Systematic applications of metabolomics in metabolic engineering

Metabolites

Volumn 2, Issue 4, 2012, Pages 1090-1122

  Dromms, Robert A ^a   Styczynski, Mark P ^a,b  

^a Georgia Institute of Technology   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Constraint based models; Flux balance analysis; Kinetic ODE models; Mass spectrometry; Metabolic engineering; Metabolic flux; Metabolic profiling; Metabolomics; Partial least squares regression; Principal components analysis

Indexed keywords

PROTEOME;

COMPUTER MODEL; DATA ANALYSIS; ENZYME ASSAY; GENOMICS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; HUMAN; KINETICS; METABOLIC ENGINEERING; METABOLOME; METABOLOMICS; NONHUMAN; PHENOTYPE; PROTEOMICS; REVIEW; TRANSCRIPTOMICS;

EID: 84940030208     PISSN: None     EISSN: 22181989     Source Type: Journal    
DOI: 10.3390/metabo2041090     Document Type: Review

References (199)

1. Stephanopoulos, G. Metabolic Fluxes and Metabolic Engineering. Metab. Eng. 1999, 1, 1-11.
2. Zaldivar, J.Z.; Borges, A.B.; Johansson, B.J.; Smits, H.S.; Villas-Bôas, S.V.-B.; Nielsen, J.N.; Olsson, L.O. Fermentation performance and intracellular metabolite patterns in laboratory and industrial xylose-fermenting Saccharomyces cerevisiae. Appl. Microbiol. Biot. 2002, 59, 436-442.
3. Sonderegger, M.; Sauer, U. Evolutionary Engineering of Saccharomyces cerevisiae for Anaerobic Growth on Xylose. Appl. Environ. Microb. 2003, 69, 1990-1998.
4. Sonderegger, M.; Jeppsson, M.; Hahn-Hägerdal, B.; Sauer, U. Molecular Basis for Anaerobic Growth of Saccharomyces cerevisiae on Xylose, Investigated by Global Gene Expression and Metabolic Flux Analysis. Appl. Environ. Microb. 2004, 70, 2307-2317.
5. Bro, C.; Regenberg, B.; Förster, J.; Nielsen, J. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab. Eng. 2006, 8, 102-111.
6. Meijer, S.; Panagiotou, G.; Olsson, L.; Nielsen, J. Physiological characterization of xylose metabolism in Aspergillus niger under oxygen-limited conditions. Biotechnol. Bioeng. 2007, 98, 462-475.
7. Panagiotou, G.; Andersen, M.R.; Grotkjær, T.; Regueira, T.B.; Hofmann, G.; Nielsen, J.; Olsson, L. Systems Analysis Unfolds the Relationship between the Phosphoketolase Pathway and Growth in Aspergillus nidulans. PLoS ONE 2008, 3, e3847.
8. Wisselink, H.W.; Toirkens, M.J.; Wu, Q.; Pronk, J.T.; van Maris, A.J.A. Novel Evolutionary Engineering Approach for Accelerated Utilization of Glucose, Xylose, and Arabinose Mixtures by Engineered Saccharomyces cerevisiae Strains. Appl. Environ. Microb. 2009, 75, 907-914.
9. Klimacek, M.; Krahulec, S.; Sauer, U.; Nidetzky, B. Limitations in Xylose-Fermenting Saccharomyces cerevisiae, Made Evident through Comprehensive Metabolite Profiling and Thermodynamic Analysis. Appl. Environ. Microb. 2010, 76, 7566-7574.
10. Hasunuma, T.; Sanda, T.; Yamada, R.; Yoshimura, K.; Ishii, J.; Kondo, A. Metabolic pathway engineering based on metabolomics confers acetic and formic acid tolerance to a recombinant xylose-fermenting strain of Saccharomyces cerevisiae. Microb. Cell Fact. 2011, 10, 2.
11. Koppram, R.; Albers, E.; Olsson, L. Evolutionary engineering strategies to enhance tolerance of xylose utilizing recombinant yeast to inhibitors derived from spruce biomass. Biotechnol. Biofuels 2012, 5, 32.
12. Zhang, W.; Geng, A. Improved ethanol production by a xylose-fermenting recombinant yeast strain constructed through a modified genome shuffling method. Biotechnol. Biofuels 2012, 5, 46.
13. Kresnowati, M.T.A.P.; van Winden, W.A.; van Gulik, W.M.; Heijnen, J.J. Dynamic in vivo metabolome response of Saccharomyces cerevisiae to a stepwise perturbation of the ATP requirement for benzoate export. Biotechnol. Bioeng. 2008, 99, 421-441.
14. Sillers, R.; Chow, A.; Tracy, B.; Papoutsakis, E.T. Metabolic engineering of the non-sporulating, non-solventogenic Clostridium acetobutylicum strain M5 to produce butanol without acetone demonstrate the robustness of the acid-formation pathways and the importance of the electron balance. Metab. Eng. 2008, 10, 321-332.
15. Song, H.; Jang, S.H.; Park, J.M.; Lee, S.Y. Modeling of batch fermentation kinetics for succinic acid production by Mannheimia succiniciproducens. Biochem. Eng. J. 2008, 40, 107-115.
16. Zhang, K.; Sawaya, M.R.; Eisenberg, D.S.; Liao, J.C. Expanding metabolism for biosynthesis of nonnatural alcohols. Proc. Natl. Acad. Sci. USA 2008,105, 20653-20658.
17. Hou, J.; Lages, N.F.; Oldiges, M.; Vemuri, G.N. Metabolic impact of redox cofactor perturbations in Saccharomyces cerevisiae. Metab. Eng. 2009, 11, 253-261.
