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References

  • 1
    von Bertalanffy L (1969) General System Theory. George Braziller, New York.
  • 2
    Iberall AS (1972) Toward a General Science of Viable Systems. McGraw-Hill, New York.
  • 3
    Kell DB (1979) On the functional proton current pathway of electron transport phosphorylation: an electrodic view. Biochim Biophys Acta 549, 5599.
  • 4
    Brenner S (1997) Loose Ends. Current Biology, London.
  • 5
    Hood L (2003) Systems biology: integrating technology, biology, and computation. Mech Ageing Dev 124, 916.
  • 6
    Ideker T, Galitski T & Hood L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2, 343372.
  • 7
    Kitano H (2002) Systems biology: a brief overview. Science 295, 16621664.
  • 8
    Kitano H (2002) Computational systems biology. Nature 420, 206210.
  • 9
    Davidov E, Holland J, Marple E & Naylor S (2003) Advancing drug discovery through systems biology. Drug Discov Today 8, 175183.
  • 10
    Henry CM (2003) Systems biology. Chem Eng News 81, 4555.
  • 11
    Westerhoff HV & Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotechnol 22, 12491252.
  • 12
    Klipp E, Herwig R, Kowald A, Wierling C & Lehrach H (2005) Systems Biology in Practice: Concepts, Implementation and Clinical Application. Wiley/VCH, Berlin.
  • 13
    Kriete A & Eils R (2005) Computational Systems Biology. Academic Press, New York.
  • 14
    Palsson BØ (2006) Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, Cambridge.
  • 15
    Kell DB (2002) Genotype: phenotype mapping: genes as computer programs. Trends Genet 18, 555559.
  • 16
    Kell DB & Oliver SG (2004) 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, 99105.
  • 17
    Kell DB (2005) Metabolomics, machine learning and modelling: towards an understanding of the language of cells. Biochem Soc Trans 33, 520524.
  • 18
    Ihekwaba AEC, Broomhead DS, Grimley R, Benson N & Kell DB (2004) Sensitivity analysis of parameters controlling oscillatory signalling in the NF-κB pathway: the roles of IKK and IκBα. Systems Biol 1, 93103.
  • 19
    Nelson DE, Ihekwaba AEC, Elliott M, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG, Edwards SW, McDowell HP, Unitt JF, Sullivan E, Grimley R, Benson N, Broomhead DS, Kell DB & White MRH (2004) Oscillations in NF-κB signalling control the dynamics of target gene expression. Science 306, 704708.
  • 20
    Ihekwaba AEC, Broomhead DS, Grimley R, Benson N, White MRH & Kell DB (2005) Synergistic control of oscillations in the NF-κB signalling pathway. IEE Systems Biol 152, 153160.
  • 21
    Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D & Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298, 824827.
  • 22
    Tyson JJ, Chen KC & Novak B (2003) Sniffers, buzzers, toggles and blinkers. dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15, 221231.
  • 23
    Bhalla U.S. (2003) Understanding complex signaling networks through models and metaphors. Prog Biophys Mol Biol 81, 4565.
  • 24
    Wall ME, Hlavacek WS & Savageau MA (2004) Design of gene circuits: Lessons from bacteria. Nat Rev Genet 5, 3442.
  • 25
    Kacser H (1986) On parts and wholes in metabolism. The Organization of Cell Metabolism (Welch, G R & Clegg, J S, eds), pp. 327337. Plenum Press, New York.
  • 26
    Kell DB & Mendes P (2000) Snapshots of systems. metabolic control analysis and biotechnology in the post-genomic era. Technological and Medical Implications of Metabolic Control Analysis (Cornish-Bowden, A & Cárdenas, M L, eds), pp. 325 (and see http://dbk.ch.umist.ac.uk/WhitePapers/mcabio.htm). Kluwer Academic Publishers, Dordrecht.
  • 27
    Kell DB (2004) Metabolomics and systems biology: making sense of the soup. Curr Op Microbiol 7, 296307.
  • 28
    Kacser H & Burns JA (1973) The control of flux. Rate Control of Biological Processes. Symposium of the Society for Experimental Biology, Vol. 27 (Davies, D D, ed.), pp. 65104. Cambridge University Press, Cambridge.
  • 29
    Heinrich R & Rapoport TA (1974) A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. Eur J Biochem 42, 8995.
  • 30
    Kell DB & Westerhoff HV (1986) Metabolic control theory: its role in microbiology and biotechnology. FEMS Microbiol Rev 39, 305320.
  • 31
    Heinrich R & Schuster S (1996) The Regulation of Cellular Systems. Chapman & Hall, New York.
  • 32
    Fell DA (1996) Understanding the Control of Metabolism. Portland Press, London.
  • 33
    Savageau M (1976) Biochemical Systems Analysis: a Study of Function and Design in Molecular Biology. Addison-Wesley, Reading, MA.
  • 34
    Voit EO (2000) Computational Analysis of Biochemical Systems. Cambridge University Press, Cambridge.
  • 35
    Lazebnik Y (2002) Can a biologist fix a radio? – or, what I learned while studying apoptosis. Cancer Cell 2, 179182.
  • 36
    Kell DB & Knowles JD (2005) The role of modeling in systems biology. System Modeling in Cellular Biology: from Concepts to Nuts and Bolts (Szallasi, Z Periwal, V & Stelling, J, eds), pp. 318. MIT Press, Cambridge.
  • 37
    Bower JM & Bolouri H (2004) Computational Modeling of Genetic and Biochemical Networks. Bradford Books, New York.
  • 38
    Mendes P & Kell DB (2001) MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous cellular systems. Bioinformatics 17, 288289.
  • 39
    Andrews SS & Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1, 137151.
  • 40
    Salis H & Kaznessis Y (2005) Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J Chem Phys 122, 54103.
  • 41
    Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524531.
  • 42
    Finney A & Hucka M (2003) Systems biology markup language: Level 2 and beyond. Biochem Soc Trans 31, 14721473.
  • 43
    Mendes P (1997) Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem Sci 22, 361363.
  • 44
    Mendes P & Kell DB (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14, 869883.
  • 45
    Kauffman S, Lobo J & Macready WG (2000) Optimal search on a technology landscape. J Econ Behav Organ 43, 141166.
  • 46
    Goldberg DE (2002) The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer, Boston.
  • 47
    Koza JR, Keane MA, Streeter MJ & Mydlowec W, Yu, J & Lanza G (2003) Genetic Programming: Routine Human-Competitive Machine Intelligence. Kluwer, New York.
  • 48
    Raju GK & Cooney CL (1998) Active learning from process data. AlChE J 44, 21992211.
  • 49
    Bryant CH, Muggleton SH, Oliver SG, Kell DB, Reiser P & King RD (2001) Combining inductive logic programming, active learning and robotics to discover the function of genes. Electronic Transactions on Artificial Intelligence 5, 136 (http://www.ep.liu.se/ej/etai/2001/001/).
