Abate-Pella, D., Freund, D. M., Ma, Y., et al. (2015). Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods. Journal of Chromatography A,
1412, 43–51.
CAS
PubMed
PubMed Central
Article
Google Scholar
Alexander, J., Gildea, L., Balog, J., et al. (2016) A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: A prospective observational study of the iKnife. Surgical Endoscopy 1-10.
Allen, J. K., Davey, H. M., Broadhurst, D., et al. (2003). High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnology,
21, 692–696.
CAS
PubMed
Article
Google Scholar
Allen, J., Davey, H. M., Broadhurst, D., et al. (2004). Discrimination of the modes of action of antifungal substances by use of metabolic footprinting. Applied and Environmental Microbiology,
70, 6157–6165.
CAS
PubMed
PubMed Central
Article
Google Scholar
Ashton, P. M., Nair, S., Dallman, T., et al. (2015). MinION nanopore sequencing identifies the position and structure of a bacterial antibiotic resistance island. Nature Biotechnology,
33, 296–300.
CAS
PubMed
Article
Google Scholar
Athersuch, T. J., & Keun, H. C. (2015). Metabolic profiling in human exposome studies. Mutagenesis,
30(6), 755–762.
CAS
PubMed
Google Scholar
Ball, P., & Garwin, L. (1992). Science at the atomic scale. Nature,
355, 761–766.
Article
Google Scholar
Balog, J., Sasi-Szabó, L., Kinross, J., et al. (2013). Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Science Translational Medicine,
5, 194ra93.
PubMed
Article
CAS
Google Scholar
Begley, P., Francis-McIntyre, S., Dunn, W. B., et al. (2009). Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale non-targeted metabolomic studies of human serum. Analytical Chemistry,
81, 7038–7046.
CAS
PubMed
Article
Google Scholar
Bradbury, J., Genta-Jouve, G., Allwood, J. W., et al. (2015). MUSCLE: Automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis. Bioinformatics,
31, 975–977.
CAS
PubMed
Article
Google Scholar
Broadhurst, D., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics,
2, 171–196.
CAS
Article
Google Scholar
Brown, M., Dunn, W. B., Dobson, P., et al. (2009). Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst,
134, 1322–1332.
CAS
PubMed
Article
Google Scholar
Bu’lock, J. D. (1961). Intermediary metabolism and antibiotic synthesis. Advances in Applied Microbiology Physiology,
3, 293–333.
Article
Google Scholar
Bundy, J. G., Davey, M. P., & Viant, M. R. (2009). Environmental metabolomics: A critical review and future perspectives. Metabolomics,
5, 3–21.
CAS
Article
Google Scholar
Carbonell, P., Parutto, P., Herisson, J., et al. (2014). XTMS: Pathway design in an eXTended metabolic space. Nucleic Acids Research,
42, W389–W394.
CAS
PubMed
PubMed Central
Article
Google Scholar
Carbonell, P., Planson, A. G., & Faulon, J. L. (2013). Retrosynthetic design of heterologous pathways. Systems Metabolic Engineering,
985, 149–173.
CAS
Article
Google Scholar
Castrillo, J. I., & Oliver, S. G. (2004). Yeast as a touchstone in post-genomic research: Strategies for integrative analysis in functional genomics. Journal of Biochemistry and Molecular Biology,
37, 93–106.
CAS
PubMed
Article
Google Scholar
Castrillo, J. I., Zeef, L. A., Hoyle, D. C., et al. (2007). Growth control of the eukaryote cell: a systems biology study in yeast. Journal of Biology,
6, 4.
PubMed
PubMed Central
Article
Google Scholar
César-Razquin, A., Snijder, B., Frappier-Brinton, T., et al. (2015). A call for systematic research on solute carriers. Cell,
162, 478–487.
PubMed
Article
CAS
Google Scholar
Chance, B., & Williams, G. R. (1955). Respiratory enzymes in oxidative phosphorylation. III The steady state. Journal of Biological Chemistry,
217, 409–427.