18. Sillers, R.; Al-Hinai, M.A.; Papoutsakis, E.T. Aldehyde-alcohol dehydrogenase and/or thiolase overexpression coupled with CoA transferase downregulation lead to higher alcohol titers and selectivity in Clostridium acetobutylicum fermentations. Biotechnol. Bioeng. 2009, 102, 38-49.
19. Trinh, C.T.; Huffer, S.; Clark, M.E.; Blanch, H.W.; Clark, D.S. Elucidating mechanisms of solvent toxicity in ethanologenic Escherichia coli. Biotechnol. Bioeng. 2010, 106, 721-730.
20. Oh, E. L; Lu, S.M.; Park, C.H.; Oh, H.B.; Lee, S.Y.; Lee, J.W. Dynamic Modeling of Lactic Acid Fermentation Metabolism with Lactococcus lactis. J. Microbiol. Biotechn. 2011, 21, 162-169.
21. Pereira, F.B.; Guimarães, P.M.R.; Teixeira, J.A.; Domingues, L. Robust industrial Saccharomyces cerevisiae strains for very high gravity bio-ethanol fermentations. J. Biosci. Bioeng. 2011, 112, 130-136.
22. Trinh, C.T.; Li, J.; Blanch, H.W.; Clark, D.S. Redesigning Escherichia coli Metabolism for Anaerobic Production of Isobutanol. Appl. Environ. Microb. 2011, 77, 4894-4904.
23. Aboka, F.O.; van Winden, W.A.; Reginald, M.M.; van Gulik, W.M.; van de Berg, M.; Oudshoorn, A.; Heijnen, J.J. Identification of informative metabolic responses using a minibioreactor: a small step change in the glucose supply rate creates a large metabolic response in Saccharomyces cerevisiae. Yeast 2012, 29, 95-110.
24. Lu, M.; Lee, S.; Kim, B.; Park, C.; Oh, M.; Park, K.; Lee, S.Y.; Lee, J. Identification of Factors Regulating Escherichia coli 2,3-Butanediol Production by Continuous Culture and Metabolic Flux Analysis. J. Microbiol. Biotechn. 2012, 22, 659-667.
25. Villas-Bôas, S.G.; Åkesson, M.; Nielsen, J. Biosynthesis of glyoxylate from glycine in Saccharomyces cerevisiae. FEMS Yeast Res. 2005, 5, 703-709.
26. Wu, L.; van Dam, J.; Schipper, D.; Kresnowati, M.T.A.P.; Proell, A.M.; Ras, C.; van Winden, W.A.; van Gulik, W.M.; Heijnen, J.J. Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN. PK 113-7D following a Glucose Pulse. Appl. Environ. Microb. 2006, 72, 3566-3577.
27. Costenoble, R.; Müller, D.; Barl, T.; Van Gulik, W.M.; Van Winden, W.A.; Reuss, M.; Heijnen, J.J. 13C-Labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae. FEMS Yeast Res. 2007, 7, 511-526.
28. Kleijn, R.J.; Geertman, J.-M.A.; Nfor, B.K.; Ras, C.; Schipper, D.; Pronk, J.T.; Heijnen, J.J.; Van Maris, A.J.A.; Van Winden, W.A. Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived 13C-labelling data. FEMS Yeast Res. 2007, 7, 216-231.
29. Nasution, U.; van Gulik, W.M.; Ras, C.; Proell, A.; Heijnen, J.J. A metabolome study of the steady-state relation between central metabolism, amino acid biosynthesis and penicillin production in Penicillium chrysogenum. Metab. Eng. 2008, 10, 10-23.
30. Moxley, J.F.; Jewett, M.C.; Antoniewicz, M.R.; Villas-Boas, S.G.; Alper, H.; Wheeler, R.T.; Tong, L.; Hinnebusch, A.G.; Ideker, T.; Nielsen, J.; Stephanopoulos, G. Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl. Acad. Sci. USA 2009, 106, 6477-6482.
31. Suthers, P.F.; Chang, Y.J.; Maranas, C.D. Improved computational performance of MFA using elementary metabolite units and flux coupling. Metab. Eng. 2010, 12, 123-128.
32. Ravikirthi, P.; Suthers, P.F.; Maranas, C.D. Construction of an E. Coli genome-scale atom mapping model for MFA calculations. Biotechnol. Bioeng. 2011, 108, 1372-1382.
33. Wu, L.; Mashego, M.R.; van Dam, J.C.; Proell, A.M.; Vinke, J.L.; Ras, C.; van Winden, W.A.; van Gulik, W.M.; Heijnen, J.J. Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal. Biochem. 2005, 336, 164-171.
34. Buffscher, J.r.M.; Czernik, D.; Ewald, J.C.; Sauer, U.; Zamboni, N. Cross-Platform Comparison of Methods for Quantitative Metabolomics of Primary Metabolism. Anal. Chem. 2009, 81, 2135-2143.
35. Choi, J.; Antoniewicz, M.R. Tandem mass spectrometry: A novel approach for metabolic flux analysis. Metab. Eng. 2011, 13, 225-233.
36. Choi, J.; Grossbach, M.T.; Antoniewicz, M.R. Measuring Complete Isotopomer Distribution of Aspartate Using Gas Chromatography/Tandem Mass Spectrometry. Anal. Chem. 2012, 84, 4628-4632.
37. Srour, O.; Young, J.; Eldar, Y. Fluxomers: a new approach for 13C metabolic flux analysis. BMC Syst. Biol. 2011, 5, 129.
38. Chang, Y.; Suthers, P.F.; Maranas, C.D. Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments. Biotechnol. Bioeng. 2008, 100, 1039-1049.
39. Antoniewicz, M.R.; Kelleher, J.K.; Stephanopoulos, G. Elementary metabolite units (EMU): A novel framework for modeling isotopic distributions. Metab. Eng. 2007, 9, 68-86.
40. Young, J.D.; Walther, J.L.; Antoniewicz, M.R.; Yoo, H.; Stephanopoulos, G. An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis. Biotechnol. Bioeng. 2008, 99, 686-699.