  • 50
    Cohn DA, Ghabhramani Z & Jordan MI (1996) Active learning with statistical models. J Artif Intell Res 4, 129145.
  • 51
    Hasenjäger M & Ritter H (1998) Active learning with local models. Neural Proc Lett 7, 110117.
  • 52
    Cohn DA, Atlas L & Ladner R (1994) Improving generalisation with active learning. Machine Learning 15, 201221.
  • 53
    Mackay D (1992) Information-based objective functions for active data selection. Neural Comput 4, 590604.
  • 54
    Milano M, Schmidhuber J & Koumoutsakos P (2001) (2001) Active learning with adaptive grids. Artifical Neural Networks-ICANN Proc 2130, 436442.
  • 55
    King RD, Whelan KE, Jones FM, Reiser PGK, Bryant CH, Muggleton SH, Kell DB & Oliver SG (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247252.
  • 56
    Whelan KE & King RD (2004) Intelligent software for laboratory automation. Trends Biotechnol 22, 440445.
  • 57
    Olansky AS, Parker LR Jr, Morgan SL & Deming SN (1977) Automated development of analytical chemical methods: the determination of serum calcium by the cresolphthalein complexone method. Anal Chim Acta 95, 107133.
  • 58
    Olansky AS & Deming SN (1978) Automated development of a kinetic method for the continuous-flow determination of creatinine. Clin Chem 24, 21152124.
  • 59
    O'Hagan S, Dunn WB, Brown M, Knowles JD & Kell DB (2005) 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, 290303.
  • 60
    Daniel C, Full J, Gonzalez L, Lupulescu C, Manz J, Merli A, Vajda S & Woste L (2003) Deciphering the reaction dynamics underlying optimal control laser fields. Science 299, 536539.
  • 61
    Schlesselman JJ (1982) Case-Control Studies – Design, Conduct, Analysis. Oxford University Press, Oxford.
  • 62
    Logothetis N & Wynn HP (1989) Quality Through Design: Experimental Design, Off-Line Quality Control, and Taguchi's Contribution. Clarendon Press, Oxford.
  • 63
    Hicks CR & Turner KV (1999) Jr. Fundamental Concepts in the Design of Experiments, 5th edn. Oxford University Press, Oxford.
  • 64
    Montgomery DC (2001) Design and Analysis of Experiments, 5th edn. Wiley, Chichester.
  • 65
    Myers RH & Montgomery DC (1995) Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley, New York.
  • 66
    Brent R (1999) Functional genomics: Learning to think about gene expression data. Curr Biol 9, R338R341.
  • 67
    Kell DB, Darby RM & Draper J (2001) Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiol 126, 943951.
  • 68
    Kell DB (2002) Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Mol Biol Report 29, 237241.
  • 69
    Brent R & Lok L (2005) A fishing buddy for hypothesis generators. Science 308, 504506.
  • 70
    Förster J, Famili I, Fu P, Palsson BØ & Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13, 244253.
  • 71
    Reed JL & Palsson BØ (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 185, 26922699.
  • 72
    Borodina I, Krabben P & Nielsen J (2005) Genome-scale analysis of Streptomyces coelicolor A3 (2) metabolism. Genome Res 15, 820829.
  • 73
    Edwards JS, Ibarra RU & Palsson BØ (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19, 125130.
  • 74
    Segrè D, Vitkup D & Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA 99, 1511215117.
  • 75
    Segrè D, Zucker J, Katz J, Lin X, D'Haeseleer P, Rindone WP, Kharchenko P, Nguyen DH, Wright MA & Church GM (2003) From annotated genomes to metabolic flux models and kinetic parameter fitting. Omics 7, 301316.
  • 76
    Covert MW & Palsson BØ (2003) Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol 221, 309325.
  • 77
    Papin JA, Stelling J, Price ND, Klamt S, Schuster S & Palsson BO (2004) Comparison of network-based pathway analysis methods. Trends Biotechnol 22, 400405.
  • 78
    Patil KR, Akesson M & Nielsen J (2004) Use of genome-scale microbial models for metabolic engineering. Curr Opin Biotechnol 15, 6469.
  • 79
    Famili I, Mahadevan R & Palsson BO (2005) k-Cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88, 16161625.
  • 80
    Patil KR, Rocha I, Forster J & Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308.
  • 81
    Fersht A (1977) Enzyme Structure and Mechanism, 2nd edn. W.H. Freeman, San Francisco.
  • 82
    Keleti T (1986) Basic Enzyme Kinetics, Akadémiai Kiadó, Budapest.
  • 83
    Segel IH (1993) Enzyme Kinetics. Wiley, New York.
  • 84
    Cornish-Bowden A (1995) Fundamentals of Enzyme Kinetics, 2nd edn. Portland Press, London.
  • 85
    Wu L, Wang W, van Winden WA, van Gulik WM & Heijnen JJ (2004) A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics. Eur J Biochem 271, 33483359.
  • 86
    Ljung L (1987) System Identification: Theory for the User. Prentice Hall, Englewood Cliffs, NJ.
  • 87
    Mendes P & Kell DB (1996) On the analysis of the inverse problem of metabolic pathways using artificial neural networks. Biosystems 38, 1528.
  • 88
    Koza JR, Mydlowec W & Lanza G, Yu J & Keane MA (2001) Reverse engineering of metabolic pathways from observed data using genetic programming. Pac Symp Biocomput 434445.
  • 89
    Moles CG, Mendes P & Banga JR (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13, 24672474.
  • 90
    Styczynski MP & Stephanopoulos G (2005) Overview of computational methods for the inference of gene regulatory networks. Comput Chem Eng 29, 519534.
  • 91
    Patil KR & Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci USA 102, 26852689.
  • 92
    Oliver SG, Winson MK, Kell DB & Baganz F (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16, 373378.
  • 93
    Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh M, Berden JA, Brindle KM, Kell DB, Rowland JJ, et al. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19, 4550.
  • 94
    Fiehn O (2002) Metabolomics: the link between genotypes and phenotypes. Plant Mol Biol 48, 155171.
  • 95
    Harrigan GG & Goodacre R (2003) Metabolic Profiling: its Role in Biomarker Discovery and Gene Function Analysis, Kluwer Academic Publishers, Boston.
  • 96
    Sumner LW, Mendes P & Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817836.
  • 97
    Weckwerth W (2003) Metabolomics in systems biology. Annu Rev Plant Biol 54, 669689.
  • 98
    German JB, Roberts MA & Watkins SM (2003) Personal metabolomics as a next generation nutritional assessment. J Nutr 133, 42604266.
  • 99
    Nicholson JK & Wilson ID (2003) Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism. Nat Rev Drug Disc 2, 668676.
  • 100
    Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, et al. (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci 9, 418425.
  • 101
    Nicholson JK, Holmes E, Lindon JC & Wilson ID (2004) The challenges of modeling mammalian biocomplexity. Nat Biotechnol 22, 12681274.
