Quantitative assignment of reaction directionality in constraint-based models of metabolism: Application to Escherichia coli

Biophysical Chemistry

Volumn 145, Issue 2-3, 2009, Pages 47-56

  Fleming, R M T ^a   Thiele, I ^a   Nasheuer, H P ^b  

^a UNIVERSITY OF ICELAND   (Iceland)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Constraint based modeling; Systems biology; Thermodynamics

Indexed keywords

GLUTAMIC ACID; LEUCINE AMINOTRANSFERASE;

ARTICLE; BACTERIAL METABOLISM; CONCENTRATION RESPONSE; CONTROLLED STUDY; EQUILIBRIUM CONSTANT; ESCHERICHIA COLI; IONIC STRENGTH; MATHEMATICAL MODEL; NONHUMAN; PH; PREDICTION; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; STOICHIOMETRY; SYSTEMS BIOLOGY; TEMPERATURE; THERMODYNAMICS;

BIOLOGICAL TRANSPORT; BIOMASS; ESCHERICHIA COLI; FEASIBILITY STUDIES; GENOME, BACTERIAL; GLUTAMIC ACID; MODELS, BIOLOGICAL; THERMODYNAMICS; UNCERTAINTY;

ESCHERICHIA COLI;

EID: 70350560332     PISSN: 03014622     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bpc.2009.08.007     Document Type: Article

References (61)