CAS
PubMed
Google Scholar
Cobb, M. (2015). Life’s greatest secret: The race to crack the genetic code. London: Profile Books.
Google Scholar
Coles, S. J., Day, N. E., Murray-Rust, P., Rzepa, H. S., & Zhang, Y. (2005). Enhancement of the chemical semantic web through the use of InChI identifiers. Organic & Biomolecular Chemistry,
3, 1832–1834.
CAS
Article
Google Scholar
Cooks, R. G., Ouyang, Z., Takats, Z., & Wiseman, J. M. (2006). Ambient mass spectrometry. Science,
311, 1566–1570.
CAS
PubMed
Article
Google Scholar
Cornish-Bowden, A., & Cárdenas, M. L. (2001). Silent genes given voice. Nature,
409, 571–572.
CAS
PubMed
Article
Google Scholar
Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2015). Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chemical Society Reviews,
44, 1172–1239.
CAS
PubMed
Article
Google Scholar
Dalgliesh, C. E., Horning, E. C., Horning, M. G., Knox, K. L., & Yarger, K. (1966). A gas-liquid-chromatographic procedure for separating a wide range of metabolites occuring in urine or tissue extracts. Biochemical Journal,
101, 792–810.
CAS
PubMed
PubMed Central
Article
Google Scholar
Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews,
26, 51–78.
CAS
PubMed
PubMed Central
Article
Google Scholar
Dikicioglu, D., Pir, P., & Oliver, S. G. (2013). Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory. Biotechnology Journal,
8, 1017–1034.
CAS
PubMed
PubMed Central
Article
Google Scholar
Dobson, P. D., & Kell, D. B. (2008). Carrier-mediated cellular uptake of pharmaceutical drugs: An exception or the rule? Nature Reviews Drug Discovery,
7, 205–220.
CAS
PubMed
Article
Google Scholar
Dobson, P., Lanthaler, K., Oliver, S. G., & Kell, D. B. (2009a). Implications of the dominant role of cellular transporters in drug uptake. Current Topics in Medicinal Chemistry,
9, 163–184.
CAS
PubMed
Article
Google Scholar
Dobson, P. D., Patel, Y., & Kell, D. B. (2009b). “Metabolite-likeness” as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discovery Today,
14, 31–40.
CAS
PubMed
Article
Google Scholar
Dunn, W. B., Broadhurst, D., Begley, P., et al. (2011). TheHusermet consortium,procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols,
6, 1060–1083.
CAS
PubMed
Article
Google Scholar
Dunn, W. B., Broadhurst, D. I., Sasalu, D., et al. (2007). Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics,
3, 413–426.
CAS
Article
Google Scholar
Dunn, W. B., Erban, A., Weber, R. J. M., et al. (2013). Mass Appeal: Metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics,
9, S44–S66.
Article
CAS
Google Scholar
Dunn, W. B., Lin, W., Broadhurst, D., et al. (2015). Molecular phenotyping of a UK population: Defining the human serum metabolome. Metabolomics,
11, 9–26.
CAS
PubMed
Article
Google Scholar
Ellis, D. I., Muhamadali, H., Haughey, S. A., et al. (2015). Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis—moving out of the laboratory and into the food supply chain. Anal Meth,
7, 9401–9414.
Article
Google Scholar
Fell, D. A. (1992). Metabolic Control Analysis—a survey of its theoretical and experimental development. Biochemical Journal,
286, 313–330.
CAS
PubMed
PubMed Central
Article
Google Scholar
Fell, D. A. (1996). Understanding the control of metabolism. London: Portland Press.
Google Scholar
Fiehn, O. (2002). Metabolomics: The link between genotypes and phenotypes. Plant Molecular Biology,
48, 155–171.
CAS
PubMed
Article
Google Scholar
Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000). Metabolite profiling for plant functional genomics. Nature Biotechnology,
18, 1157–1161.
CAS
PubMed
Article
Google Scholar
Goffeau, A., Barrell, B. G., Bussey, H., et al. (1996). Life with 6000 genes. Science,
274, 546–567.