  • 102
    Whitfield PD, German AJ & Noble PJ (2004) Metabolomics: an emerging post-genomic tool for nutrition. Br J Nutr 92, 549555.
  • 103
    Gibney MJ, Walsh M, Brennan L, Roche HM, German B & van Ommen B (2005) Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr 82, 497503.
  • 104
    Vaidyanathan S, Harrigan GG & Goodacre R (2005) Metabolome Analyses: Strategies for Systems Biology. Springer, New York.
  • 105
    Wilson ID, Nicholson JK, Castro-Perez J, Granger JH, Johnson KA, Smith BW & Plumb RS (2005) High resolution ‘ultra performance’ liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 4, 591598.
  • 106
    Wilson ID & Brinkman UA (2003) Hyphenation and hypernation: the practice and prospects of multiple hyphenation. J Chromatogr A 1000, 325356.
  • 107
    Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG & Kell DB (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22, 245252.
  • 108
    Dunn WB & Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. Trends Anal Chem 24, 285294.
  • 109
    Dunn WB, Bailey NJC & Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130, 606625.
  • 110
    Brown M, Dunn WB, Ellis DI, Goodacre R, Handl J, Knowles JD, O'Hagan S, Spasic I & Kell DB (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 3546.
  • 111
    Vaidyanathan S, Broadhurst DI, Kell DB & Goodacre R (2003) Explanatory optimisation of protein mass spectrometry via genetic search. Anal Chem 75, 66796686.
  • 112
    Barrow JD & Silk J (1995) The Left Hand of Creation: the Origin and Evolution of the Expanding Universe. Penguin, London.
  • 113
    Reeves CR (1995) Modern Heuristic Techniques for Combinatorial Problems. McGraw-Hill, London.
  • 114
    RaywardSmith VJ, Osman IH, Reeves CR & Smith GD (1996) Modern Heuristic Search Methods. Wiley, Chichester.
  • 115
    Corne D, Dorigo M & Glover F (1999) New Ideas in Optimization. McGraw-Hill, London.
  • 116
    Dasgupta P, Chakrabarti PP & DeSarkar SC (1999) Multiobjective Heuristic Search, Vieweg, Braunschweig.
  • 117
    Michalewicz Z & Fogel DB (2000) How to Solve It: Modern Heuristics. Springer-Verlag, Heidelberg.
  • 118
    Vaidyanathan S, Kell DB & Goodacre R (2004) Selective detection of proteins in mixtures using electrospray ionization mass spectrometry: influence of instrumental settings and implications for proteomics. Anal Chem 76, 50245032.
  • 119
    Bäck T, Fogel DB & Michalewicz Z (1997) Handbook of Evolutionary Computation. IOP Publishing/Oxford University Press, Oxford.
  • 120
    Kell DB & King RD (2000) On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol 18, 9398.
  • 121
    Langley P, Simon HA, Bradshaw GL & Zytkow JM (1987) Scientific Discovery: Computational Exploration of the Creative Processes. MIT Press, Cambridge, MA.
  • 122
    Allen JK, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG & Kell DB (2003) High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21, 692696.
  • 123
    Allen J, Davey HM, Broadhurst D, Rowland JJ, Oliver SG & Kell DB (2004) Discrimination of the modes of action of antifungal substances by use of metabolic footprinting. Appl Env Micr 70, 61576165.
  • 124
    Kell DB, Brown M, Davey HM, Dunn WB, Spasic I & Oliver SG (2005) Metabolic footprinting and Systems Biology: the medium is the message. Nat Rev Microbiol 3, 557565.
  • 125
    Kenny LC, Dunn WB, Ellis DI, Myers J & Baker PN, The GOPEC Consortium & Kell DB (2005) Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 1 (in press). Online 10.1007/s11306-005-0003-1.
  • 126
    Catchpole GS, Beckmann M, Enot DP, Mondhe M, Zywicki B, Taylor J, Hardy N, Smith A, King RD, Kell DB, Fiehn O & Draper J (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102, 1445814462.
  • 127
    Kaderbhai NN, Broadhurst DI, Ellis DI, Goodacre R & Kell DB (2003) Functional genomics via metabolic footprinting: Monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp Func Genomics 4, 376391.
  • 128
    Marriott P & Shellie R (2002) Principles and applications of comprehensive two-dimensional gas chromatography. Trends Anal Chem 21, 573583.
  • 129
    Ong RC & Marriott PJ (2002) A review of basic concepts in comprehensive two-dimensional gas chromatography. J Chromatogr Sci 40, 276291.
  • 130
    Blumberg LM (2003) Comprehensive two-dimensional gas chromatography: metrics, potentials, limits. J Chromatogr A 985, 2938.
  • 131
    Plumb R, Castro-Perez J, Granger J, Beattie I, Joncour K & Wright A (2004) Ultra-performance liquid chromatography coupled to quadrupole-orthogonal time-of-flight mass spectrometry. Rapid Commun Mass Spectrom 18, 23312337.
  • 132
    Wilson ID, Plumb R, Granger J, Major H, Williams R & Lenz EM (2005) HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life Sci 817, 6776.
  • 133
    Sauro HM & Kholodenko BN (2004) Quantitative analysis of signaling networks. Prog Biophys Mol Biol 86, 543.
  • 134
    Hoffmann A, Levchenko A, Scott ML & Baltimore D (2002) The IκB-NF-κB signaling module: temporal control and selective gene activation. Science 298, 12411245.
  • 135
    Ghosh S & Karin M (2002) Missing pieces in the NF-kappaB puzzle. Cell 109 (Suppl.), S81S96.
  • 136
    Richmond A (2002) NF-κB, chemokine gene transcription and tumour growth. Nat Rev Immunol 2, 664674.
  • 137
    Tian B & Brasier AR (2003) Identification of a nuclear factor kappa B-dependent gene network. Recent Prog Horm Res 58, 95130.
  • 138
    Tian B, Nowak DE, Jamaluddin M, Wang S & Brasier AR (2005) Identification of direct genomic targets downstream of the nuclear factor-kappaB transcription factor mediating tumor necrosis factor signalling. J Biol Chem 280, 1743517448.
  • 139
    Nelson G, Paraoan L, Spiller DG, Wilde GJ, Browne MA, Djali PK, Unitt JF, Sullivan E, Floettmann E & White MR (2002) Multi-parameter analysis of the kinetics of NF-κB signalling and transcription in single living cells. J Cell Sci 115, 11371148.
  • 140
    Mantzaris NV (2005) Single-cell gene-switching networks and heterogeneous cell population phenotypes. Comput Chem Eng 29, 631643.
  • 141
    Kell DB, Ryder HM, Kaprelyants AS & Westerhoff HV (1991) Quantifying heterogeneity: Flow cytometry of bacterial cultures. Antonie van Leeuwenhoek 60, 145158.
  • 142
    Davey HM & Kell DB (1996) Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analysis. Microbiol Rev 60, 641696.
