New types of experimental data shape the use of enzyme kinetics for dynamic network modeling

FEBS Journal

Volumn 281, Issue 2, 2014, Pages 549-571

  Tummler, Katja ^a   Lubitz, Timo ^a   Schelker, Max ^a   Klipp, Edda ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

databases for kinetics; history of rate laws; mathematical modeling; Michaelis Menten kinetics; network related approach; parameter estimation; sensitivity analysis; standardization; systems biology; use of omics data

Indexed keywords

PROTEOME; TRANSCRIPTOME;

COMPLEX FORMATION; ENZYME ANALYSIS; ENZYME ASSAY; ENZYME KINETICS; ENZYME MECHANISM; GENOME; IN VITRO STUDY; IN VIVO STUDY; MATHEMATICAL MODEL; MOLECULAR BIOLOGY; PRIORITY JOURNAL; REVIEW;

DATABASES FOR KINETICS; HISTORY OF RATE LAWS; MATHEMATICAL MODELING; MICHAELIS-MENTEN KINETICS; NETWORK RELATED APPROACH; PARAMETER ESTIMATION; SENSITIVITY ANALYSIS; STANDARDIZATION; SYSTEMS BIOLOGY; USE OF OMICS DATA;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; ENZYME ASSAYS; ENZYMES; KINETICS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; MODELS, CHEMICAL; REFERENCE STANDARDS;

EID: 84892781633     PISSN: 1742464X     EISSN: 17424658     Source Type: Journal    
DOI: 10.1111/febs.12525     Document Type: Review

References (125)