CAS
PubMed
Article
Google Scholar
Goodacre, R., Broadhurst, D., Smilde, A., et al. (2007). Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics,
3, 231–241.
CAS
Article
Google Scholar
Goodacre, R., Timmins, É. M., Burton, R., et al. (1998). Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology,
144, 1157–1170.
CAS
PubMed
Article
Google Scholar
Goodacre, R., Vaidyanathan, S., Bianchi, G., & Kell, D. B. (2002). Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils. Analyst,
127, 1457–1462.
CAS
PubMed
Article
Google Scholar
Goodacre, R., Vaidyanathan, S., Dunn, W. B. (2004). Metabolomics by numbers: acquiring and understanding global metabolite data. Trends in Biotechnology,
22, 245–252.
CAS
PubMed
Article
Google Scholar
Grant, B. R., Greenaway, W., & Whatley, F. R. (1988). Metabolic changes during development of Phytophthora palmivora examined by Gas-Chromatography Mass-Spectrometry. Journal of General Microbiology,
134, 1901–1911.
CAS
PubMed
Google Scholar
Grapov, D., Wanichthanarak, K., & Fiehn, O. (2015). MetaMapR: Pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics,
31, 2757–2760.
PubMed
PubMed Central
Article
Google Scholar
Greenaway, W., May, J., Scaysbrook, T., & Whatley, F. R. (1991). Identification by gas chromatography-mass spectrometry of 150 compounds in propolis. Zeitschrift für Naturforschung C,
46, 111–121.
CAS
Google Scholar
Griffin, J. L. (2006). The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philosophical Transactions of the Royal Society of London B,
361, 147–161.
Article
CAS
Google Scholar
Haug, K., Salek, R. M., Conesa, P., et al. (2013). MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Research,
41, D781–D786.
CAS
PubMed
Article
Google Scholar
Hediger, M. A., Romero, M. F., Peng, J. B., et al. (2004). The ABCs of solute carriers: Physiological, pathological and therapeutic implications of human membrane transport proteins. Pflügers Archiv,
447, 465–468.
CAS
PubMed
Article
Google Scholar
Heinrich, R., & Rapoport, T. A. (1974). A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. European Journal of Biochemistry,
42, 89–95.
CAS
PubMed
Article
Google Scholar
Heinrich, R., & Schuster, S. (1996). The regulation of cellular systems. New York: Chapman & Hall.
Book
Google Scholar
Heller, S., McNaught, A., Stein, S., et al. (2013). InChI—the worldwide chemical structure identifier standard. Journal of Cheminformatics,
5, 7.
CAS
PubMed
PubMed Central
Article
Google Scholar
Herrgård, M. J., Swainston, N., Dobson, P., et al. (2008). A consensus yeast metabolic network obtained from a community approach to systems biology. Nature Biotechnology,
26, 1155–1160.
PubMed
PubMed Central
Article
CAS
Google Scholar
Hirschman, J. E., Balakrishnan, R., Christie, K. R., et al. (2006). Genome Snapshot: A new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome. Nucleic Acids Research,
34, D442–D445.
CAS
PubMed
Article
Google Scholar
Honda, K., & Littman, D. R. (2016). The microbiota in adaptive immune homeostasis and disease. Nature,
535, 75–84.
CAS
PubMed
Article
Google Scholar
Horning, E. C., & Horning, M. G. (1971). Metabolic profiles: Gas-phase methods for analysis of metabolites. Clinical Chemistry,
17, 802–809.
CAS
PubMed
Google Scholar
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine,
2, e124.
PubMed
PubMed Central
Article
Google Scholar
Jeffryes, J. G., Colastani, R. L., Elbadawi-Sidhu, M., et al. (2015). MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. Journal of Cheminformatics,
7, 44.
PubMed
PubMed Central
Article
CAS
Google Scholar
Jenkins, H., Hardy, N., Beckmann, M., et al. (2004). A proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnology,
22, 1601–1606.
CAS
PubMed
Article
Google Scholar
Jewison, T., Knox, C., Neveu, V., et al. (2012). YMDB: The yeast metabolome database. Nucleic Acids Research,
40, D815–D820.