  • 143
    Werner SL, Barken D & Hoffmann A (2005) Stimulus specificity of gene expression programs determined by temporal control of IKK activity. Science 309, 18571861.
  • 144
    Covert MW, Leung TH, Gaston JE & Baltimore D (2005) Achieving stability of lipopolysaccharide-induced NF-kappaB activation. Science 309, 18541857.
  • 145
    White TA & Kell DB (2004) Comparative genomic assessment of novel broad-spectrum targets for antibacterial drugs. Comp Func Genomics 5, 304327.
  • 146
    Wolf DM & Arkin AP (2003) Motifs, modules and games in bacteria. Curr Opin Microbiol 6, 125134.
  • 147
    Yeger-Lotem E, Sattath S, Kashtan N, Itzkovitz S, Milo R, Pinter RY, Alon U & Margalit H (2004) Network motifs in integrated cellular networks of transcription-regulation and protein–protein interaction. Proc Natl Acad Sci USA 101, 59345939.
  • 148
    Woodward AM, Rowland JJ & Kell DB (2004) Fast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels. Analyst 129, 542552.
  • 149
    Isaacs FJ, Blake WJ & Collins JJ (2005) Molecular biology. Signal processing in single cells. Science 307, 18861888.
  • 150
    Mendes P, Kell DB & Welch GR (1995) Metabolic channeling in organized enzyme systems: experiments and models. Enzymology in Vivo (Brindle, K M, ed.), pp. 119. JAI Press, London.
  • 151
    Ovádi J (1995) Cell Architecture and Metabolic Channeling. Springer-Verlag, New York.
  • 152
    Agius L & Sherratt HSA (1997) Channelling in Intermediary Metabolism. Portland Press, London.
  • 153
    Ovádi J & Srere PA (2000) Macromolecular compartmentation and channeling. Int Rev Cytol 192, 255280.
  • 154
    Buchler NE, Gerland U & Hwa T (2003) On schemes of combinatorial transcription logic. Proc Natl Acad Sci USA 100, 51365141.
  • 155
    Westerhoff HV, Tsong TY, Chock PB, Chen Y & Astumian RD (1986) How enzymes can capture and transmit free energy from an oscillating electric field. Proc Natl Acad Sci USA 83, 47344738.
  • 156
    Westerhoff HV, Astumian RD & Kell DB (1988) Mechanisms for the interaction between nonstationary electric fields and biological systems.2. Nonlinear dielectric theory and free-energy transduction. Ferroelectrics 86, 79101.
  • 157
    Woodward AM & Kell DB (1990) On the nonlinear dielectric properties of biological systems. Saccharomyces cerevisiae. Bioelectrochem Bioenerg 24, 83100.
  • 158
    Woodward AM, Jones A, Zhang X, Rowland J & Kell DB (1996) Rapid and non-invasive quantification of metabolic substrates in biological cell suspensions using nonlinear dielectric spectroscopy with multivariate calibration and artificial neural networks. Principles and applications. Bioelectrochem Bioenerg 40, 99132.
  • 159
    Kell DB, Woodward AM, Davies E, Todd RW, Evans MF & Rowland JJ (2004) Nonlinear dielectric spectroscopy of biological systems: principles and applications. Nonlinear Dielectric Phenomena in Complex Liquids (RzoskaSJ & ZheleznyVP, eds), pp. 335344. Kluwer, Dordrecht.
  • 160
    Mikulecky DC (1983) Network thermodynamics: a candidate for a common language for theoretical and experimental biology. Am J Physiol 245, R1R9.
  • 161
    Mikulecky DC (2001) Network thermodynamics and complexity: a transition to relational systems theory. Comput Chem 25, 369391.
  • 162
    Westerhoff HV & van Dam K (1987) Thermodynamics and Control of Biological Free Energy Transduction. Elsevier, Amsterdam.
  • 163
    Koza JR, Mydlowec W, Lanza G, Yu J & Keane MA (2001) Automatic synthesis of both the topology and sizing of metabolic pathways using genetic programming. Proceedings of the. GECCO-2001. (SpectorL, GoodmanED, WuA, LangdonWB, GeneralM, SenS, DorigoM, PezeshkS, GarzonMH & BurkeE, eds), pp. 5765. Morgan Kaufmann, San Francisco.
  • 164
    Tyson JJ, Chen K & Novak B (2001) Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2, 908916.
  • 165
    Csete ME & Doyle JC (2002) Reverse engineering of biological complexity. Science 295, 16641669.
  • 166
    Kramer BP, Fischer C & Fussenegger M (2004) BioLogic gates enable logical transcription control in mammalian cells. Biotechnol Bioeng 87, 478484.
  • 167
    Deckard A & Sauro HM (2004) Preliminary studies on the in silico evolution of biochemical networks. Chembiochem 5, 14231431.
  • 168
    Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M & Alon U (2004) Superfamilies of evolved and designed networks. Science 303, 15381542.
  • 169
    Kashtan N & Alon U (2005) Spontaneous evolution of modularity and network motifs. Proc Natl Acad Sci USA 102, 1377313778.
  • 170
    Endy D & Brent R (2001) Modelling cellular behaviour. Nat 409, 391395.
  • 171
    Pethig R & Kell DB (1987) The passive electrical properties of biological systems: their significance in physiology, biophysics and biotechnology. Phys Med Biol 32, 933970.
  • 172
    Chen W-K (1986) Passive and Active Filters: Theory and Implementations. Wiley, New York.
  • 173
    Rosenfeld N & Alon U (2003) Response delays and the structure of transcription networks. J Mol Biol 329, 645654.
  • 174
    Shen-Orr SS, Milo R, Mangan S & Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31, 6468.
  • 175
    Mangan S & Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100, 1198011985.
  • 176
    Barkai N & Leibler S (1997) Robustness in simple biochemical networks. Nature 387, 913917.
  • 177
    von Dassow G, Meir E, Munro EM & Odell GM (2000) The segment polarity network is a robust development module. Nature 406, 188192.
  • 178
    Ma L & Iglesias PA (2002) Quantifying robustness of biochemical network models. BMC Bioinformatics 3. http://www.biomedcentral.com/1471-2105/3/38.
  • 179
    Morohashi M, Winn AE, Borisuk MT, Bolouri H, Doyle J & Kitano H (2002) Robustness as a measure of plausibility in models of biochemical networks. J Theor Biol 216, 1930.
  • 180
    Ebenhoh O & Heinrich R (2003) Stoichiometric design of metabolic networks: multifunctionality, clusters, optimization, weak and strong robustness. Bull Math Biol 65, 323357.
  • 181
    Aldana M & Cluzel P (2003) A natural class of robust networks. Proc Natl Acad Sci USA 100, 87108714.
  • 182
    Kitano H (2004) Biological robustness. Nat Rev Genet 5, 826837.
  • 183
    Schmitt BM (2004) The concept of ‘buffering’ in systems and control theory: from metaphor to math. Chembiochem 5, 13841392.