41. Raamsdonk, L.M.; Teusink, B.; Broadhurst, D.; Zhang, N.; Hayes, A.; Walsh, M.C.; Berden, J.A.; Brindle, K.M.; Kell, D.B.; Rowland, J.J.; Westerhoff, H.V.; van Dam, K.; Oliver, S.G. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotech. 2001, 19, 45-50.
42. Allen, J.; Davey, H.M.; Broadhurst, D.; Heald, J.K.; Rowland, J.J.; Oliver, S.G.; Kell, D.B. Highthroughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotech. 2003, 21, 692-696.
43. Chen, H.; Pan, Z.; Talaty, N.; Raftery, D.; Cooks, R.G. Combining desorption electrospray ionization mass spectrometry and nuclear magnetic resonance for differential metabolomics without sample preparation. Rapid Commun. Mass Sp. 2006, 20, 1577-1584.
44. Sreekumar, A.; Poisson, L.M.; Rajendiran, T.M.; Khan, A.P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R.J.; Li, Y.; Nyati, M.K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G.S.; Ghosh, D.; Pennathur, S.; Alexander, D.C.; Berger, A.; Shuster, J.R.; Wei, J.T.; Varambally, S.; Beecher, C.; Chinnaiyan, A.M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457, 910-914.
45. Huang, Z.; Chen, Y.; Hang, W.; Gao, Y.; Lin, L.; Li, D.; Xing, J.; Yan, X. Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers. Metabolomics 2012, 1-11.
46. Krömer, J.O.; Sorgenfrei, O.; Klopprogge, K.; Heinzle, E.; Wittmann, C. In-Depth Profiling of Lysine-Producing Corynebacterium glutamicum by Combined Analysis of the Transcriptome, Metabolome, and Fluxome. J. Bacteriol. 2004, 186, 1769-1784.
47. Hua, Q.; Joyce, A.R.; Fong, S.S.; Palsson, B. Ø. Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Biotechnol. Bioeng. 2006, 95, 992-1002.
48. Mas, S.; Villas-Bôas, S.G.; Edberg Hansen, M.; Åkesson, M.; Nielsen, J. A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants. Biotechnol. Bioeng. 2007, 96, 1014-1022.
49. van der Werf, M.J.; Overkamp, K.M.; Muilwijk, B.; Koek, M.M.; van der Werff-van der Vat, B.J.C.; Jellema, R.H.; Coulier, L.; Hankemeier, T. Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources. Mol. Biosyst. 2008, 4, 315-327.
50. Kleijn, R.J.; Buescher, J.M.; Le Chat, L.; Jules, M.; Aymerich, S.; Sauer, U. Metabolic Fluxes during Strong Carbon Catabolite Repression by Malate in Bacillus subtilis. J. Biol. Chem. 2010, 285, 1587-1596.
51. Taymaz-Nikerel, H.; de Mey, M.; Ras, C.; ten Pierick, A.; Seifar, R.M.; van Dam, J.C.; Heijnen, J.J.; van Gulik, W.M. Development and application of a differential method for reliable metabolome analysis in Escherichia coli. Anal. Biochem. 2009, 386, 9-19.
52. Kümmel, A.; Ewald, J.C.; Fendt, S.-M.; Jol, S.J.; Picotti, P.; Aebersold, R.; Sauer, U.; Zamboni, N.; Heinemann, M. Differential glucose repression in common yeast strains in response to HXK2 deletion. FEMS Yeast Res. 2010, 10, 322-332.
53. Taymaz-Nikerel, H.; van Gulik, W.M.; Heijnen, J.J. Escherichia coli responds with a rapid and large change in growth rate upon a shift from glucose-limited to glucose-excess conditions. Metab. Eng. 2011, 13, 307-318.
54. Canelas, A.B.; Ras, C.; ten Pierick, A.; van Gulik, W.M.; Heijnen, J.J. An in vivo data-driven framework for classification and quantification of enzyme kinetics and determination of apparent thermodynamic data. Metab. Eng. 2011, 13, 294-306.
55. Piddocke, M.; Fazio, A.; Vongsangnak, W.; Wong, M.; Heldt-Hansen, H.; Workman, C.; Nielsen, J.; Olsson, L. Revealing the beneficial effect of protease supplementation to high gravity beer fermentations using "-omics" techniques. Microb. Cell Fact. 2011, 10, 27.
56. Carnicer, M.; Canelas, A.; ten Pierick, A.; Zeng, Z.; van Dam, J.; Albiol, J.; Ferrer, P.; Heijnen, J.; van Gulik, W. Development of quantitative metabolomics for Pichia pastoris. Metabolomics 2012, 8, 284-298.
57. Carnicer, M.; Pierich, A.; Dam, J.; Heijnen, J.; Albiol, J.; Gulik, W.; Ferrer, P. Quantitative metabolomics analysis of amino acid metabolism in recombinant pichia pastoris under different oxygen availability conditions. Microb. Cell Fact. 2012, 11, 83.
58. Dikicioglu, D.; Dunn, W.B.; Kell, D.B.; Kirdar, B.; Oliver, S.G. Short-and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment. Mol. Biosyst. 2012, 8, 1760-1774.
59. Fendt, S.-M.; Buescher, J.M.; Rudroff, F.; Picotti, P.; Zamboni, N.; Sauer, U. Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity. Mol. Syst. Biol. 2010, 6, 356.
60. Yuan, J.; Doucette, C.D.; Fowler, W.U.; Feng, X.-J.; Piazza, M.; Rabitz, H.A.; Wingreen, N.S.; Rabinowitz, J.D. Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli. Mol. Syst. Biol. 2009, 5, 302.
