Metabolomic biomarkers: Search, discovery and validation

Expert Review of Molecular Diagnostics

Volumn 7, Issue 4, 2007, Pages 329-333

  Kell, Douglas B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; PROTEOME; TRANSCRIPTOME;

EDITORIAL; ENZYME BINDING; EXPERIMENTAL DESIGN; INFORMATION PROCESSING; MEMBRANE TRANSPORT; METABOLIC CONTROL ANALYSIS; METABOLIC REGULATION; METABOLITE; METABOLOMICS; MULTIVARIATE ANALYSIS; RESEARCH; SENSITIVITY ANALYSIS; SYSTEMS BIOLOGY; VALIDATION STUDY;

ANIMALS; BIOLOGICAL MARKERS; HUMANS; MULTIPROTEIN COMPLEXES; PROTEOME; REPRODUCIBILITY OF RESULTS; TRANSCRIPTION, GENETIC;

EID: 34447542843     PISSN: 14737159     EISSN: 17448352     Source Type: Journal    
DOI: 10.1586/14737159.7.4.329     Document Type: Editorial

References (92)

1. Brenner S. Loose ends. Current Biology, London, UK (1997).
2. Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 26, 99-105 (2004).
3. Brent R, Lok L. A fishing buddy for hypothesis generators. Science 308(5721), 504-506 (2005).
4. Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16(9), 373-378 (1998).
5. Raamsdonk LM, Teusink B, Broadhurst D et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45-50 (2001).
6. Harrigan GG, Goodacre R (Eds). Metabolic Profiling: its Role in Biomarker Discovery and Gene Function Analysis. Kluwer Academic Publishers, MA, USA (2003).
7. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245-252 (2004).
8. Kell DB. Metabolomics and systems biology: making sense of the soup. Curr. Op. Microbiol. 7, 296-307 (2004).
9. Vaidyanathan S, Harrigan GG, Goodacre R (Eds). Metabolome Analyses: Strategies for Systems Biology. Springer, NY, USA (2005).
10. Tomita M, Nishioka T (Eds). Metabolomics: the Frontier of Systems Biology. Springer, Tokyo, Japan (2005).
11. Dunn WB, Ellis DI. Metabolomics: current analytical platforms and methodologies. Trends. Anal. Chem. 24(4), 285-294 (2005).
12. Dunn WB, Bailey NJC, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst 130(5), 606-625 (2005).
13. Kell DB, Brown M, Davey HM et al. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol 3(7), 557-565 (2005).
14. Kell DB. Metabolomics, modelling and machine learning in systems biology: towards an understanding of the languages of cells. The 2005 Theodor Bücher lecture. FEBS J. 273, 873-894 (2006).
15. Kell DB. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Disc. Today 11(23-24), 1085-1092 (2006).
16. van der Greef J, Hankemeier T, McBurney RN. Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials? Pharmacogenomics 7(7), 1087-1094 (2006).
17. Lenz EM, Wilson ID. Analytical strategies in metabonomics. J. Proteome Res. 6(2), 443-458 (2007).
18. Lindon JC, Nicholson JK, Holmes E (Eds). The Handbook of Metabonomics and Metabolomics. Elsevier, Amsterdam, The Netherlands (2007).
19. Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H. Systems Biology in Practice: Concepts, Implementation and Clinical Application. Wiley/VCH, Berlin, Germany (2005).
20. Kell DB, Knowles JD. The role of modeling in systems biology. In: System Modeling in Cellular Biology: from Concepts to Nuts and Bolts. Szallasi Z, Stelling J, Periwal V (Eds). MIT Press, Cambridge, UK 3-18 (2006).
21. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall/CRC, London, UK (2006).
22. Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief Bioinform. 7(2), 140-150 (2006).
23. Palsson BØ. Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, Cambridge, UK (2006).
24. Kacser H, Burns JA. The control of flux. In: Rate Control of Biological Processes. Symposium of the Society for Experimental Biology Vol 27. Davies DD (Ed.). Cambridge University Press, Cambridge, UK 65-104 (1973).
25. Heinrich R, Rapoport TA. A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. Eur. J. Biochem. 42, 89-95 (1974).
26. Fell DA. Understanding the Control of Metabolism. Portland Press, London, UK (1996).
27. Heinrich R, Schuster S. The Regulation of Cellular Systems. Chapman & Hall, NY, USA (1996).
28. Mendes P, Kell DB, Westerhoff HV. Why and when channeling can decrease pool size at constant net flux in a simple dynamic channel. Biochim. Biophys. Acta 1289(2), 175-186 (1996).