  • 184
    Stelling J, Sauer U, Szallasi Z & Doyle FJ (2004) 3rd & Doyle, J. Robustness of cellular functions. Cell 118, 675685.
  • 185
    Chaves M, Albert R & Sontag ED (2005) Robustness and fragility of Boolean models for genetic regulatory networks. J Theor Biol 235, 431449.
  • 186
    Chen BS, Wang YC, Wu WS & Li WH (2005) A new measure of the robustness of biochemical networks. Bioinformatics 21, 26982705.
  • 187
    Wagner A (2005) Circuit topology and the evolution of robustness in two-gene circadian oscillators. Proc Natl Acad Sci USA 102, 1177511780.
  • 188
    Strömbäck L & Lambrix P (2005) Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics 21, 44014407.
  • 189
    Cornell M, Paton NW, Hedeler C, Kirby P, Delneri D, Hayes A & Oliver SG (2003) GIMS: an integrated data storage and analysis environment for genomic and functional data. Yeast 20, 12911306.
  • 190
    Spellman P, Miller M, Stewart J, Troup C, Sarkans U, Chervitz S, Bernhart D, Sherlock G, Ball C, Lepage M, et al. (2002) Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 3, research0046.1-0046.9.
  • 191
    Hermjakob H, Montecchi-Palazzi L, Bader G, Wojcik J, Salwinski L, Ceol A, Moore S, Orchard S, Sarkans U, von Mering C, et al. (2004) The HUPO PSI's molecular interaction format – a community standard for the representation of protein interaction data. Nat Biotechnol 22, 177183.
  • 192
    Garwood KL, McLaughlin T, Garwood C, Joens S, Morrison N, Taylor CF, Carroll K, Evans C, Whetton AD, Hart S, et al. (2004) PEDRo: a database for storing, searching and disseminating experimental proteomics data. BMC Genomics, doi:10.1186/1471-2164-5-68.
  • 193
    Orchard S, Hermjakob H & Apweiler R (2003) The proteomics standards initiative. Proteomics 3, 13741376.
  • 194
    Orchard S, Hermjakob H, Julian RK Jr, Runte K, Sherman D, Wojcik J, Zhu W & Apweiler R (2004) Common interchange standards for proteomics data: public availability of tools and schema. Proteomics 4, 490491.
  • 195
    Jenkins H, Hardy N, Beckmann M, Draper J, Smith AR, Taylor J, Fiehn O, Goodacre R, Bino R, Hall R, et al. (2004) A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 22, 16011606.
  • 196
    Lindon JC, Nicholson JK, Holmes E, Keun HC, Craig A, Pearce JT, Bruce SJ, Hardy N, Sansone SA, Antti H, et al. (2005) Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 23, 833838.
  • 197
    Xirasagar S, Gustafson S, Merrick BA, Tomer KB, Stasiewicz S, Chan DD, Yost KJ 3rd, Yates JR, 3rd, Sumner S, Xiao N, & Waters MD (2004) CEBS object model for systems biology data, SysBio-OM. Bioinformatics 20,15.
  • 198
    Achard F, Vaysseix G & Barillot E (2001) XML, bioinformatics and data integration. Bioinformatics 17, 115125.
  • 199
    Jones AR & Paton NW (2005) An analysis of extensible modelling for functional genomics data. BMC Bioinformatics 6, 235 ff.
  • 200
    Soldatova LN & King RD (2005) Are the current ontologies in biology good ontologies? Nat Biotechnol 23, 10951098.
  • 201
    Goble CA, Stevens R, Ng G, Bechhofer S, Paton NW, Baker PG, Peim M & Brass A (2001) Transparent access to multiple bioinformatics information sources. IBM Syst J 40, 532551.
  • 202
    Sauro HM, Hucka M, Finney A, Wellock C, Bolouri H, Doyle J & Kitano H (2003) Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. Omics 7, 355372.
  • 203
    Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A & Li P (2004) Taverna. a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20, 30453054.
  • 204
    Lu Q, Hao P, Curcin V, He W, Li YY, Luo QM, Guo YK & Li YX (2005) KDE Bioscience: Platform for bioinformatics analysis workflows. J Biomed Inform, doi:10.1016/j.jbi.2005.09.001.
  • 205
    Wilkinson MD & Links M (2002) BioMOBY: an open source biological web services proposal. Brief Bioinform 3, 331341.
  • 206
    Curcin V, Ghanem M & Guo Y (2005) Web services in the life sciences. Drug Discov Today 10, 865871.
  • 207
    Wilkinson M, Schoof H, Ernst R & Haase D (2005) BioMOBY successfully integrates distributed heterogeneous bioinformatics Web Services. The PlaNet exemplar case. Plant Physiol 138, 517.
  • 208
    Arkin AP (2001) Synthetic cell biology. Curr Opin Biotechnol 12, 638644.
  • 209
    Blake WJ & Isaacs FJ (2004) Synthetic biology evolves. Trends Biotechnol 22, 321324.
  • 210
    Ferber D (2004) Synthetic biology. Microbes made to order. Science 303, 158161.
  • 211
    Gibbs WW (2004) Synthetic life. Sci Am 290, 7481.
  • 212
    Sismour AM & Benner SA (2005) Synthetic biology. Expert Opin Biol Ther 5, 14091414.
  • 213
    Benner SA & Sismour AM (2005) Synthetic biology. Nat Rev Genet 6, 533543.
  • 214
    Hasty J, Isaacs F, Dolnik M, McMillen D & Collins JJ (2001) Designer gene networks: Towards fundamental cellular control. Chaos 11, 207220.
  • 215
    Hasty J, McMillen D & Collins JJ (2002) Engineered gene circuits. Nature 420, 224230.
  • 216
    Kærn M, Blake WJ & Collins JJ (2003) The engineering of gene regulatory networks. Annu Rev Biomed Eng 5, 179206.
  • 217
    Bailey JE (1991) Toward a science of metabolic engineering. Science 252, 16681675.
  • 218
    Stephanopoulos G & Vallino JJ (1991) Network rigidity and metabolic engineering in metabolite overproduction. Science 252, 16751681.
  • 219
    Stephanopoulos G & Sinskey AJ (1993) Metabolic engineering – methodologies and future prospects. Trends Biotechnol 11, 392396.
  • 220
    Keasling JD (1999) Gene-expression tools for the metabolic engineering of bacteria. Trends Biotechnol 17, 452460.
  • 221
    Stafford DE, Yanagimachi KS, Lessard PA, Rijhwani SK, Sinskey AJ & Stephanopoulos G (2002) Optimizing bioconversion pathways through systems analysis and metabolic engineering. Proc Natl Acad Sci USA 99, 18011806.
  • 222
    Khosla C & Keasling JD (2003) Metabolic engineering for drug discovery and development. Nat Rev Drug Discov 2, 10191025.