61. Buescher, J.M.; Moco, S.; Sauer, U.; Zamboni, N. Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry Method for Fast and Robust Quantification of Anionic and Aromatic Metabolites. Anal. Chem. 2010, 82, 4403-4412.
62. Christen, S.; Sauer, U. Intracellular characterization of aerobic glucose metabolism in seven yeast species by 13C flux analysis and metabolomics. FEMS Yeast Res. 2011, 11, 263-272.
63. McKee, A.; Rutherford, B.; Chivian, D.; Baidoo, E.; Juminaga, D.; Kuo, D.; Benke, P.; Dietrich, J.; Ma, S.; Arkin, A.; Petzold, C.; Adams, P.; Keasling, J.; Chhabra, S. Manipulation of the carbon storage regulator system for metabolite remodeling and biofuel production in Escherichia coli. Microb. Cell Fact. 2012, 11, 79.
64. Hirayama, A.; Nakashima, E.; Sugimoto, M.; Akiyama, S.-i.; Sato, W.; Maruyama, S.; Matsuo, S.; Tomita, M.; Yuzawa, Y.; Soga, T. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal. Bioanal. Chem. 2012, 404, 1-9.
65. Toya, Y.; Nakahigashi, K.; Tomita, M.; Shimizu, K. Metabolic regulation analysis of wild-type and arcA mutant Escherichia coli under nitrate conditions using different levels of omics data. Mol. Biosyst. 2012, 8, 2593-2604.
66. Canelas, A.B.; Harrison, N.; Fazio, A.; Zhang, J.; Pitkanen, J.-P.; van den Brink, J.; Bakker, B.M.; Bogner, L.; Bouwman, J.; Castrillo, J.I.; Cankorur, A.; Chumnanpuen, P.; Daran-Lapujade, P.; Dikicioglu, D.; van Eunen, K.; Ewald, J.C.; Heijnen, J.J.; Kirdar, B.; Mattila, I.; Mensonides, F.I.C.; Niebel, A.; Penttila, M.; Pronk, J.T.; Reuss, M.; Salusjarvi, L.; Sauer, U.; Sherman, D.; Siemann-Herzberg, M.; Westerhoff, H.; de Winde, J.; Petranovic, D.; Oliver, S.G.; Workman, C.T.; Zamboni, N.; Nielsen, J. Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains. Nat. Commun. 2010, 1, 145.
67. Cajka, T.; Riddellova, K.; Tomaniova, M.; Hajslova, J. Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition. Metabolomics 2011, 7, 500-508.
68. Villas-Bôas, S.G.; Moxley, J.F.; Akesson, M.; Stephanopoulos, G.; Nielsen, J. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem. J. 2005, 388, 669-677.
69. Zhang, X.; Asara, J.M.; Adamec, J.; Ouzzani, M.; Elmagarmid, A.K. Data pre-processing in liquid chromatography-mass spectrometry-based proteomics. Bioinformatics 2005, 21, 4054-4059.
70. Smith, C.A.; Want, E.J.; O'Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 2006, 78, 779-787.
71. Styczynski, M.P.; Moxley, J.F.; Tong, L.V.; Walther, J.L.; Jensen, K.L.; Stephanopoulos, G.N. Systematic Identification of Conserved Metabolites in GC/MS Data for Metabolomics and Biomarker Discovery. Anal. Chem. 2006, 79, 966-973.
72. Hoffmann, N.; Stoye, J. ChromA: signal-based retention time alignment for chromatography-mass spectrometry data. Bioinformatics 2009, 25, 2080-2081.
73. Xia, J.; Psychogios, N.; Young, N.; Wishart, D.S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009, 37, W652-W660.
74. Pluskal, T.; Castillo, S.; Villar-Briones, A.; Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 2010, 11, 395.
75. Aggio, R.; Villas-Bôas, S.G.; Ruggiero, K. Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS. Bioinformatics 2011, 27, 2316-2318.
76. Lommen, A.; Kools, H.J. MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware. Metabolomics 2012, 8, 719-726.
77. Tautenhahn, R.; Patti, G.J.; Rinehart, D.; Siuzdak, G. XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data. Anal. Chem. 2012, 84, 5035-5039.
78. Xia, J.; Mandal, R.; Sinelnikov, I.V.; Broadhurst, D.; Wishart, D.S. MetaboAnalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012, 40, W127-W133.
79. Dunn, W.B.; Broadhurst, D.I.; Atherton, H.J.; Goodacre, R.; Griffin, J.L. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem. Soc. Rev. 2011, 40, 387-426.
80. Devantier, R.; Scheithauer, B.; Villas-Bôas, S.G.; Pedersen, S.; Olsson, L. Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations. Biotechnol. Bioeng. 2005, 90, 703-714.
81. van den Berg, R.; Hoefsloot, H.; Westerhuis, J.; Smilde, A.; van der Werf, M. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7, 142.
82. Højer-Pedersen, J.; Smedsgaard, J.; Nielsen, J. The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry. Metabolomics 2008, 4, 393-405.
83. MacKenzie, D.A.; Defernez, M.; Dunn, W.B.; Brown, M.; Fuller, L.J.; de Herrera, S.R.M.S.; Günther, A.; James, S.A.; Eagles, J.; Philo, M.; Goodacre, R.; Roberts, I.N. Relatedness of medically important strains of Saccharomyces cerevisiae as revealed by phylogenetics and metabolomics. Yeast 2008, 25, 501-512.
84. Barrett, C.; Herrgard, M.; Palsson, B. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC Syst. Biol. 2009, 3, 30.
85. Chong, W.P.K.; Goh, L.T.; Reddy, S.G.; Yusufi, F.N.K.; Lee, D.Y.; Wong, N.S.C.; Heng, C.K.; Yap, M.G.S.; Ho, Y.S. Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture. Rapid Commun. Mass Sp. 2009, 23, 3763-3771.