29. Fell DA. Increasing the flux in metabolic pathways: a metabolic control analysis perspective. Biotechnol. Bioeng. 58(2-3), 121-124 (1998).
30. Cornish-Bowden A, Cárdenas ML (Eds). Technological and Medical Implications of Metabolic Control Analysis. Kluwer Academic Publishers, Dordrecht, The Netherland (2000).
31. Cascante M, Boros LG, Comin-Anduix B et al. Metabolic control analysis in drug discovery and disease. Nat. Biotechnol. 20(3), 243-249 (2002).
32. Fell DA, Thomas S. Physiological control of metabolic flux: the requirement for multisite modulation. Biochem. J. 311(Pt 1), 35-39 (1995).
33. Nelson DE, Ihekwaba AEC, Elliott M et al. Oscillations in NF-κB signalling control the dynamics of target gene expression. Science 306, 704-708 (2004).
34. Ihekwaba AEC, Broomhead DS, Grimley R, Benson N, Kell DB. Sensitivity analysis of parameters controlling oscillatory signalling in the NF-κB pathway: the roles of IκK and IκBα. Systems Biol. 1(1), 93-103 (2004).
35. Ihekwaba AEC, Broomhead DS, Grimley R et al. Synergistic control of oscillations in the NF-κB signalling pathway. IEE Systems Biol. 152(3), 153-160 (2005).
36. Alis OF, Rabitz H. Efficient implementation of high dimensional model representations. J. Math. Chem. 29(2), 127-142 (2001).
37. Saltelli A, Tarantola S, Campolongo F, Ratto M. Sensitivity Analysis in Practice: a Guide to Assessing Scientific Models. Wiley, NY, USA (2004).
38. Saltelli A, Ratto M, Tarantola S, Campolongo F. Sensitivity analysis for chemical models. Chem. Rev. 105(7), 2811-2827 (2005).
39. Yue H, Brown M, Knowles J et al. Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κB signalling pathway Mol. Biosyst. 2, 640-649 (2006).
40. Oinn T, Greenwood M, Addis M et al. Taverna: lessons in creating a workflow environment for the life sciences. Concurrency and Computation: Practice & Experience 18(10), 1067-1100 (2006).
41. Oinn T, Li P, Kell DB et al. Taverna/myGrid: aligning a workflow system with the life sciences community. In: Workflows for E-science: Scientific Workflows for Grids. Taylor IJ, Deelman E, Gannon DB, Shields M (Eds.). Springer, Guildford, UK, 300-319 (2007).
42. Taylor IJ, Deelman E, Gannon DB, Shields M. Workflows for E-science: Scientific Workflows for Grids. Taylor IJ, Deelman E, Gannon DB, Shields M (Eds.). Springer, Guildford, UK (2007).
43. Brown M, Dunn WB, Ellis DI et al. A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 35-46 (2005).
44. Broadhurst D, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2(4), 171-196 (2006).
45. O'Hagan S, Dunn WB, Brown M, Knowles JD, Kell DB. Closed-loop, multiobjective optimisation of analytical instrumentation: gas-chromatography- time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem. 77, 290-303 (2005).
46. O'Hagan S, Dunn WB, Broadhurst D et al. Closed-loop, multi-objective optimisation of two-dimensional gas chromatography (GCxGC-tof-MS) for serum metabolomics. Anal. Chem. 79(2), 464-476 (2007).
47. Allen JK, Davey HM, Broadhurst D et al. High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21(6), 692-696 (2003).
48. van der Greef J, Smilde AK. Symbiosis of chemometrics and metabolomics: past, present, and future. J. Chemometrics 19(5-7), 376-386 (2005).
49. Jenkins H, Hardy N, Beckmann M et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat. Biotechnol. 22(12), 1601-1606 (2004).
50. Spasic I, Dunn WB, Velarde G et al. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7, 281 (2006).
51. Goodacre R, Kell DB, Bianchi G. Neural networks and olive oil. Nature 359, 594 (1992).
52. Goodacre R, Neal MJ, Kell DB. Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra. Z. Bakteriol. 284, 516-539 (1996).
53. Kell DB, Darby RM, Draper J. Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiol. 126, 943-951 (2001).
54. Kell DB. Genotype:phenotype mapping: genes as computer programs. Trends Genet. 18(11), 555-559 (2002).
55. Allen J, Davey HM, Broadhurst D et al. Discrimination of the modes of action of antifungal substances by use of metabolic footprinting. Appl. Env. Micr. 70, 6157-6165 (2004).
56. Goodacre R. Making sense of the metabolome using evolutionary computation: seeing the wood with the trees. J. Exp. Bot. 56(410), 245-254 (2005).