  • 223
    Sweetlove LJ, Last RL & Fernie AR (2003) Predictive metabolic engineering: a goal for systems biology. Plant Physiol 132, 420425.
  • 224
    Ulmer KM (1983) Protein engineering. Science 219, 666671.
  • 225
    Richardson JS & Richardson DC (1989) The de novo design of protein structures. Trends Biochem Sci 14, 304309.
  • 226
    Jones DT (1994) De novo protein design using pairwise potentials and a genetic algorithm. Protein Sci 3, 567574.
  • 227
    Tuchscherer G & Mutter M (1995) Templates in protein de novo design. J Biotechnol 41, 197210.
  • 228
    Dahiyat BI & Mayo SL (1997) De novo protein design: fully automated sequence selection. Science 278, 8287.
  • 229
    Liu LP & Deber CM (1998) Guidelines for membrane protein engineering derived from de novo designed model peptides. Biopolymers 47, 4162.
  • 230
    Hill RB, Raleigh DP, Lombardi A & DeGrado WF (2000) De novo design of helical bundles as models for understanding protein folding and function. Acc Chem Res 33, 745754.
  • 231
    Park S, Xi Y & Saven JG (2004) Advances in computational protein design. Curr Opin Struct Biol 14, 487494.
  • 232
    Park S, Kono H, Wang W, Boder ET & Saven JG (2005) Progress in the development and application of computational methods for probabilistic protein design. Comput Chem Eng 29, 407421.
  • 233
    Schueler-Furman O, Wang C, Bradley P, Misura K & Baker D (2005) Progress in modeling of protein structures and interactions. Science 310, 638642.
  • 234
    Bradley P, Misura KM & Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309, 18681871.
  • 235
    Gellman SH (1998) Foldamers: a manifesto. Acc Chem Res 31, 173180.
  • 236
    Cubberley MS & Iverson BL (2001) Models of higher-order structure: foldamers and beyond. Curr Opin Chem Biol 5, 650653.
  • 237
    Hill DJ, Mio MJ, Prince RB, Hughes TS & Moore JS (2001) A field guide to foldamers. Chem Rev 101, 38934012.
  • 238
    Cheng RP (2004) Beyond de novo protein design – de novo design of non-natural folded oligomers. Curr Opin Struct Biol 14, 512520.
  • 239
    Stemmer WPC (1994) Rapid evolution of a protein in vivo by DNA shuffling. Nature 370, 389391.
  • 240
    Stemmer WPC (1994) DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. Proc Natl Acad Sci USA 91, 1074710751.
  • 241
    Colas P, Cohen B, Jessen T, Grishina I, McCoy J & Brent R (1996) Genetic selection of peptide aptamers that recognize and inhibit cyclin-dependent kinase 2. Nature 380, 548550.
  • 242
    Boder ET, Midelfort KS & Wittrup KD (2000) Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity. Proc Natl Acad Sci USA 97, 1070110705.
  • 243
    Reetz MT & Jaeger K-E (2000) Enantioselective enzymes for organic synthesis created by directed evolution. Chemistry – A Eur J 6, 407412.
  • 244
    Arnold FH, Wintrode PL, Miyazaki K & Gershenson A (2001) How enzymes adapt: lessons from directed evolution. Trends Biochem Sci 26, 100106.
  • 245
    Arnold FH (2001) Combinatorial and computational challenges for biocatalyst design. Nature 409, 253257.
  • 246
    Alexeeva M, Carr R & Turner NJ (2003) Directed evolution of enzymes: new biocatalysts for asymmetric synthesis. Org Biomol Chem 1, 41334137.
  • 247
    Oates MJ, Corne DW & Kell DB (2003) The bimodal feature at large population sizes and high selection pressure: implications for directed evolution. Recent Advances in Simulated Evolution and Learning (Tan, K C Lim, M H Yao, X & Wang, L, eds), pp. 215240. World Scientific, Singapore.
  • 248
    Joyce GF (2004) Directed evolution of nucleic acid enzymes. Annu Rev Biochem 73, 791836.
  • 249
    Lutz S & Patrick WM (2004) Novel methods for directed evolution of enzymes: quality, not quantity. Curr Opin Biotechnol 15, 291297.
  • 250
    Williams GJ, Nelson AS & Berry A (2004) Directed evolution of enzymes for biocatalysis and the life sciences. Cell Mol Life Sci 61, 30343046.
  • 251
    Otten LG & Quax WJ (2005) Directed evolution: selecting today's biocatalysts. Biomol Eng 22, 19.
  • 252
    Reetz MT, Bocola M, Carballeira JD, Zha D & Vogel A (2005) Expanding the range of substrate acceptance of enzymes: combinatorial active-site saturation test. Angew Chem Int Ed Engl 44, 41924196.
  • 253
    Conrad RC, Giver L, Tian Y & Ellington AD (1996) In vitro selection of nucleic acid aptamers that bind proteins. Methods Enzymol 267, 336367.
  • 254
    Brody EN, Willis MC, Smith JD, Jayasena S, Zichi D & Gold L (1999) The use of aptamers in large arrays for molecular diagnostics. Mol Diagn 4, 381388.
  • 255
    Jayasena SD (1999) Aptamers: an emerging class of molecules that rival antibodies in diagnostics. Clin Chem 45, 16281650.
  • 256
    Famulok M, Mayer G & Blind M (2000) Nucleic acid aptamers-from selection in vitro to applications in vivo. Acc Chem Res 33, 591599.
  • 257
    Hermann T & Patel DJ (2000) Adaptive recognition by nucleic acid aptamers. Science 287, 820825.
  • 258
    Jhaveri SD, Kirby R, Conrad R, Maglott EJ, Bowser M, Kennedy RT, Glick G & Ellington AD (2000) Designed signaling aptamers that transduce molecular recognition to changes in fluorescence intensity. JACS 122, 24692473.
  • 259
    Stojanovic MN, de Prada P & Landry DW (2001) Aptamer-based folding fluorescent sensor for cocaine. J Am Chem Soc 123, 49284931.
  • 260
    Gold L, Brody E, Heilig J & Singer B (2002) One, two, infinity: genomes filled with aptamers. Chem Biol 9, 12591264.
  • 261
    Cox JC, Hayhurst A, Hesselberth J, Bayer TS, Georgiou G & Ellington AD (2002) Automated selection of aptamers against protein targets translated in vitro: from gene to aptamer. Nucl Acids Res 30, e108.
  • 262
    Clark SL & Remcho VT (2002) Aptamers as analytical reagents. Electrophoresis 23, 13351340.
  • 263
    Luzi E, Minunni M, Tombelli S & Mascini M (2003) New trends in affinity sensing: aptamers for ligand binding. Trac 22, 810818.
  • 264
    Rimmele M (2003) Nucleic acid aptamers as tools and drugs: recent developments. Chembiochem 4, 963971.
  • 265
    Nutiu R, Yu, JM & Li Y (2004) Signaling aptamers for monitoring enzymatic activity and for inhibitor screening. Chembiochem 5, 11391144.