86. Kamei, Y.; Tamura, T.; Yoshida, R.; Ohta, S.; Fukusaki, E.; Mukai, Y. GABA metabolism pathway genes, UGA1 and GAD1, regulate replicative lifespan in Saccharomyces cerevisiae. Biochem. Bioph. Res. Co. 2011, 407, 185-190.
87. Peres-Neto, P.R.; Jackson, D.A.; Somers, K.M. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data. An. 2005, 49, 974-997.
88. Wold, S.; Ruhe, A.; Wold, H.; Dunn, I., W. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses. SISC 1984, 5, 735-743.
89. Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemometr. 2002, 16, 119-128.
90. Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E.J.; Edlund, U.; Shockcor, J.P.; Gottfries, J.; Moritz, T.; Trygg, J. Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. Anal. Chem. 2007, 80, 115-122.
91. Musumarra, G.; Barresi, V.; Condorelli, D.F.; Fortuna, C.G.; Scirè, S. Potentialities of multivariate approaches in genome-based cancer research: identification of candidate genes for new diagnostics by PLS discriminant analysis. J. Chemometr. 2004, 18, 125-132.
92. Storey, J.D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 2003, 100, 9440-9445.
93. Westerhuis, J.; Hoefsloot, H.; Smit, S.; Vis, D.; Smilde, A.; van Velzen, E.; van Duijnhoven, J.; van Dorsten, F. Assessment of PLSDA cross validation. Metabolomics 2008, 4, 81-89.
94. Dunn, W.B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-McIntyre, S.; Anderson, N.; Brown, M.; Knowles, J.D.; Halsall, A.; Haselden, J.N.; Nicholls, A.W.; Wilson, I.D.; Kell, D.B.; Goodacre, R. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protocols 2011, 6, 1060-1083.
95. Broadhurst, D.; Kell, D. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006, 2, 171-196.
96. Culeddu, N.; Chessa, M.; Porcu, M.; Fresu, P.; Tonolo, G.; Virgilio, G.; Migaleddu, V. NMRbased metabolomic study of type 1 diabetes. Metabolomics 2012, 8, 1-8.
97. Sonkar, K.; Behari, A.; Kapoor, V.; Sinha, N. 1H NMR metabolic profiling of human serum associated with benign and malignant gallstone diseases. Metabolomics 2012, 1-14.
98. Blekherman, G.; Laubenbacher, R.; Cortes, D.; Mendes, P.; Torti, F.; Akman, S.; Torti, S.; Shulaev, V. Bioinformatics tools for cancer metabolomics. Metabolomics 2011, 7, 329-343.
99. Mashego, M.R.; Van Gulik, W.M.; Heijnen, J.J. Metabolome dynamic responses of Saccharomyces cerevisiae to simultaneous rapid perturbations in external electron acceptor and electron donor. FEMS Yeast Res. 2007, 7, 48-66.
100. Kresnowati, M.T.A.P.; van Winden, W.A.; Almering, M.J.H.; ten Pierick, A.; Ras, C.; Knijnenburg, T.A.; Daran-Lapujade, P.; Pronk, J.T.; Heijnen, J.J.; Daran, J.M. When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation. Mol. Syst. Biol. 2006, 2, 49.
101. Douma, R.; Batista, J.; Touw, K.; Kiel, J.; Krikken, A.; Zhao, Z.; Veiga, T.; Klaassen, P.; Bovenberg, R.; Daran, J.-M.; Heijnen, J.; van Gulik, W. Degeneration of penicillin production in ethanol-limited chemostat cultivations of Penicillium chrysogenum: A systems biology approach. BMC Syst. Biol. 2011, 5, 132.
102. Santos, C.N.S.; Stephanopoulos, G. Melanin-Based High-Throughput Screen for l-Tyrosine Production in Escherichia coli. Appl. Environ. Microb. 2008, 74, 1190-1197.
103. Tyo, K.E.; Zhou, H.; Stephanopoulos, G.N. High-Throughput Screen for Poly-3-Hydroxybutyrate in Escherichia coli and Synechocystis sp. Strain PCC6803. Appl. Environ. Microb. 2006, 72, 3412-3417.
104. Lee, S.K.; Chou, H.H.; Pfleger, B.F.; Newman, J.D.; Yoshikuni, Y.; Keasling, J.D. Directed Evolution of AraC for Improved Compatibility of Arabinose-and Lactose-Inducible Promoters. Appl. Environ. Microb. 2007, 73, 5711-5715.
105. Atsumi, S.; Liao, J.C. Directed Evolution of Methanococcus jannaschii Citramalate Synthase for Biosynthesis of 1-Propanol and 1-Butanol by Escherichia coli. Appl. Environ. Microb. 2008, 74, 7802-7808.
106. Leonard, E.; Ajikumar, P.K.; Thayer, K.; Xiao, W.-H.; Mo, J.D.; Tidor, B.; Stephanopoulos, G.; Prather, K.L.J. Combining metabolic and protein engineering of a terpenoid biosynthetic pathway for overproduction and selectivity control. Proc. Natl. Acad. Sci. USA 2010, 107, 13654-13659.
107. Hong, K.-K.; Vongsangnak, W.; Vemuri, G.N.; Nielsen, J. Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis. Proc. Natl. Acad. Sci. USA 2011, 108, 12179-12184.
108. Shen, C.R.; Lan, E.I.; Dekishima, Y.; Baez, A.; Cho, K.M.; Liao, J.C. Driving Forces Enable High-Titer Anaerobic 1-Butanol Synthesis in Escherichia coli. Appl. Environ. Microb. 2011, 77, 2905-2915.
109. Sun, Z.; Ning, Y.; Liu, L.; Liu, Y.; Sun, B.; Jiang, W.; Yang, C.; Yang, S. Metabolic engineering of the L-phenylalanine pathway in Escherichia coli for the production of S-or R-mandelic acid. Microb. Cell Fact. 2011, 10, 71.