CAS
PubMed
Article
Google Scholar
Judson, H. F. (1979). The eighth day of creation: Makers of the revolution in biology. New York: Touchstone Books.
Google Scholar
Kacser, H., & Burns, J. A. (1973). The control of flux in Davies. In D. D. Davies (Ed.), Rate control of biological processes. symposium of the society for experimental biology (pp. 65–104). Cambridge: Cambridge University Press.
Google Scholar
Kacser, H., & Burns, J. A. (1981). The molecular basis of dominance. Genetics,
97, 639–666.
CAS
PubMed
PubMed Central
Google Scholar
Kaderbhai, N. N., Broadhurst, D. I., Ellis, D. I., et al. (2003). Functional genomics via metabolic footprinting: Monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comparative Functional Genomics,
4, 376–391.
CAS
PubMed
PubMed Central
Article
Google Scholar
Kell, D. B. (2004). Metabolomics and systems biology: making sense of the soup. Current Option in Microbiology,
7, 296–307.
CAS
Article
Google Scholar
Kell, D. B. (2006). Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Disc Today,
11, 1085–1092.
CAS
Article
Google Scholar
Kell, D. B. (2013). Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening, and knowledge of transporters: Where drug discovery went wrong and how to fix it. FEBS Journal,
280, 5957–5980.
CAS
PubMed
Article
Google Scholar
Kell, D. B. (2015a). The transporter-mediated cellular uptake of pharmaceutical drugs is based on their metabolite-likeness and not on their bulk biophysical properties: Towards a systems pharmacology. Perspectives on Science,
6, 66–83.
Article
Google Scholar
Kell, D. B. (2015b). What would be the observable consequences if phospholipid bilayer diffusion of drugs into cells is negligible? Trends in Pharmacological Sciences,
36, 15–21.
CAS
PubMed
Article
Google Scholar
Kell, D.B. (2016) How drugs pass through biological cell membranes—a paradigm shift in our understanding? Beilstein Magazine
2,
http://www.beilstein-institut.de/download/628/09_kell.pdf.
Kell, D. B., Brown, M., Davey, H. M., et al. (2005). Metabolic footprinting and Systems Biology: The medium is the message. Nature Reviews Microbiology,
3, 557–565.
CAS
PubMed
Article
Google Scholar
Kell, D. B., Dobson, P. D., Bilsland, E., & Oliver, S. G. (2013). The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: What we (need to) know and how we can do so. Drug Disc Today,
18, 218–239.
CAS
Article
Google Scholar
Kell, D. B., Dobson, P. D., & Oliver, S. G. (2011). Pharmaceutical drug transport: The issues and the implications that it is essentially carrier-mediated only. Drug Disc Today,
16, 704–714.
CAS
Article
Google Scholar
Kell, D. B., & Oliver, S. G. (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, 99–105.
PubMed
Article
Google Scholar
Kell, D. B., & Oliver, S. G. (2014). How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Frontiers in Pharmacology,
5, 231.
PubMed
PubMed Central
Article
CAS
Google Scholar
Kell, D. B., Potgieter, M., & Pretorius, E. (2015a). Individuality, phenotypic differentiation, dormancy and ‘persistence’ in culturable bacterial systems: Commonalities shared by environmental, laboratory, and clinical microbiology. F1000Research,
4, 179.
PubMed
PubMed Central
Google Scholar
Kell, D. B., Swainston, N., Pir, P., & Oliver, S. G. (2015b). Membrane transporter engineering in industrial biotechnology and whole-cell biocatalysis. Trends in Biotechnology,
33, 237–246.
CAS
PubMed
Article
Google Scholar
Kell, D. B., van Dam, K., & Westerhoff, H. V. (1989). Control analysis of microbial growth and productivity. Symp. Soc. Gen. Microbiol.,
44, 61–93.
CAS
Google Scholar
Kell, D. B., & Westerhoff, H. V. (1986). Metabolic control theory: Its role in microbiology and biotechnology. FEMS Microbiology Reviews,
39, 305–320.