  • 266
    Stojanovic MN & Kolpashchikov DM (2004) Modular aptameric sensors. J Am Chem Soc 126, 92669270.
  • 267
    Blank M & Blind M (2005) Aptamers as tools for target validation. Curr Opin Chem Biol 9, 336342.
  • 268
    Famulok M & Mayer G (2005) Intramers and aptamers. applications in protein-function analyses and potential for drug screening. Chembiochem 6, 1926.
  • 269
    Famulok M (2005) Allosteric aptamers and aptazymes as probes for screening approaches. Curr Opin Mol Ther 7, 137143.
  • 270
    Nutiu R & Li Y (2005) In vitro selection of structure-switching signaling aptamers. Angew Chem Int Ed Engl 44, 10611065.
  • 271
    Nutiu R & Li Y (2005) Aptamers with fluorescence- signaling properties. Methods 37, 1625.
  • 272
    Stojanovic MN, Semova S, Kolpashchikov D, Macdonald J, Morgan C & Stefanovic D (2005) Deoxyribozyme-based ligase logic gates and their initial circuits. J Am Chem Soc 127, 69146915.
  • 273
    Tombelli S, Minunni M & Mascini M (2005) Analytical applications of aptamers. Biosens Bioelectron 20, 24242434.
  • 274
    Proske D, Blank M, Buhmann R & Resch A (2005) Aptamers-basic research, drug development, and clinical applications. Appl Microbiol Biotechnol 69, 367374.
  • 275
    Hitchens GD & Kell DB (1983) Uncouplers can shuttle rapidly between localised energy coupling sites during photophosphorylation by chromatophores of Rhodopseudomonas capsulata N22. Biochem J 212, 2530.
  • 276
    Westerhoff HV & Kell DB (1988) A control theoretical analysis of inhibitor titrations of metabolic channelling. Comments Mol Cell Biophys 5, 57107.
  • 277
    Schreiber SL (1998) Chemical genetics resulting from a passion for synthetic organic chemistry. Bioorg Medical Chem 6, 11271152.
  • 278
    Crews CM & Splittgerber U (1999) Chemical genetics: exploring and controlling cellular processes with chemical probes. Trends Biochem Sci 24, 317320.
  • 279
    Stockwell BR (2000) Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet 1, 116125.
  • 280
    Stockwell BR (2000) Frontiers in chemical genetics. Trends Biotechnol 18, 449455.
  • 281
    Zheng XF & Chan TF (2002) Chemical genomics: a systematic approach in biological research and drug discovery. Curr Issues Mol Biol 4, 3343.
  • 282
    Carroll PM, Dougherty B, Ross-Macdonald P, Browman K & FitzGerald K (2003) Model systems in drug discovery: chemical genetics meets genomics. Pharmacol Ther 99, 183220.
  • 283
    Zanders ED, Bailey DS & Dean PM (2002) Probes for chemical genomics by design. Drug Discovery Today 7, 711718.
  • 284
    Giaever G (2003) A chemical genomics approach to understanding drug action. Trends Pharmacol Sci 24, 444446.
  • 285
    Salemme FR (2003) Chemical genomics as an emerging paradigm for postgenomic drug discovery. Pharmacogenomics 4, 257267.
  • 286
    Brenner C (2004) Chemical genomics in yeast. Genome Biol 5, 240.
  • 287
    Darvas F, Dorman G, Krajcsi P, Puskas LG, Kovari Z, Lorincz Z & Urge L (2004) Recent advances in chemical genomics. Curr Medical Chem 11, 31193145.
  • 288
    Meisner NC, Hintersteiner M, Uhl V, Weidemann T, Schmied M, Gstach H & Auer M (2004) The chemical hunt for the identification of drugable targets. Curr Opin Chem Biol 8, 424431.
  • 289
    Shim JS & Kwon HJ (2004) Chemical genetics for therapeutic target mining. Expert Opin Ther Targets 8, 653661.
  • 290
    Wagner BK, Haggarty SJ & Clemons PA (2004) Chemical genomics: probing protein function using small molecules. Am J Pharmacogenomics 4, 313320.
  • 291
    Spring DR (2005) Chemical genetics to chemical genomics: small molecules offer big insights. Chem Soc Rev 34, 472482.
  • 292
    Smukste I & Stockwell BR (2005) Advances in chemical genetics. Annu Rev Genomics Hum Genet 6, 261286.
  • 293
    Haggarty SJ, Clemons PA, Wong JC & Schreiber SL (2004) Mapping chemical space using molecular descriptors and chemical genetics: deacetylase inhibitors. Comb Chem High Throughput Screen 7, 669676.
  • 294
    Fan QW, Specht KM, Zhang C, Goldenberg DD, Shokat KM & Weiss WA (2003) Combinatorial efficacy achieved through two-point blockade within a signaling pathway-a chemical genetic approach. Cancer Res 63, 89308938.
  • 295
    Tochtrop GP & King RW (2004) Target identification strategies in chemical genetics. Comb Chem High Throughput Screen 7, 677688.
  • 296
    Hastie T, Tibshirani R & Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer-Verlag, Berlin.
  • 297
    Han J & Kamber M (2001) Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco.
  • 298
    Ananiadou S & McNaught J (2006) Text Mining in Biology and Biomedicine. Artech House, London.
  • 299
    Swanson DR (1990) Medical literature as a potential source of new knowledge. Bull Medical Libr Assoc 78, 2937.
  • 300
    Hirschman L, Park JC, Tsujii J, Wong L & Wu CH (2002) Accomplishments and challenges in literature data mining for biology. Bioinformatics 18, 15531561.
  • 301
    Nenadic G, Spasic I & Ananiadou S (2003) Terminology-driven mining of biomedical literature. Bioinformatics 19, 938943.
  • 302
    Corney DP, Buxton BF, Langdon WB & Jones DT (2004) BioRAT: extracting biological information from full-length papers. Bioinformatics 20, 32063213.
  • 303
    Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A & Mazo I (2004) Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 20, 604611.
  • 304
    Hakenberg J, Schmeier S, Kowald A, Klipp E & Leser U (2004) Finding kinetic parameters using text mining. Omics 8, 131152.
  • 305
    Rzhetsky A, Iossifov I, Koike T, Krauthammer M, Kra P & Morris M., Yu, H, Duboue PA, Weng W, Wilbur WJ, Hatzivassiloglou V & Friedman C (2004) GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J Biomed Inform 37, 4353.
  • 306
    Hoffmann R, Krallinger M, Andres E, Tamames J, Blaschke C & Valencia A (2005) Text mining for metabolic pathways, signaling cascades, and protein networks. Sci STKE pe21.
  • 307
    Vailaya A, Bluvas P, Kincaid R, Kuchinsky A, Creech M & Adler A (2005) An architecture for biological information extraction and representation. Bioinformatics 21, 430438.
  • 308
    Spasic I, Ananiadou S, McNaught J & Kumar A (2005) Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics 6, 239251.