1. Palsson B.O. Systems Biology: Properties of Reconstructed Networks (2006), Cambridge University Press, Cambridge
2. I. Thiele and B. Ø. Palsson. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc, (in press), 2009.
3. Pál C., Papp B., Lercher M.J., Csermely P., Oliver S.G., and Hurst L.D. Chance and necessity in the evolution of minimal metabolic networks. Nature 440 7084 (2006) 667-670
4. Mahadevan R., and Schilling C.H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5 4 (2003) 264-276
5. Burgard A.P., Nikolaev E.V., Schilling C.H., and Maranas C.D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14 2 (2004) 301-312
6. Barrett C.L., Herring C.D., Reed J.L., and Palsson B.O. The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states. Proc. Natl. Acad. Sci. U. S. A. 102 52 (2005) 19103-19108
7. Samal A., and Jain S. The regulatory network of E. coli metabolism as a Boolean dynamical system exhibits both homeostasis and flexibility of response. BMC Syst. Biol. 2 (2008) 21
8. Edwards J.S., Ibarra R.U., and Palsson B.O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19 2 (2001) 125-130
9. Ibarra R.U., Edwards J.S., and Palsson B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420 6912 (2002) 186-189
10. Covert M.W., Knight E.M., Reed J.L., Herrgard M.J., and Palsson B.O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429 6987 (2004) 92-96
11. Herrgård M.J., Fong S.S., and Palsson B.O. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comp. Bio. 2 7 (2006) e72
12. Reed J.L., Patel T.R., Chen K.H., Joyce A.R., Applebee M.K., Herring C.D., Bui O.T., Knight E.M., Fong S.S., and Palsson B.O. Systems approach to refining genome annotation. Proc. Natl. Acad. Sci. U. S. A. 103 46 (2006) 17480-17484
13. Burgard A.P., Pharkya P., and Maranas C.D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84 6 (2003) 647-657
14. Patil K.R., Rocha I., Förster J., and Nielsen J. Evolutionary programming as a platform for in silico metabolic engineering. BioMed. Central Bioinformatics 6 (2005) 308
15. Park J.H., Lee K.H., Kim T.Y., and Lee S.Y. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. U. S. A. 104 19 (2007) 7797-7802
16. Feist A.M., and Palsson B.O. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat. Biotechnol. 26 6 (2008) 659-667
17. Savinell J.M., and Palsson B.O. Network analysis of intermediary metabolism using linear optimization. i. development of mathematical formalism. J. Theor. Biol. 154 4 (1992) 421-454
18. Schuetz R., Kuepfer L., and Sauer U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3 (2007) e119
19. Henry C.S., Jankowski M.D., Broadbelt L.J., and Hatzimanikatis V. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J. 90 4 (2006) 1453-1461
20. Henry C.S., Broadbelt L.J., and Hatzimanikatis V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92 5 (2007) 1792-1805
21. Kümmel A., Panke S., and Heinemann M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2 (2006) e34
22. Kümmel A., Panke S., and Heinemann M. Systematic assignment of thermodynamic constraints in metabolic network models. BioMed. Central Bioinformatics 7 1 (2006) 512
23. Alberty R.A. A short history of the thermodynamics of enzyme-catalyzed reactions. J. Biol. Chem. 279 27 (2004) 27831-27836
24. Burton K., Krebs H.A., and Kornberg H.L. Energy Transformations in Living Matter (1957), Springer-Verlag, Berlin
25. Yang F., Qian H., and Beard D.A. Ab initio prediction of thermodynamically feasible reaction directions from biochemical network stoichiometry. Metab. Eng. 7 4 (2005) 251-259
26. Feng Y., and Beard D.A. Thermodynamically based profiling of drug metabolism and drug-drug metabolic interactions: a case study of acetaminophen and ethanol toxic interaction. Biophys. Chem. 120 2 (2006) 121-134
27. Heuett W.J., and Qian H. Combining flux and energy balance analysis to model large-scale biochemical networks. J. Bioinform. Comput. Biol. 4 6 (2006) 1227-1243
28. Feist A.M., Henry C.S., Reed J.L., Krummenacker M., Joyce A.R., Karp P.D., Broadbelt L.J., Hatzimanikatis V., and 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. 3 1 (2007) e121
29. Alberty R.A. Thermodynamics of Biochemical Reactions (2003), Wiley-Interscience, Hoboken, NJ
30. Alberty R.A. Biochemical Thermodynamics: Applications of Mathematica (2006), Wiley-Interscience, Hoboken, NJ
31. Jankowski M.D., Henry C.S., Broadbelt L.J., and Hatzimanikatis V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 95 3 (2008) 1487-1499
32. Mavrovouniotis M.L. Estimation of standard Gibbs energy changes of biotransformations. J. Biol. Chem. 266 22 (1991) 14440-14445
33. Varma A., and Palsson B.O. Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors. J. Theor. Biol. 165 4 (1993) 477-502