CAS
Article
Google Scholar
Kenny, L. C., Broadhurst, D. I., Dunn, W., et al. (2010). Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension,
56, 741–749.
CAS
PubMed
Article
Google Scholar
Kenny, L. C., Dunn, W. B., Ellis, D. I., et al. (2005). Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics,
1, 227–234. doi:10.1007/s11306-005-0003-1.
Article
CAS
Google Scholar
Kilianski, A., Haas, J. L., Corriveau, E. J., et al. (2015). Bacterial and viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer. Gigascience,
4, 12.
PubMed
PubMed Central
Article
CAS
Google Scholar
Kind, T., & Fiehn, O. (2007). Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics,
8, 105.
PubMed
PubMed Central
Article
CAS
Google Scholar
King, R. D., Rowland, J., Oliver, S. G., et al. (2009). The automation of science. Science,
324, 85–89.
CAS
PubMed
Article
Google Scholar
King, R. D., Whelan, K. E., Jones, F. M., et al. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist. Nature,
427, 247–252.
CAS
PubMed
Article
Google Scholar
Koza, J. R. (2010). Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines,
11, 251–284.
Article
Google Scholar
Lanthaler, K., Bilsland, E., Dobson, P., et al. (2011). Genome-wide assessment of the carriers involved in the cellular uptake of drugs: A model system in yeast. BMC Biology,
9, 70.
CAS
PubMed
PubMed Central
Article
Google Scholar
Lewis, K., Epstein, S., D’Onofrio, A., & Ling, L. L. (2010). Uncultured microorganisms as a source of secondary metabolites. The Journal of Antibiotics (Tokyo),
63, 468–476.
CAS
Article
Google Scholar
Lewis, M.R., Pearce, J.T., Spagou, K., et al. (2016) Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. Analytical Chemistry. doi:10.1021/acs.analchem.6b01481.
Link, H., Fuhrer, T., Gerosa, L., Zamboni, N., & Sauer, U. (2015). Real-time metabolome profiling of the metabolic switch between starvation and growth. Nature Methods,
12, 1091–1097.
CAS
PubMed
Google Scholar
Makarov, A., Denisov, E., Kholomeev, A., et al. (2006). Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Analytical Chemistry,
78, 2113–2120.
CAS
PubMed
Article
Google Scholar
Mendes, P., Oliver, S. G., & Kell, D. B. (2015). Fitting transporter activities to cellular drug concentrations and fluxes: Why the bumblebee can fly. Trends in Pharmacological Sciences,
36, 710–723.
CAS
PubMed
PubMed Central
Article
Google Scholar
Meuzelaar, H. L. C., Haverkamp, J., & Hileman, F. D. (1982). Pyrolysis mass spectrometry of recent and fossil biomaterials. Amsterdam: Elsevier.
Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature,
518, 529–533.
CAS
PubMed
Article
Google Scholar
Nielsen, J., & Keasling, J. D. (2016). Engineering cellular metabolism. Cell,
164, 1185–1197.
CAS
PubMed
Article
Google Scholar
O’Hagan, S., Dunn, W. B., Brown, M., Knowles, J. D., & Kell, D. B. (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. Analytical Chemistry,
77, 290–303.
PubMed
Article
CAS
Google Scholar
O’Hagan, S., & Kell, D. B. (2015a). The apparent permeabilities of Caco-2 cells to marketed drugs: Magnitude, and independence from both biophysical properties and endogenite similarities. PeerJ,
3, e1405.
PubMed
PubMed Central
Article
Google Scholar
O’Hagan, S., & Kell, D. B. (2015b). Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites. Frontiers in Pharmacology,
6, 105.
PubMed
PubMed Central
Google Scholar
O’Hagan, S. and Kell, D.B. (2016) MetMaxStruct: A Tversky-similarity-based strategy for analysing the (sub)structural similarities of drugs and endogenous metabolites. Frontiers in Pharmacology, in press.