1. Michaelis L, &, Menten MMI, (1913) Die Kinetik der Invertinwirkung. Biochem Ztg 49, 333-369.
2. Palsson B, (2002) In silico biology through 'omics'. Nat Biotechnol 20, 649-650.
3. Petelenz-Kurdziel E, Kuehn C, Nordlander B, Klein D, Hong K-K, Jacobson T, Dahl P, Schaber J, Nielsen J, Hohmann S, et al,. (2013) Quantitative analysis of glycerol accumulation, glycolysis and growth under hyper osmotic stress. PLoS Comput Biol 9, e1003084.
4. Wegner K, Bachmann A, Schad J-U, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingmüller U, Dooley S, &, Kummer U, (2012) Dynamics and feedback loops in the transforming growth factor β signaling pathway. Biophys Chem 162, 22-34.
5. Briggs GE, &, Haldane JBS, (1925) A note on the kinematics of enzyme action. Biochem J 19, 338-339.
6. Cornish-Bowden A, (2012) Fundamentals of Enzyme Kinetics. Wiley-Blackwell, Weinheim, Germany.
7. Rogers A, &, Gibon Y, (2009) Plant Metabolic Networks (, Schwender J, ed). Springer, New York.
8. Sauro H, (2012) Enzyme Kinetics for Systems Biology. Ambrosius Publishing, Seattle, USA.
9. Veisova D, Macakova E, Rezabkova L, Sulc M, Vacha P, Sychrova H, Obsil T, &, Obsilova V, (2012) Role of individual phosphorylation sites for the 14-3-3-protein-dependent activation of yeast neutral trehalase Nth1. Biochem J 443, 663-670.
10. Van Hoek P, Van Dijken JP, &, Pronk JT, (1998) Effect of specific growth rate on fermentative capacity of baker's yeast. Appl Environ Microbiol 64, 4226-4233.
11. Lineweaver H, Burk D, &, Deming WE, (1934) The dissociation constant of nitrogen-nitrogenase in Azotobacter. J Am Chem Soc 56, 225-230.
12. Dowd J, &, Riggs D, (1965) A comparison of estimates of Michaelis-Menten kinetic constants from various linear transformations. J Biol Chem 240, 863-869.
13. Woolf B, (1931) The addition compound theory of enzyme action. Biochem J 25, 342-348.
14. Hanes CS, (1932) Studies on plant amylases: the effect of starch concentration upon the velocity of hydrolysis by the amylase of germinated barley. Biochem J 26, 1406-1421.
15. Eadie G, (1942) The inhibition of cholinesterase by physostigmine and prostigmine. J Biol Chem 146, 85-93.
16. Hofstee BHJ, (1952) Specificity of esterases. I. Identification of two pancreatic aliesterases. J Biol Chem 199, 357-364.
17. Eisenthal R, &, Cornish-Bowden A, (1974) The direct linear plot. A new graphical procedure for estimating enzyme kinetic parameters. Biochem J 139, 715-720.
18. Smallbone K, Messiha HL, Carroll KM, Winder CL, Malys N, Dunn WB, Murabito E, Swainston N, Dada JO, Khan F, et al,. (2013) A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett 587, 2832-2841.
19. Cleland W, (1963) The kinetics of enzyme-catalyzed reactions with two or more substrates or products. Biochim Biophys Acta 67, 104-137.
20. Tipton K, &, Boyce S, (2000) History of the enzyme nomenclature system. Bioinformatics 16, 34-40.
21. Wong JT-F, &, Hanes CS, (1962) Kinetic formulations for enzymic reactions involving two substrates. Biochem Cell Biol 40, 763-804.
22. Koshland DE, (1959) Enzyme flexibility and enzyme action. J Cell Comp Physiol 54, 245-258.
23. Koshland DJ, (1953) Group Transfer as an Enzymatic Substitution Mechanism. Brookhaven National Laboratory, Upton. report number BNL-1502
24. Monod J, Changeux J-P, &, Jacob F, (1963) Allosteric proteins and cellular control systems. J Mol Biol 6, 306-329.
25. Monod J, Wyman J, &, Changeux J, (1965) On the nature of allosteric transitions: a plausible model. J Mol Biol 12, 88-118.
26. Changeux J-P, (2012) Allostery and the Monod-Wyman-Changeux model after 50 years. Annu Rev Biophys 41, 103-133.
27. Koshland DE, Némethy G, &, Filmer D, (1966) Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochemistry 5, 365-385.
28. Hill A, (1910) The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J Physiol 40, iv-vii.
29. Hofmeyr JS, &, Cornish-Bowden H, (1997) The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models. Bioinformatics 13, 377-385.
30. Michels PAM, (1997) Cell biology and metabolism: glycolysis in bloodstream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes. J Biol Chem 272, 3207-3215.
31. Heinrich R, &, Rapoport TA, (1973) Linear theory of enzymatic chains; its application for the analysis of the crossover theorem and of the glycolysis of human erythrocytes. Acta Biol Med Ger 31, 479-494.
32. Cornish-Bowden A, &, Cárdenas ML, (2001) Information transfer in metabolic pathways. Eur J Biochem 268, 6616-6624.