110. Bailey, J.E.; Sburlati, A.; Hatzimanikatis, V.; Lee, K.; Renner, W.A.; Tsai, P.S. Inverse metabolic engineering: A strategy for directed genetic engineering of useful phenotypes. Biotechnol. Bioeng. 1996, 52, 109-121.
111. Yoshida, S.; Imoto, J.; Minato, T.; Oouchi, R.; Sugihara, M.; Imai, T.; Ishiguro, T.; Mizutani, S.; Tomita, M.; Soga, T.; Yoshimoto, H. Development of Bottom-Fermenting Saccharomyces Strains That Produce High SO2 Levels, Using Integrated Metabolome and Transcriptome Analysis. Appl. Environ. Microb. 2008, 74, 2787-2796.
112. Wisselink, H.W.; Cipollina, C.; Oud, B.; Crimi, B.; Heijnen, J.J.; Pronk, J.T.; van Maris, A.J.A. Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae. Metab. Eng. 2010, 12, 537-551.
113. Ding, M.-Z.; Zhou, X.; Yuan, Y.-J. Metabolome profiling reveals adaptive evolution of Saccharomyces cerevisiae during repeated vacuum fermentations. Metabolomics 2010, 6, 42-55.
114. Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545-15550.
115. Xia, J.; Wishart, D.S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010, 38, W71-W77.
116. Kizer, L.; Pitera, D.J.; Pfleger, B.F.; Keasling, J.D. Application of Functional Genomics to Pathway Optimization for Increased Isoprenoid Production. Appl. Environ. Microb. 2008, 74, 3229-3241.
117. Alsaker, K.V.; Paredes, C.; Papoutsakis, E.T. Metabolite stress and tolerance in the production of biofuels and chemicals: Gene-expression-based systems analysis of butanol, butyrate, and acetate stresses in the anaerobe Clostridium acetobutylicum. Biotechnol. Bioeng. 2010, 105, 1131-1147.
118. Cakir, T.; Kirdar, B.; Onsan, Z.I.; Ulgen, K.; Nielsen, J. Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae. BMC Syst. Biol. 2007, 1, 18.
119. Joshi, A.; Palsson, B.O. Metabolic dynamics in the human red cell: Part I-A comprehensive kinetic model. J. Theor. Biol. 1989, 141, 515-528.
120. Chassagnole, C.; Noisommit-Rizzi, N.; Schmid, J.W.; Mauch, K.; Reuss, M. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 2002, 79, 53-73.
121. van Eunen, K.; Bouwman, J.; Daran-Lapujade, P.; Postmus, J.; Canelas, A.B.; Mensonides, F.I.C.; Orij, R.; Tuzun, I.; van den Brink, J.; Smits, G.J.; van Gulik, W.M.; Brul, S.; Heijnen, J.J.; de Winde, J.H.; Teixeira de Mattos, M.J.; Kettner, C.; Nielsen, J.; Westerhoff, H.V.; Bakker, B.M. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J 2010, 277, 749-760.
122. van Eunen, K.; Kiewiet, J.A.L.; Westerhoff, H.V.; Bakker, B.M. Testing Biochemistry Revisited: How In Vivo Metabolism Can Be Understood from In Vitro Enzyme Kinetics. PLoS Comput. Biol. 2012, 8, e1002483.
123. Gutenkunst, R.N.; Waterfall, J.J.; Casey, F.P.; Brown, K.S.; Myers, C.R.; Sethna, J.P. Universally Sloppy Parameter Sensitivities in Systems Biology Models. PLoS Comput. Biol. 2007, 3, e189.
124. Savinell, J.M.; Palsson, B.O. Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. J. Theor. Biol. 1992, 154, 421-454.
125. Varma, A.; Palsson, B.O. Metabolic Capabilities of Escherichia coli: I. Synthesis of Biosynthetic Precursors and Cofactors. J. Theor. Biol. 1993, 165, 477-502.
126. Suthers, P.F.; Burgard, A.P.; Dasika, M.S.; Nowroozi, F.; Van Dien, S.; Keasling, J.D.; Maranas, C.D. Metabolic flux elucidation for large-scale models using 13C labeled isotopes. Metab. Eng. 2007, 9, 387-405.
127. Choi, H.S.; Kim, T.Y.; Lee, D.-Y.; Lee, S.Y. Incorporating metabolic flux ratios into constraintbased flux analysis by using artificial metabolites and converging ratio determinants. J. Biotechnol. 2007, 129, 696-705.
128. Schuetz, R.; Kuepfer, L.; Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 2007, 3, 119.
129. Nookaew, I.; Meechai, A.; Thammarongtham, C.; Laoteng, K.; Ruanglek, V.; Cheevadhanarak, S.; Nielsen, J.; Bhumiratana, S. Identification of flux regulation coefficients from elementary flux modes: A systems biology tool for analysis of metabolic networks. Biotechnol. Bioeng. 2007, 97, 1535-1549.
130. Chen, X.; Alonso, A.P.; Allen, D.K.; Reed, J.L.; Shachar-Hill, Y. Synergy between 13Cmetabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli. Metab. Eng. 2011, 13, 38-48.
131. Hyduke, D.R.; Schellenberger, J.; Que, R.; Fleming, R.M.T.; Thiele, I.; Orth, J.D.; Feist, A.M.; Zielinski, D.C.; Bordbar, A.; Lewis, N.E.; Rahmanian, S.; Kang, J.; Palsson, B.O. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protocols 2011, 6, 1290-1307.
132. Klamt, S.; Saez-Rodriguez, J.; Gilles, E. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst. Biol. 2007, 1, 2.
133. Wright, J.; Wagner, A. The Systems Biology Research Tool: evolvable open-source software. BMC Syst. Biol. 2008, 2, 55.