O’Hagan, S., Swainston, N., Handl, J., & Kell, D. B. (2015). A ‘rule of 0.5’ for the metabolite-likeness of approved pharmaceutical drugs. Metabolomics,
11, 323–339.
Article
CAS
Google Scholar
Oliver, S. G. (1996). From DNA sequence to biological function. Nature,
379, 597–600.
CAS
PubMed
Article
Google Scholar
Oliver, S. G. (2000). Guilt-by-association goes global. Nature,
403, 601–603.
CAS
PubMed
Article
Google Scholar
Oliver, S. G., Winson, M. K., Kell, D. B., & Baganz, F. (1998). Systematic functional analysis of the yeast genome. Trends in Biotechnology,
16, 373–378.
CAS
PubMed
Article
Google Scholar
Oliver, S. G., Vanderaart, Q. J. M., Agostonicarbone, M. L., et al. (1992). The complete DNA sequence of yeast chromosome III. Nature,
357, 38–46.
CAS
PubMed
Article
Google Scholar
Palsson, B. Ø. (2006). Systems biology: Properties of reconstructed networks. Cambridge: Cambridge University Press.
Book
Google Scholar
Potgieter, M., Bester, J., Kell, D. B., & Pretorius, E. (2015). The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiology Reviews,
39, 567–591.
PubMed
PubMed Central
Article
Google Scholar
Quanbeck, S. M., Brachova, L., Campbell, A. A., et al. (2012). Metabolomics as a hypothesis-generating functional genomics tool for the annotation of arabidopsis thaliana genes of “unknown function”. Frontiers in Plant Science,
3, 15.
PubMed
PubMed Central
Article
Google Scholar
Raamsdonk, L. M., Teusink, B., Broadhurst, D., et al. (2001). A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnology,
19, 45–50.
CAS
PubMed
Article
Google Scholar
Rappaport, S. M., Barupal, D. K., Wishart, D., Vineis, P., & Scalbert, A. (2014). The blood exposome and its role in discovering causes of disease. Environmental Health Perspectives,
122, 769–774.
PubMed
PubMed Central
Google Scholar
Rattray, N. J. W., Hamrang, Z., Trivedi, D. K., et al. (2014). Taking your breath away: metabolomics breathes life into personalized medicine. Trends in Biotechnology,
32, 538–548.
CAS
PubMed
Article
Google Scholar
Rocca-Serra, P., Salek, R. M., Arita, M., et al. (2016). Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics,
12, 14.
PubMed
Article
CAS
Google Scholar
Salek, R. M., Neumann, S., Schober, D., et al. (2015). COordination of Standards in MetabOlomicS (COSMOS): Facilitating integrated metabolomics data access. Metabolomics,
11, 1587–1597.
CAS
PubMed
PubMed Central
Article
Google Scholar
Salek, R. M., Steinbeck, C., Viant, M. R., et al. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience,
2, 13.
PubMed
PubMed Central
Article
CAS
Google Scholar
Sansone, S. A., Fan, T., Goodacre, R., et al. (2007). The metabolomics standards initiative. Nature Biotechnology,
25, 846–848.
CAS
PubMed
Article
Google Scholar
Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature,
529, 484–489.
CAS
PubMed
Article
Google Scholar
Skogerson, K., Wohlgemuth, G., Barupal, D. K., & Fiehn, O. (2011). The volatile compound binbase mass spectral database. BMC Bioinformatics,
12, 321.
CAS
PubMed
PubMed Central
Article
Google Scholar
Spjuth, O., Berg, A., Adams, S., & Willighagen, E. L. (2013). Applications of the InChI in cheminformatics with the CDK and Bioclipse. Journal of Cheminformatics,
5, 14.
CAS
PubMed
PubMed Central
Article
Google Scholar
Stajich, J. E., Dietrich, F. S., & Roy, S. W. (2007). Comparative genomic analysis of fungal genomes reveals intron-rich ancestors. Genome Biology,
8, R223.
PubMed
PubMed Central
Article
CAS
Google Scholar
Swainston, N., Smallbone, K., Hefzi, H., et al. (2016). Recon 2.2: From reconstruction to model of human metabolism. Metabolomics,
12, 109.