33. Heijnen JJ, (2005) Approximative kinetic formats used in metabolic network modeling. Biotechnol Bioeng 91, 534-545.
34. Cascante M, Franco R, &, Canela EI, (1989) Use of implicit methods from general sensitivity theory to develop a systematic approach to metabolic control II. Complex systems. Math Biosci 94, 289-309.
35. Heinrich R, &, Rapoport SM, (1977) Metabolic regulation and mathematical models. Prog Biophys Mol Biol 32, 1-82.
36. Kacser H, &, Burns JA, (1973) The control of flux. Symp Soc Exp Biol 27, 65-104.
37. Westerhoff H, &, van Dam K, (1987) Thermodynamics and Control of Biological Free-Energy Transduction. Elsevier, Amsterdam.
38. Visser D, &, Heijnen JJ, (2002) The mathematics of metabolic control analysis revisited. Metab Eng 4, 114-123.
39. Hatzimanikatis V, &, Bailey JE, (1997) Effects of spatiotemporal variations on metabolic control: approximate analysis using (log)linear kinetic models. Biotechnol Bioeng 54, 91-104.
40. Small JR, &, Kacser H, (1993) Responses of metabolic systems to large changes in enzyme activities and effectors. 2. The linear treatment of branched pathways and metabolite concentrations. Assessment of the general non-linear case. Eur J Biochem 213, 625-640.
41. Delgado JP, &, Liao JC, (1991) Identifying rate-controlling enzymes in metabolic pathways without kinetic parameters. Biotechnol Prog 7, 15-20.
42. Savageau M, &, Rosen R, (1976) Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. Addison-Wesley, Reading, MA.
43. Visser D, Van der Heijden R, Mauch K, Reuss M, &, Heijnen S, (2000) Tendency modeling: a new approach to obtain simplified kinetic models of metabolism applied to Saccharomyces cerevisiae. Metab Eng 2, 252-275.
44. Savageau MA, (1988) Introduction to S-systems and the underlying power-law formalism. Math Comput Model 11, 546-551.
45. Liebermeister W, &, Klipp E, (2006) Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 3, 41.
46. Liebermeister W, &, Klipp E, (2005) Biochemical networks with uncertain parameters. Syst Biol 152, 97-107.
47. Schomburg I, Chang A, Placzek S, Söhngen C, Rother M, Lang M, Munaretto C, Ulas S, Stelzer M, Grote A, et al,. (2013) BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res 41, D764-D772.
48. Wittig U, Kania R, Golebiewski M, Rey M, Shi L, Jong L, Algaa E, Weidemann A, Sauer-Danzwith H, Mir S, et al,. (2012) SABIO-RK-database for biochemical reaction kinetics. Nucleic Acids Res 40, D790-D796.
49. Liebermeister W, Uhlendorf J, &, Klipp E, (2010) Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation. Bioinformatics 26, 1528-1534.
50. Wegscheider R, (1911) Über simultane Gleichgewichte und die Beziehungen zwischen Thermodynamik und Reactionskinetik homogener Systeme. Monatshefte für Chemie 32, 849-906.
51. Mellors A, (1976) The haldane relationship enzymes & equilibrium. Biochemical Education 4, 71.
52. Liebermeister W, &, Klipp E, (2006) Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data. Theor Biol Med Model 3, 42.
53. Buescher JM, Liebermeister W, Jules M, Uhr M, Muntel J, Botella E, Hessling B, Kleijn RJ, Le Chat L, Lecointe F, et al,. (2012) Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science 335, 1099-1103.
54. Van Eunen K, Bouwman J, Daran-Lapujade P, Postmus J, Canelas AB, Mensonides FIC, Orij R, Tuzun I, Van den Brink J, Smits GJ, et al,. (2010) Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J 277, 749-760.
55. García-Contreras R, Vos P, Westerhoff HV, &, Boogerd FC, (2012) Why in vivo may not equal in vitro-new effectors revealed by measurement of enzymatic activities under the same in vivo-like assay conditions. FEBS J 279, 4145-4159.
56. Ellis RJ, (2001) Macromolecular crowding: obvious but underappreciated. Trends Biochem Sci 26, 597-604.
57. Schnell S, &, Turner TE, (2004) Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Prog Biophys Mol Biol 85, 235-260.
58. Minton AP, (2001) The influence of macromolecular crowding and macromolecular confinement on biochemical reactions in physiological media. J Biol Chem 276, 10577-10580.
59. Rohwer JM, Postma PW, Kholodenko BN, &, Westerhoff HV, (1998) Implications of macromolecular crowding for signal transduction and metabolite channeling. Proc Natl Acad Sci USA 95, 10547-10552.
60. Huang X, Holden HM, &, Raushel FM, (2001) Channeling of substrates and intermediates in enzyme-catalyzed reactions. Annu Rev Biochem 70, 149-180.
61. Grima R, &, Schnell S, (2006) A systematic investigation of the rate laws valid in intracellular environments. Biophys Chem 124, 1-10.