34. Varma A., and Palsson B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60 10 (1994) 3724-3731
35. Ross J. Thermodynamics and fluctuations far from equilibrium, volume 80 of Springer Series in Chemical Physics (2008), Springer, New York
36. Schnell S., and Turner T.E. Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Prog. Biophys. Mol. Biol. 85 2-3 (2004) 235-260
37. Minton A.P. How can biochemical reactions within cells differ from those in test tubes?. J. Cell Sci. 119 14 (2006) 2863
38. Verkman A.S. Solute and macromolecule diffusion in cellular aqueous compartments. Trends Biochem. Sci. 27 1 (2002) 27-33
39. Kao H.P., Abney J.R., and Verkman A.S. Determinants of the translational mobility of a small solute in cell cytoplasm. J. Cell Biol. 120 1 (1993) 175-184
40. Esther B.-O., Mahmood N.A.B.N., and Bert P. A sensor for intracellular ionic strength. Proc. Natl. Acad. Sci. U. S. A. 103 28 (2006) 10624-10629
41. Dill K.A., and Bromberg S. Molecular driving forces: Statistical thermodynamics in Chemistry and Biology (2003), Garland Science, London
42. F. C. Neidhardt (Ed. in Chief), R. Curtis, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, M. Schaechter, and H. E. Umbarger (eds). Escherichia coli and Salmonella: Cellular and Molecular Biology. American Society for Microbiology Press, Washington, DC, 1996.
43. Padan E., and Schuldiner S. Intracellular pH and membrane potential as regulators in the prokaryotic cell. J. Membr. Biol. 95 3 (1987) 189-198
44. Wilks J.C., and Slonczewski J.L. pH of the cytoplasm and periplasm of Escherichia coli: rapid measurement by green fluorescent protein fluorimetry. J. Bacteriol. 189 15 (2007) 5601-5607
45. Slonczewski J.L., Macnab R.M., Alger J.R., and Castle A.M. Effects of pH and repellent tactic stimuli on protein methylation levels in Escherichia coli. J. Bacteriol. 152 1 (1982) 384-399
46. Smith W.R., and Missen R.W. Chemical Reaction Equilibrium Analysis: Theory and Algorithms (1982), Wiley, New York
47. Fersht A. Structure and mechanism in protein science: A guide to enzyme catalysis and protein folding (1999), W.H. Freeman, New York
48. Fiehn O. Metabolomics: the link between genotypes and phenotypes. Plant. Mol. Biol. 48 1-2 (2002) 155-171
49. Brauer M.J., Yuan J., Bennett B.D., Lu W., Kimball E., Botstein D., and Rabinowitz J.D. Conservation of the metabolomic response to starvation across two divergent microbes. Proc. Natl. Acad. Sci. U. S. A. 103 51 (2006) 19302-19307
50. Ishii N., Nakahigashi K., Baba T., Robert M., Soga T., Kanai A., Hirasawa T., Naba M., Hirai K., Hoque A., Ho P.Y., Kakazu Y., Sugawara K., Igarashi S., Harada S., Masuda T., Sugiyama N., Togashi T., Hasegawa M., Takai Y., Yugi K., Arakawa K., Iwata N., Toya Y., Nakayama Y., Nishioka T., Shimizu K., Mori H., and Tomita M. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316 5824 (2007) 593-597
51. Bennett B.D., Kimball E.H., Gao M., Osterhout R., Van Dien S.J., and Rabinowitz J.D. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5 8 (2009) 593-599
52. Gowers T., Barrow-Green J., and Leader I. The Princeton Companion to Mathematics (2008), Princeton University Pres, Princeton
53. Richard H., and Foster J.W. Escherichia coli glutamate- and arginine-dependent acid resistance systems increase internal pH and reverse transmembrane potential. J. Bacteriol. 186 18 (2004) 6032-6041
54. Alberty R.A. Standard transformed Gibbs energies of coenzyme A derivatives as functions of pH and ionic strength. Biophys. Chem. 104 1 (2003) 327-334
55. Joyce A.R., Reed J.L., White A., Edwards R., Osterman A., Baba T., Mori H., Lesely S.A., Palsson B.O., and Agarwalla S. Experimental and computational assessment of conditionally essential genes in Escherichia coli?. J. Bacteriol. 188 23 (2006) 8259-8271
56. Yuan J., Fowler W.U., Kimball E., Lu W., and Rabinowitz J.D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli. Nat. Chem. Biol. 2 10 (2006) 529-530
57. Umbarger H.E. Amino acid biosynthesis and its regulation. Annu. Rev. Biochem. 47 (1978) 532-606
58. Tewari Y.B., Goldberg R.N., and Rozzell J.D. Thermodynamics of reactions catalysed by branched-chain-amino-acid transaminase. J. Chem. Thermodyn. 32 10 (2000) 1381-1398
59. Varma A., and Palsson B.O. Metabolic capabilities of Escherichia coli: II. Optimal growth patterns. J. Theor. Biol. 165 4 (1993) 503-522
60. Fischer E., Zamboni N., and Sauer U. High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13c constraints. Anal. Biochem. 325 2 (2004) 308-316
61. J.D. Orth, R.M.T. Fleming, and Bernhard Ø. Palsson. Escherichia coli and Salmonella: Cellular and Molecular Biology, chapter Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide (in press No). ASM Press, 2009.