  • 309
    Chalfie M & Kain S (1998) Green Fluorescent Protein: Properties, Applications, and Protocols. Wiley-Liss, New York.
  • 310
    Uhlen M & Ponten F (2005) Antibody-based proteomics for human tissue profiling. Mol Cell Proteomics 4, 384393.
  • 311
    Fehr M, Lalonde S, Lager I, Wolff MW & Frommer WB (2003) In vivo imaging of the dynamics of glucose uptake in the cytosol of COS-7 cells by fluorescent nanosensors. J Biol Chem 278, 1912719133.
  • 312
    Famulok M (2004) Green fluorescent RNA. Nature 430, 976977.
  • 313
    Rosi NL & Mirkin CA (2005) Nanostructures in biodiagnostics. Chem Rev 105, 15471562.
  • 314
    Rotman B (1961) Measurement of activity of single molecules of β -D-galactosidase. Proc Natl Acad Sci 47, 19811991.
  • 315
    Xie XS & Lu HP (1999) Single-molecule enzymology. J Biol Chem 274, 1596715970.
  • 316
    Moore KJ, Turconi S, Ashman S, Ruediger M, Haupts U, Emerick V & Pope AJ (1999) Single molecule detection technologies in miniaturized high throughput screening: fluorescence correlation spectroscopy. J Biomolecular Screening 4, 335353.
  • 317
    Haupts U, Rüdiger M, Ashman S, Turconi S, Bingham R, Wharton C, Hutchinson J, Carey C, Moore KJ & Pope AJ (2003) Single-molecule detection technologies in miniaturized high-throughput screening: fluorescence intensity distribution analysis. J Biomol Screening 8, 1933.
  • 318
    Bannai M, Higuchi K, Akesaka T, Furukawa M, Yamaoka M, Sato K & Tokunaga K (2004) Single-nucleotide-polymorphism genotyping for whole-genome-amplified samples using automated fluorescence correlation spectroscopy. Anal Biochem 327, 215221.
  • 319
    Twist CR, Winson MK, Rowland JJ & Kell DB (2004) SNP detection using nanomolar nucleotides and single molecule fluorescence. Anal Biochem 327, 3544.
  • 320
    Bennett ST, Barnes C, Cox A, Davies L & Brown C (2005) Toward the $1000 human genome. Pharmacogenomics 6, 373382.
  • 321
    Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z et al. (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380.
  • 322
    Shendure J, Porreca GJ, Reppas NB, Lin X, McCutcheon JP, Rosenbaum AM, Wang MD, Zhang K, Mitra RD & Church GM (2005) Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 17281732.
  • 323
    Prigogine I (1980) From Being to Becoming: Time and Complexity in the Physical Sciences. W.H. Freeman, San Francisco.
  • 324
    Coveney P & Highfield R (1990) The Arrow of Time. W.H. Allen, London.
  • 325
    Nicolis G & Prigogine I (1977) Self-organization in Nonequilibrium Systems: From Dissipative Structures to Order Through Fluctuations. Wiley, New York.
  • 326
    Kauffman SA (1993) The Origins of Order. Oxford University Press, Oxford.
  • 327
    Holland JH (1998) Emergence. Helix, Reading, MA.
  • 328
    Johnson S (2001) Emergence: the Connected Lives of Ants, Brains, Cities and Software. Scribner, New York.
  • 329
    Barabási A-L (2002) Linked: the New Science of Networks. Perseus Publishing, Cambridge, MA.
  • 330
    Buchanan M (2002) Nexus: Small Worlds and the Groundbreaking Science of Networks. W.W. Norton, New York.
  • 331
    Coveney PV & Highfield RR (1995) Frontiers of Complexity. Faber & Faber, London.
  • 332
    Kauffman SA (1995) At Home in the Universe: the Search for Laws of Self-Organization and Complexity. Oxford University Press, Oxford.
  • 333
    Solé R & Goodwin B (2000) Signs of Life: How Complexity Pervades Biology. Basic Books, New York.
  • 334
    Lipton P (1991) Inference to the Best Explanation. Routledge, London.
  • 335
    Pearl J (2000) Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge.
  • 336
    Shipley B (2001) Cause and Correlation in Biology. A User's Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge University Press, Cambridge.
  • 337
    Mackay DJC (2003) Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge.
  • 338
    Laughlin RB (2005) A Different Universe: Reinventing Physics from the Bottom Down. Basic Books, New York.
  • 339
    Kell DB & Welch GR (1991) No turning back, Reductonism and Biological Complexity. Times Higher Educational (Suppl.) 9th August, p. 15.
  • 340
    Westerhoff HV (2001) The silicon cell, not dead but live!. Metab Eng 3, 207210.
  • 341
    Pe'er D, Regev A, Elidan G & Friedman N (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 (Suppl. 1), S215S224.
  • 342
    de la Fuente A, Brazhnik P & Mendes P (2002) Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet 18, 395398.
  • 343
    Kholodenko BN, Kiyatkin A, Bruggeman FJ, Sontag E, Westerhoff HV & Hoek JB (2002) Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. Proc Natl Acad Sci USA 99, 1284112846.
  • 344
    Stark J, Callard R & Hubank M (2003) From the top down: towards a predictive biology of signalling networks. Trends Biotechnol 21, 290293.
  • 345
    Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D & Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34, 166176.
  • 346
    King RD, Garrett SM & Coghill GM (2005) On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21, 20172026.
  • 347
    Sachs K & Perez O, Pe'er D, Lauffenburger DA & Nolan GP (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523529.
  • 348
    Corne D, Jerram NR, Knowles J & Oates M (2001) PESA-II: Region-based selection in evolutionary multiobjective optimization. Paper presented at the GECCO – Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco. CA.
  • 349
    Cornish-Bowden A, Hofmeyr J-HS & Cárdenas ML (1995) Strategies for manipulating metabolic fluxes in biotechnology. Bioorg Chem 23, 439449.
  • 350
    Fell DA & Thomas S (1995) Physiological control of metabolic flux: the requirement for multisite modulation. Biochem J 311, 3539.
  • 351
    Fell DA (1998) Increasing the flux in metabolic pathways: a metabolic control analysis perspective. Biotechnol Bioeng 58, 121124.
  • 352
    Cascante M, Boros LG, Comin-Anduix B, de Atauri P, Centelles JJ & Lee PW (2002) Metabolic control analysis in drug discovery and disease. Nat Biotechnol 20, 243249.
  • 353
    McCafferty DG, Cudic P, Yu MK, Behenna DC & Kruger R (1999) Synergy and duality in peptide antibiotic mechanisms. Curr Opin Chem Biol 3, 672680.
  • 354
    Borisy AA, Elliott PJ, Hurst NW, Lee MS, Lehar J, Price ER, Serbedzija G, Zimmermann GR, Foley MA, Stockwell BR & Keith CT (2003) Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci USA 100, 79777982.