62. Berry H, (2002) Monte Carlo simulations of enzyme reactions in two dimensions: fractal kinetics and spatial segregation. Biophys J 83, 1891-1901.
63. Apweiler R, Cornish-Bowden A, Hofmeyr J-HS, Kettner C, Leyh TS, Schomburg D, &, Tipton K, (2005) The importance of uniformity in reporting protein-function data. Trends Biochem Sci 30, 11-12.
64. Kettner C, (2007) Good publication practice as a prerequisite for comparable enzyme data? In Silico Biol 7, S57-S64.
65. Taylor CF, Field D, Sansone S-A, Aerts J, Apweiler R, Ashburner M, Ball CA, Binz P-A, Bogue M, Booth T, et al,. (2008) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol 26, 889-896.
66. Reo NV, (2002) NMR-based metabolomics. Drug Chem Toxicol 25, 375-382.
67. Pan Z, &, Raftery D, (2007) Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 387, 525-527.
68. Ramautar R, Demirci A, &, de Jong GJ, (2006) Capillary electrophoresis in metabolomics. Trends Anal Chem 25, 455-466.
69. Dunn WB, &, Ellis DI, (2005) Metabolomics: current analytical platforms and methodologies. Trends Anal Chem 24, 285-294.
70. Büscher JM, Czernik D, Ewald JC, Sauer U, &, Zamboni N, (2009) Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal Chem 81, 2135-2143.
71. 13C-based flux analysis. Mol Syst Biol 2, 62.
72. Doerr A, (2012) Mass spectrometry-based targeted proteomics. Nat Methods 10, 23.
73. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, Chen S, Eddes J, Loevenich SN, &, Aebersold R, (2006) The PeptideAtlas project. Nucleic Acids Res 34, D655-D658.
74. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, Wenschuh H, &, Aebersold R, (2010) High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Methods 7, 43-46.
75. Gerber SA, Rush J, Stemman O, Kirschner MW, &, Gygi SP, (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci USA 100, 6940-6945.
76. Carroll KM, Simpson DM, Eyers CE, Knight CG, Brownridge P, Dunn WB, Winder CL, Lanthaler K, Pir P, Malys N, et al,. (2011) Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach. Mol Cell Proteomics 10, M111.007633.
77. Wong JWH, &, Cagney G, (2010) An overview of label-free quantitation methods in proteomics by mass spectrometry. Methods Mol Biol 604, 273-283.
78. Wang Z, Gerstein M, &, Snyder M, (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10, 57-63.
79. Vogel C, &, Marcotte EM, (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13, 227-232.
80. Stöcklein WF, Behrsing O, Scharte G, Micheel B, Benkert A, Schössler W, Warsinke A, &, Scheller FW, (2000) Enzyme kinetic assays with surface plasmon resonance (BIAcore) based on competition between enzyme and creatinine antibody. Biosens Bioelectron 15, 377-382.
81. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, &, Kanehisa M, (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 27, 29-34.
82. The UniProt Consortium (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40, D71-D75.
83. Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, et al,. (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4, 92.
84. Olivier BG, &, Snoep JL, (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20, 2143-2144.
85. Kreutz C, Bartolome Rodriguez MM, Maiwald T, Seidl M, Blum HE, Mohr L, &, Timmer J, (2007) An error model for protein quantification. Bioinformatics 23, 2747-2753.
86. Raue A, Schilling M, Bachmann J, Matteson A, Schelker M, Kaschek D, Hug S, Kreutz C, Harms BD, Theis FJ, et al,. (2013) Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE, 8, e74335.
87. Mendes P, &, Kell DB, (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14, 869-883.
88. McKay MD, Beckman RJ, &, Conover WJ, (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239-245.
89. Moles CGC, Mendes P, &, Banga JRJ, (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13, 2467-2474.
90. Ashyraliyev M, Fomekong-Nanfack Y, Kaandorp JA, &, Blom JG, (2009) Systems biology: parameter estimation for biochemical models. FEBS J 276, 886-902.
91. Zi Z, (2011) Sensitivity analysis approaches applied to systems biology models. IET Syst Biol 5, 336.
92. Zi Z, &, Klipp E, (2006) SBML-PET: a Systems Biology Markup Language-based parameter estimation tool. Bioinformatics 22, 2704-2705.
93. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, &, Kummer U, (2006) COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-3074.
94. Schmidt H, &, Jirstrand M, (2006) Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics 22, 514-515.
95. Maiwald T, &, Timmer J, (2008) Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24, 2037-2043.
