiple conditions

Journal of Chemometrics

Volumn 23, Issue 7-8, 2009, Pages 406-420

  Verouden, Maikel P H ^a   Notebaart, Richard A ^b   Westerhuis, Johan A ^a   van der Werf, Marïet J ^c   Teusink, Bas ^d   Smilde, Age K ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^c TNO   (Netherlands)

^d VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

Correlation; Flux distributions; Genome scale network; PARAFAC; PCA

Indexed keywords

ASSOCIATION REACTIONS; GENES; LACTIC ACID; METABOLISM; REACTION RATES;

CONDITION; CORRELATION; ENVIRONMENTAL CONDITIONS; FLUX DISTRIBUTIONS; GENOME SEQUENCES; GENOME-SCALE NETWORK; IN-SILICO; MULTIWAY ANALYSIS; PARALLEL FACTOR ANALYSIS; PRINCIPAL-COMPONENT ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 70349653254     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1238     Document Type: Article

References (40)

1. Francke C, Siezen RJ, Teusink B. Reconstructing the metabolic network of a bacterium from its genome. Trends Microbiol. 2005; 13(11): 550-558.
2. Reed JL, Famili I, Thiele I, Palsson BØ Towards multidimensional genome annotation. Nat. Rev. Genet. 2006; 7(2): 130-141.
3. Notebaart RA, van Enckevort FHJ, Francke C, Siezen RJ, Teusink B. Accelerating the re30 construction of genome-scale metabolic networks. BMC Bioinf. 2006; 7: 296.
4. Satishkumar V, Dasika MS, Maranas CD. Optimization based automated curation of metabolic reconstructions. BMC Bioinf. 2007; 8: 212.
5. DeJongh M, Formsma K, Boillot P, Gould J, Rycenga M, Best A. Toward theautomatedgenerationofgenome-scalemetabolicnetworksinthe seed. BMC Bioinf. 2007; 8: 139.
6. Varma A, Palsson BØ. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 1994; 60(10): 3724-3731.
7. Ibarra RU, Edwards JS, Palsson BØ. Escherichia coli k-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 2002; 420(6912): 186-189.
8. Price ND, Reed JL, Palsson BØ. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2004; 2(11): 886-897.
9. Teusink B, Wiersma A, Molenaar D, Francke C, de Vos WM, Siezen RJ, Smid EJ. Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model. J. Biol. Chem. 2006; 281(52): 40041-40048.
10. Price ND, Schellenberger J, Palsson BØ. Uniform Sampling of Steady-State Flux Spaces: means to design experiments and to interpret enzymopathies. Biophys. J. 2004; 87(4): 2172-2186.
11. Burgard AP, Nikolaev EV, Schilling CH, Maranas CD. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 2004; 14(2): 301-312.
12. Notebaart RA, Teusink B, Siezen RJ, Papp B. Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput. Biol. 2008; 4(1): e26.
13. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, HerrgardMJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat. Protoc. 2007; 2(3): 727-738.
14. Poolman MG, Sebu C, Pidcock MK, Fell DA. Modular decomposition of metabolic systems via null-space analysis. J. Theor. Biol. 2007; 249(4): 691-705.
15. Wegmann U, ÓConnell-Motherway M, Zomer A, Buist G, Shearman C, Canchaya C, Ventura M, Goesmann A, Gasson MJ, Kuipers OP, van Sinderen D, Kok J. Complete genome sequence of the prototype lactic acid bacterium Lacto coccus lactis sub sp. cremor is MG1363. J. Bacteriol. 2007; 189(8): 3256-3270.
16. Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief. Bioinform. 2006; 7(2): 140-150.
17. Edwards JS, Covert M, Palsson BØ. Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 2002; 4(3): 133-140.
18. Wagner C, Urbanczik R. The geometry of the flux cone of a metabolic network. Biophys J. 2005; 89(6): 3837-3845.
19. Schwender J, Ohlrogge J, Shachar-Hill Y. Understanding flux in plant metabolic networks. Curr. Opin. Plant Biol. 2004; 7(3): 309-317.
20. Samoilov M, Plyasunov S, Arkin AP. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc. Natl. Acad. Sci. U.S.A. 2005; 102(7): 2310-2315.
21. Beard DA, Liang Sd, Qian H. Energy balance for analysis of complex metabolic networks. Biophys. J. 2002; 83(1): 79-86.
22. Kümmel A, Panke S, Heinemann M. Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinf. 2006; 7: 512.
23. Henry CS, Broadbelt LJ, Hatzimanikatis V. Thermodynamics-based metabolic flux analysis. Biophys. J. 2007; 92(5): 1792-1805.
24. Pfeiffer T, Scheckentityánchez-Valdenebro I, Nucheckentityño J, Montero F, Schuster S. METATOOL: for studying metabolic networks. Bioinformatics 1999; 15(3): 251-257.
25. Bro R, Smilde AK. Centering and scaling in component analysis. J. Chemom. 2003; 17(1): 16-33.
26. Horan C. Multidimensional scaling: combining observations when individuals have different perceptual structures. Psychometrika 1969; 34(2): 139-165.
27. Carroll JD, Chang JJ. Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika 1970; 35(3): 283-319.
28. Carroll JD, Pruzansky S. The CANDECOMP-CANDELINC Family of Models and Methods for Multidimensional Data Analysis, chap. 10. In Law et al. [40], 372-402.
29. Harshman R. Foundations of the PARAFAC procedure: Models and conditions for an explanatory multimodal factor analysis. UCLA Working Papers in Phonetics 1970; 16: 1-84.
30. Harshman RA, Lundy ME. The PARAFAC Model for Three-Way Factor Analysis and Multidimensional Scaling, chap. 5. In Law et al. [40], 122-215.
31. Smilde A, Bro R, Geladi P. Multi-way Analysis with Applications in the Chemical Sciences. John Wiley & Sons, Ltd: Chichester, 2004.
32. Ten Berge JM, Kiers HA. Some clarifications of the CANDECOMP algorithm applied to INDSCAL. Psychometrika 1991; 56(2): 317-326.
33. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987; 2(1-3): 37-52.
34. Jolliffe IT. Principal Component Analysis, Springer Series in Statistics, 2nd edn, Vol. XXIX. Springer-Verlag: New York, 2002.
35. Andersson GG, Dable BK, Booksh KS. Weighted parallel factor analysis for calibration of HPLC-UV/Vis spectrometers in the presence of Beer's law deviations. Chemom. Intell. Lab. Syst. 1999; 49(2): 195-213.
36. Vega-Montoto L, Wentzell PD. Maximum likelihood parallel factor analysis (MLPARAFAC). J. Chemom. 2003; 17(4): 237-253.
37. Bro R, Sidiropoulos ND, Smilde AK. Maximum likelihood fitting using ordinary least squares algorithms. J. Chemom. 2002; 16(8-10): 387-400.
38. Hageman JA, Hendriks MMWB, Westerhuis JA, van der Werf MJ, Berger R, Smilde AK. Simplivariate models: ideas and first examples. PLoS One 2008; 3(9): e3259.
39. Schepers J, van Mechelen I, Ceulemans E. Three-mode partitioning. Comput. Stat. Data Anal. 2006; 51(3): 1623-1642.
40. Law HG, Snyder CW, Jr., Hattie JA, McDonald RP (eds.) Research Methods for Multimode Data Analysis. Praeger Publishers: New York, 1984.