134. Rocha, I.; Maia, P.; Evangelista, P.; Vilaca, P.; Soares, S.; Pinto, J.; Nielsen, J.; Patil, K.; Ferreira, E.; Rocha, M. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 2010, 4, 45.
135. Feist, A.M.; Herrgard, M.J.; Thiele, I.; Reed, J.L.; Palsson, B.O. Reconstruction of biochemical networks in microorganisms. Nat. Rev. Micro. 2009, 7, 129-143.
136. Thiele, I.; Palsson, B.O. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protocols 2010, 5, 93-121.
137. Satish Kumar, V.; Dasika, M.; Maranas, C. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 2007, 8, 212.
138. Senger, R.S.; Papoutsakis, E.T. Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis. Biotechnol. Bioeng. 2008, 101, 1036-1052.
139. Barua, D.; Kim, J.; Reed, J.L. An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models. PLoS Comput. Biol. 2010, 6, e1000970.
140. Henry, C.S.; DeJongh, M.; Best, A.A.; Frybarger, P.M.; Linsay, B.; Stevens, R.L. Highthroughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotech. 2010, 28, 977-982.
141. Andersen, M.R.; Nielsen, M.L.; Nielsen, J. Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger. Mol. Syst. Biol. 2008, 4, 178.
142. Milne, C.; Eddy, J.; Raju, R.; Ardekani, S.; Kim, P.-J.; Senger, R.; Jin, Y.-S.; Blaschek, H.; Price, N. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst. Biol. 2011, 5, 130.
143. Feist, A.M.; Henry, C.S.; Reed, J.L.; Krummenacker, M.; Joyce, A.R.; Karp, P.D.; Broadbelt, L.J.; Hatzimanikatis, V.; Palsson, B.O. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 2007, 3, 121.
144. Orth, J.D.; Conrad, T.M.; Na, J.; Lerman, J.A.; Nam, H.; Feist, A.M.; Palsson, B.O. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 2011, 7, 535.
145. Nookaew, I.; Jewett, M.; Meechai, A.; Thammarongtham, C.; Laoteng, K.; Cheevadhanarak, S.; Nielsen, J.; Bhumiratana, S. The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst. Biol. 2008, 2, 71.
146. Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N.; Gutschow, Miriam V.; Jacobs, Jared M.; Bolival Jr, B.; Assad-Garcia, N.; Glass, John I.; Covert, Markus W. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 2012, 150, 389-401.
147. Kumar, A.; Suthers, P.; Maranas, C. MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics 2012, 13, 6.
148. Lee, J.M.; Gianchandani, E.P.; Papin, J.A. Flux balance analysis in the era of metabolomics. Brief. Bioinf. 2006, 7, 140-150.
149. Gianchandani, E.P.; Chavali, A.K.; Papin, J.A. The application of flux balance analysis in systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 2010, 2, 372-382.
150. Covert, M.W.; Schilling, C.H.; Palsson, B. Regulation of Gene Expression in Flux Balance Models of Metabolism. J. Theor. Biol. 2001, 213, 73-88.
151. Covert, M.W.; Palsson, B. Ø. Transcriptional Regulation in Constraints-based Metabolic Models of Escherichia coli. J. Biol. Chem. 2002, 277, 28058-28064.
152. Meadows, A.L.; Karnik, R.; Lam, H.; Forestell, S.; Snedecor, B. Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation. Metab. Eng. 2010, 12, 150-160.
153. Mahadevan, R.; Edwards, J.S.; Doyle Iii, F.J. Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli. Biophys. J. 2002, 83, 1331-1340.
154. Feng, X.; Xu, Y.; Chen, Y.; Tang, Y.J. Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1. PLoS Comput. Biol. 2012, 8, e1002376.
155. Covert, M.W.; Xiao, N.; Chen, T.J.; Karr, J.R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24, 2044-2050.
156. Lee, J.M.; Gianchandani, E.P.; Eddy, J.A.; Papin, J.A. Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks. PLoS Comput. Biol. 2008, 4, e1000086.
157. Herrgård, M.J.; Fong, S.S.; Palsson, B. Ø. Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles. PLoS Comput. Biol. 2006, 2, e72.
158. Kumar, V.S.; Maranas, C.D. GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions. PLoS Comput. Biol. 2009, 5, e1000308.
159. Zomorrodi, A.; Maranas, C. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst. Biol. 2010, 4, 178.
160. Oh, Y.-G.; Lee, D.-Y.; Lee, S.Y.; Park, S. Multiobjective flux balancing using the NISE method for metabolic network analysis. Biotechnol. Progr. 2009, 25, 999-1008.
161. Adadi, R.; Volkmer, B.; Milo, R.; Heinemann, M.; Shlomi, T. Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters. PLoS Comput. Biol. 2012, 8, e1002575.
162. Segrè, D.; Vitkup, D.; Church, G.M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 2002, 99, 15112-15117.
163. Burgard, A.P.; Pharkya, P.; Maranas, C.D. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 2003, 84, 647-657.
164. Patil, K.; Rocha, I.; Forster, J.; Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 2005, 6, 308.
165. Asadollahi, M.A.; Maury, J.; Patil, K.R.; Schalk, M.; Clark, A.; Nielsen, J. Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab. Eng. 2009, 11, 328-334.
166. Ranganathan, S.; Suthers, P.F.; Maranas, C.D. OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions. PLoS Comput. Biol. 2010, 6, e1000744.
167. Kummel, A.; Panke, S.; Heinemann, M. Putative regulatory sites unraveled by networkembedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2006, 2, 2006.0034.
168. Zamboni, N.; Kummel, A.; Heinemann, M. anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics 2008, 9, 199.
169. Karp, P.D.; Ouzounis, C.A.; Moore-Kochlacs, C.; Goldovsky, L.; Kaipa, P.; Ahrén, D.; Tsoka, S.; Darzentas, N.; Kunin, V.; López-Bigas, N. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 2005, 33, 6083-6089.