57. Duda RO, Hart PE, Stork DE. Pattern Classification, 2nd Ed. John Wiley, London, UK (2001).
58. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer-Verlag, Berlin, Germany (2001).
59. Ransohoff DF. Rules of evidence for cancer molecular-marker discovery and validation. Nat. Rev. Cancer 4(4), 309-314 (2004).
60. Ransohoff DF. Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer 5(2), 142-149 (2005).
61. Ioannidis JP. Why most published research findings are false. PLoS Med 2(8), e124 (2005).
62. Ioannidis JP. Contradicted and initially stronger effects in highly cited clinical research. JAMA 294(2), 218-228 (2005).
63. Kirschenlohr HL, Griffin JL, Clarke SC et al. Proton NMR analysis of plasma is a weak predictor of coronary artery disease. Nat. Med. 12, 705-710 (2006).
64. Handl J, Knowles J, Kell DB. Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201-3212 (2005).
65. Ananiadou S, Kell DB, Tsujii J. Text mining and its potential applications in systems biology. Trends Biotechnol. 24, 571-579 (2006).
66. Ananiadou S, McNaught J (Eds). Text Mining in Biology and Biomedicine. Artech House, London, UK (2006).
67. Fensel D, Hendler J, Lieberman H, Wahlster W (Eds). Spinning the Semantic Web. MIT Press, Cambridge, MA, USA (2003).
68. Stevens R, Bodenreider O, Lussier YA. Semantic webs for life sciences. Pac. Symp. Biocomput. 112-115 (2006).
69. Berners-Lee T, Hendler J. Publishing on the semantic web. Nature 410(6832), 1023-1024 (2001).
70. Hendler J. Science and the semantic web. Science 299(5606), 520-521 (2003).
71. Baker CJO, Cheung K-H (Eds). Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences. Springer, NY, USA (2007).
72. McLuhan M, Fiore Q. The Medium is the Massage. Penguin Books, London, USA (1971).
73. Duarte NC, Becker SA, Jamshidi N et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104(6), 1777-1782 (2007).
74. Wishart DS, Tzur D, Knox C et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35(Database issue), D521-D526 (2007).
75. Boettcher C, Fellermeier M, Boettcher C, Drager B, Zenk MH. How human neuroblastoma cells make morphine. Proc. Natl Acad. Sci. USA 102(24), 8495-8500 (2005).
76. Williams PJ, Gumaa K, Scioscia M, Redman CW, Rademacher TW. Inositol phosphoglycan P-type in preeclampsia: a novel marker? Hypertension 49(1), 84-89 (2007).
77. Kenny LC, Dunn WB, Ellis DI et al. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 1(3), 227-234 (2005).
78. Underwood BR, Broadhurst D, Dunn WB et al. Huntington's disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles. Brain 129(4), 877-886 (2006).
79. Dunn WB, Broadhurst DI, Sasalu D et al. Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics (2007) (In Press).
80. Mendes P. Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem. Sci. 22, 361-363 (1997).
81. Mendes P, Kell DB. Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14, 869-883 (1998).
82. Mendes P, Kell DB. MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous cellular systems. Bioinformatics 17, 288-289 (2001).
83. Hoops S, Sahle S, Gauges R et al. COPASI: a COmplex PAthway SImulator. Bioinformatics 22(24), 3067-3074 (2006).
84. Mendes P, Kell DB. On the analysis of the inverse problem of metabolic pathways using artificial neural networks. Biosystems 38, 15-28 (1996).
85. Ljung L. System Identification: Theory for the User. Prentice Hall, Englewood Cliffs, NJ, USA (1987).
86. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13(11), 2467-2474 (2003).
87. Rodriguez-Fernandez M, Mendes P, Banga JR. A hybrid approach for efficient and robust parameter estimation in biochemical pathways. Biosystems 83(2-3), 248-265 (2006).
88. Oinn T, Addis M, Ferris J et al. Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17), 3045-3054 (2004).
89. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol 2(11), 886-897 (2004).
90. Castrillo JI, Zeef LA, Hoyle DC et al. Growth control of the eukaryote cell: a systems biology study in yeast. J. Biol 6(1), 4 (2007).
91. Catchpole GS, Beckmann M, Enot DP et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc. Natl Acad. Sci. USA 102(40), 14458-14462 (2005).
92. The Metabolic Control Analysis Web http://dbkgroup.org/mca_home.htm