PubMed
PubMed Central
Article
CAS
Google Scholar
Teusink, B., Baganz, F., Westerhoff, H. V., & Oliver, S. G. (1998). Metabolic Control Analysis as a tool in the elucidation of the function of novel genes. In M. F. Tuite & A. J. P. Brown (Eds.), Methods in microbiology: Yeast gene analysis (pp. 297–336). London: Academic Press.
Chapter
Google Scholar
Thiele, I., Swainston, N., Fleming, R. M. T., et al. (2013). A community-driven global reconstruction of human metabolism. Nature Biotechnology,
31, 419–425.
CAS
PubMed
Article
Google Scholar
Vaidyanathan, S., Rowland, J. J., Kell, D. B., & Goodacre, R. (2001). Rapid discrimination of aerobic endospore-forming bacteria via electrospray ionization mass spectrometry of whole cell suspensions. Analytical Chemistry,
73, 4134–4144.
CAS
PubMed
Article
Google Scholar
Walter, R. P., Morris, J. G., & Kell, D. B. (1987). The roles of osmotic stress and water activity in the inhibition of the growth, glycolysis and glucose phosphotransferase system of Clostridium pasteurianum. Journal of General Microbiology,
133, 259–266.
CAS
PubMed
Google Scholar
Wang, Z., Klipfell, E., Bennett, B. J., et al. (2011). Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature,
472, 57–63.
CAS
PubMed
PubMed Central
Article
Google Scholar
Weber, R. J., Southam, A. D., Sommer, U., & Viant, M. R. (2011). Characterization of isotopic abundance measurements in high resolution FT-ICR and Orbitrap mass spectra for improved confidence of metabolite identification. Analytical Chemistry,
83, 3737–3743.
CAS
PubMed
Article
Google Scholar
Weininger, D. (1988). SMILES, a chemical language and information system.1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences,
28, 31–36.
CAS
Google Scholar
Westerhoff, H. V., Hellingwerf, K. J., & van Dam, K. (1983). Thermodynamic efficiency of microbial growth is low but optimal for maximal growth rate. Proceedings of the National Academy of Sciences of the United States of America,
80, 305–309.
CAS
PubMed
PubMed Central
Article
Google Scholar
Wikoff, W. R., Anfora, A. T., Liu, J., et al. (2009). Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proceedings of the National Academy of Sciences of the United States of America,
10, 3698–3703.
Article
Google Scholar
Wilkins, M. R., Pasquali, C., Appel, R. D., et al. (1996). From proteins to proteomes: Large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology,
14, 61–65.
CAS
PubMed
Article
Google Scholar
Williams, R. J. (1956). Biochemical Individuality. New York: John Wiley.
Google Scholar
Williams, K., Bilsland, E., Sparkes, A., et al. (2015). Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. Journal of the Royal Society, Interface,
12, 20141289.
PubMed
PubMed Central
Article
Google Scholar
Winter, G. E., Radic, B., Mayor-Ruiz, C., et al. (2014). The solute carrier SLC35F2 enables YM155-mediated DNA damage toxicity. Nature Chemical Biology,
10, 768–773.
CAS
PubMed
PubMed Central
Article
Google Scholar
Wishart, D. S., Jewison, T., Guo, A. C., et al. (2013). HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Research,
41, D801–D807.
CAS
PubMed
Article
Google Scholar
Zamboni, N., Fendt, S.-M., Ruhl, M., & Sauer, U. (2009). 13C-based metabolic flux analysis. Nature Protocols,
4, 878–892.
CAS
PubMed
Article
Google Scholar
Zelena, E., Dunn, W. B., Broadhurst, D., et al. (2009). Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Analytical Chemistry,
81, 1357–1364.
CAS
PubMed
Article
Google Scholar
Zhu, Z. J., Schultz, A. W., Wang, J., et al. (2013). Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nature Protocols,
8, 451–460.
CAS
PubMed
PubMed Central
Article
Google Scholar