170. Schomburg, I.; Chang, A.; Schomburg, D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 2002, 30, 47-49.
171. de Matos, P.; Alcántara, R.; Dekker, A.; Ennis, M.; Hastings, J.; Haug, K.; Spiteri, I.; Turner, S.; Steinbeck, C. Chemical Entities of Biological Interest: an update. Nucleic Acids Res. 2010, 38, D249-D254.
172. Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28, 27-30.
173. Caspi, R.; Foerster, H.; Fulcher, C.A.; Kaipa, P.; Krummenacker, M.; Latendresse, M.; Paley, S.; Rhee, S.Y.; Shearer, A.G.; Tissier, C.; Walk, T.C.; Zhang, P.; Karp, P.D. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2008, 36, D623-D631.
174. Bolton, E.E.; Wang, Y.; Thiessen, P.A.; Bryant, S.H. Chapter 12 PubChem: Integrated Platform of Small Molecules and Biological Activities. In Annu. Rep. Comput. Chem.; Ralph, A.W., David, C.S., Eds.; Elsevier: Amsterdam, The Netherland, 2008; Volume 4, pp. 217-241.
175. Canelas, A.B.; van Gulik, W.M.; Heijnen, J.J. Determination of the cytosolic free NAD/NADH ratio in Saccharomyces cerevisiae under steady-state and highly dynamic conditions. Biotechnol. Bioeng. 2008, 100, 734-743.
176. Jol, S.J.; Kümmel, A.; Terzer, M.; Stelling, J.; Heinemann, M. System-Level Insights into Yeast Metabolism by Thermodynamic Analysis of Elementary Flux Modes. PLoS Comput. Biol. 2012, 8, e1002415.
177. Henry, C.S.; Broadbelt, L.J.; Hatzimanikatis, V. Thermodynamics-Based Metabolic Flux Analysis. Biophys. J. 2007, 92, 1792-1805.
178. Price, N.D.; Famili, I.; Beard, D.A.; Palsson, B. Ø. Extreme Pathways and Kirchhoff's Second Law. Biophys. J. 2002, 83, 2879-2882.
179. Schellenberger, J.; Lewis, N.E.; Palsson, B. Ø. Elimination of Thermodynamically Infeasible Loops in Steady-State Metabolic Models. Biophys. J. 2011, 100, 544-553.
180. Jankowski, M.D.; Henry, C.S.; Broadbelt, L.J.; Hatzimanikatis, V. Group Contribution Method for Thermodynamic Analysis of Complex Metabolic Networks. Biophys. J. 2008, 95, 1487-1499.
181. Finley, S.D.; Broadbelt, L.J.; Hatzimanikatis, V. Thermodynamic analysis of biodegradation pathways. Biotechnol. Bioeng. 2009, 103, 532-541.
182. Garg, S.; Yang, L.; Mahadevan, R. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling. BMC Research Notes 2010, 3, 125.
183. Bordel, S.; Nielsen, J. Identification of flux control in metabolic networks using non-equilibrium thermodynamics. Metab. Eng. 2010, 12, 369-377.
184. Hoppe, A.; Hoffmann, S.; Holzhutter, H.-G. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst. Biol. 2007, 1, 23.
185. Heijnen, J.J. Approximative kinetic formats used in metabolic network modeling. Biotechnol. Bioeng. 2005, 91, 534-545.
186. Savageau, M.A. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J. Theor. Biol. 1969, 25, 365-369.
187. Savageau, M.A. Mathematics of organizationally complex systems. Biomed. Biochim. Acta 1985, 44, 839-844.
188. Voit, E.O.; Savageau, M.A. Accuracy of alternative representations for integrated biochemical systems. Biochemistry 1987, 26, 6869-6880.
189. Voit, E.O. Modelling metabolic networks using power-laws and S-systems. Essays Biochem. 2008, 45, 29-40.
190. Steuer, R.; Gross, T.; Selbig, J.; Blasius, B. Structural kinetic modeling of metabolic networks. Proc. Natl. Acad. Sci. USA 2006, 103, 11868-11873.
191. Nikerel, I.E.; van Winden, W.; van Gulik, W.; Heijnen, J. A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics. BMC Bioinformatics 2006, 7, 540.
192. Nikerel, I.E.; van Winden, W.A.; Verheijen, P.J.T.; Heijnen, J.J. Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics. Metab. Eng. 2009, 11, 20-30.
193. Costa, R.S.; Machado, D.; Rocha, I.; Ferreira, E.C. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis-Menten and approximate kinetic equations. Biosystems 2010, 100, 150-157.
194. Ralser, M.; Wamelink, M.; Kowald, A.; Gerisch, B.; Heeren, G.; Struys, E.; Klipp, E.; Jakobs, C.; Breitenbach, M.; Lehrach, H.; Krobitsch, S. Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress. J. Biol. 2007, 6, 10.
195. Tran, L.M.; Rizk, M.L.; Liao, J.C. Ensemble Modeling of Metabolic Networks. Biophys. J. 2008, 95, 5606-5617.
196. Rizk, M.L.; Liao, J.C. Ensemble Modeling for Aromatic Production in Escherichia coli. PLoS ONE 2009, 4, e6903.
197. Contador, C.A.; Rizk, M.L.; Asenjo, J.A.; Liao, J.C. Ensemble modeling for strain development of l-lysine-producing Escherichia coli. Metab. Eng. 2009, 11, 221-233.
198. Yizhak, K.; Benyamini, T.; Liebermeister, W.; Ruppin, E.; Shlomi, T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 2010, 26, i255-i260.
199. Jamshidi, N.; Palsson, B. Ø. Mass Action Stoichiometric Simulation Models: Incorporating Kinetics and Regulation into Stoichiometric Models. Biophys. J. 2010, 98, 175-185.