96. Lubitz T, Schulz M, Klipp E, &, Liebermeister W, (2010) Parameter balancing in kinetic models of cell metabolism. J Phys Chem B 114, 16298-16303.
97. Alberty RA, &, Cornish-Bowden A, (1993) The pH dependence of the apparent equilibrium constant, K′, of a biochemical reaction. Trends Biochem Sci 18, 288-291.
98. Alberty RA, (1998) Calculation of standard transformed formation properties of biochemical reactants and standard apparent reduction potentials of half reactions. Arch Biochem Biophys 358, 25-39.
99. 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, 524-531.
100. Krause F, Uhlendorf J, Lubitz T, Schulz M, Klipp E, &, Liebermeister W, (2010) Annotation and merging of SBML models with semanticSBML. Bioinformatics 26, 421-422.
101. Kümmel A, Panke S, &, Heinemann M, (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7, 512.
102. Ederer M, &, Gilles ED, (2007) Thermodynamically feasible kinetic models of reaction networks. Biophys J 92, 1846-1857.
103. Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H, &, Herwig R, (2011) Systems Biology. John Wiley & Sons, Weinheim.
104. Chen WW, Niepel M, &, Sorger PK, (2010) Classic and contemporary approaches to modeling biochemical reactions. Genes Dev 24, 1861-1875.
105. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, &, Timmer J, (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25, 1923-1929.
106. Raue A, Kreutz C, Theis FJ, &, Timmer J, (2013) Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability. Philos Trans A Math Phys Eng Sci 371, 20110544, doi: 10.1098/rsta.2011.0544.
107. Hengl S, Kreutz C, Timmer J, &, Maiwald T, (2007) Data-based identifiability analysis of non-linear dynamical models. Bioinformatics 23, 2612-2618.
108. Yates JWT, Evans ND, &, Chappell MJ, (2009) Structural identifiability analysis via symmetries of differential equations. Automatica 45, 2585-2591.
109. Kreutz C, Raue A, Kaschek D, &, Timmer J, (2013) Profile likelihood in systems biology. FEBS J 280, 2564-2571.
110. Joshi M, Seidel-Morgenstern A, &, Kremling A, (2006) Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. Metab Eng 8, 447-455.
111. Kreutz C, Raue A, &, Timmer J, (2012) Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Syst Biol 6, 120.
112. Kass RE, Carlin BP, Gelman A, &, Neal RM, (1998) Markov chain Monte Carlo in practice: a roundtable discussion. Am Stat 52, 93-100.
113. Ingalls BP, &, Sauro HM, (2003) Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories. J Theor Biol 222, 23-36.
114. Schoeberl B, Pace EA, Fitzgerald JB, Harms BD, Xu L, Nie L, Linggi B, Kalra A, Paragas V, Bukhalid R, et al,. (2009) Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2, ra31.
115. Raia V, Schilling M, Böhm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, Möller P, et al,. (2011) Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets. Cancer Res 71, 1-12.
116. Sahle S, Mendes P, Hoops S, &, Kummer U, (2008) A new strategy for assessing sensitivities in biochemical models. Philos Trans A Math Phys Eng Sci 366, 3619-3631.
117. Balsa-Canto E, Alonso AA, &, Banga JR, (2008) Computational procedures for optimal experimental design in biological systems. IET Syst Biol 2, 163-172.
118. Schenkendorf R, Kremling A, &, Mangold M, (2009) Optimal experimental design with the sigma point method. IET Syst Biol 3, 10-23.
119. Raue A, Kreutz C, Maiwald T, Klingmuller U, &, Timmer J, (2011) Addressing parameter identifiability by model-based experimentation. IET Syst Biol 5, 120-130.
120. Raue A, Becker V, Klingmüller U, &, Timmer J, (2010) Identifiability and observability analysis for experimental design in nonlinear dynamical models. Chaos 20, 45105.
121. Van Riel NAW, (2006) Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments. Brief Bioinform 7, 364-374.
122. Franceschini G, &, Macchietto S, (2008) Model-based design of experiments for parameter precision: state of the art. Chem Eng Sci 63, 4846-4872.
123. Kreutz C, &, Timmer J, (2009) Systems biology: experimental design. FEBS J 276, 923-942.
124. Ghaemmaghami S, Huh W-K, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, &, Weissman JS, (2003) Global analysis of protein expression in yeast. Nature 425, 737-741.
125. Zampar GG, Kümmel A, Ewald J, Jol S, Niebel B, Picotti P, Aebersold R, Sauer U, Zamboni N, &, Heinemann M, (2013) Temporal system-level organization of the switch from glycolytic to gluconeogenic operation in yeast. Mol Syst Biol 9, 651.