Uniform sampling of steady-state flux spaces: Means to design experiments and to interpret enzymopathies

Biophysical Journal

Volumn 87, Issue 4, 2004, Pages 2172-2186

  Price, Nathan D ^a   Schellenberger, Jan ^a   Palsson, Bernhard O ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGY; CALCULATION; CELL FUNCTION; CORRELATION COEFFICIENT; ERYTHROCYTE; GENOMICS; HUMAN; HUMAN CELL; INBORN ERROR OF METABOLISM; LAW; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEASUREMENT; METABOLISM; MONTE CARLO METHOD; PHYSICAL CHEMISTRY; PHYSIOLOGY; PREDICTION; SIMULATION; STEADY STATE; ANIMAL; BIOLOGICAL MODEL; COMPARATIVE STUDY; COMPUTER SIMULATION; ENZYMOLOGY; EVALUATION; GENE EXPRESSION REGULATION; SAMPLE SIZE; SIGNAL TRANSDUCTION; STATISTICAL MODEL; VALIDATION STUDY;

MULTIENZYME COMPLEX; PLASMA PROTEIN;

ANIMALS; BLOOD PROTEINS; COMPUTER SIMULATION; ERYTHROCYTES; GENE EXPRESSION REGULATION, ENZYMOLOGIC; HUMANS; METABOLISM, INBORN ERRORS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MULTIENZYME COMPLEXES; SAMPLE SIZE; SIGNAL TRANSDUCTION;

EID: 8544235670     PISSN: 00063495     EISSN: None     Source Type: Journal    
DOI: 10.1529/biophysj.104.043000     Document Type: Article

References (57)

1. Almaas, E., B. Kovacs, T. Vicsek, Z. N. Oltvai, and A. L. Barabasi. 2004. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature. 427:839-843.
2. Alves, R., and M. A. Savageau. 2000a. Comparing systemic properties of ensembles of biological networks by graphical and statistical methods. Bioinformatics. 16:527-533.
3. Alves, R., and M. A. Savageau. 2000b. Extending the method of mathematically controlled comparison to include numerical comparisons. Bioinformatics. 16:786-798.
4. Alves, R., and M. A. Savageau. 2000c. Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways. Bioinformatics. 16:534-547.
5. Bonarius, H. P. J., G. Schmid, and J. Tramper. 1997. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol. 15:308-314.
6. Burgard, A. P., and C. D. Maranas. 2001. Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. Biotechnol. Bioeng. 74:364-375.
7. Burgard, A. P., and C. D. Maranas. 2003. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82:670-677.
8. Burgard, A. P., E. V. Nikolaev, C. H. Schilling, and C. D. Maranas. 2004. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14:301-312.
9. Burgard, A. P., S. Vaidyaraman, and C. D. Maranas. 2001. Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol. Prog. 17:791-797.
10. Chen, M. H., and B. Schmeiser. 1993. Performance of the Gibbs, hit-and-run and Metropolis samplers. J. Comput. Graph. Stat. 2:251-272.
11. Covert, M., I. Famili, and B. Palsson. 2003. Identifying the constraints that govern cell behavior: a key to converting conceptual to computational models in biology? Biotechnol. Bioeng. 84:763-772.
12. Edwards, J. S., and B. Palsson. O.2000. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformat. 1:1. Epub 2000 Jul 27.
13. Edwards, J. S., M. Covert, and B. Palsson. 2002. Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 4:133-140.
14. Edwards, J. S., R. U. Ibarra, and B. O. Palsson. 2001. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19:125-130.
15. Edwards, J. S., and B. O. Palsson. 1999. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274:17410-17416.
16. Famili, I., J. Forster, J. Nielsen, and B. O. Palsson. 2003. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA. 100:13134-13139.
17. Forster, J., I. Famili, B. O. Palsson, and J. Nielsen. 2003. Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Ohmics. 7:193-202.
18. Grimes, A. J. 1980. Human Red Cell Metabolism. Blackwell Scientific Publications, Oxford, UK.
19. Hasty, J., D. McMillen, and J. J. Collins. 2002. Engineered gene circuits. Nature. 420:224-230.
20. Hasty, J., D. McMillen, F. Isaacs, and J. J. Collins. 2001. Computational studies of gene regulatory networks: in numero molecular biology. Nat. Rev. Genet. 2:268-279.
21. Holzhütter, H. G., G. Jacobasch, and A. Bisdorff. 1985. Mathematical modelling of metabolic pathways affected by an enzyme deficiency. A mathematical model of glycolysis in normal and pyruvate-kinase-deficient red blood cells. Eur. J. Biochem. 149:101-111.
22. Ibarra, R. U., J. S. Edwards, and B. O. Palsson. 2002. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 420:186-189.
23. Jacobasch, G., and S. M. Rapoport. 1996. Hemolytic anemias due to erythrocyte enzyme deficiencies. Mol. Aspects Med. 17:143-170.
24. Jamshidi, N., J. S. Edwards, T. Fahland, G. M. Church, and B. O. Palsson. 2001. Dynamic simulation of the human red blood cell metabolic network. Bioinformatics. 17:286-287.
25. Jamshidi, N., S. J. Wiback, and B. O. Palsson. 2002. In silico model-driven assessment of the effects of single nucleolide polymorphisms (SNPs) on human red blood cell metabolism. Genome Res. 12:1687-1692.
26. Kauffman, K. J., J. D. Pajerowski, N. Jamshidi, B. O. Palsson, and J. S. Edwards. 2002. Description and analysis of metabolic connectivity and dynamics in the human red blood cell. Biophys. J. 83:646-662.
27. Kaufman, D. E., and R. L. Smith. 1998. Direction choice for accelerated convergence in hit-and-run sampling. Opt. Res. 46:84-95.
28. Lew, V. L., and R. M. Bookchin. 1986. Volume, pH, and ion-content regulation in human red cells: analysis of transient behavior with an integrated model. J. Membr. Biol. 92:57-74.
29. Lovasz, L. 1999. Hit-and-run mixes fast. Math. Program. 86:443-461.
30. Meyer, C. D. 2000. Matrix Analysis and Applied Linear Algebra. Society for Industrial and Applied Mathematics, Philadelphia, PA.
31. Mulquiney, P. J., W. A. Bubb, and P. W. Kuchel. 1999. Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: in vivo kinetic characterization of 2,3-bisphosphoglycerate synthase/phosphatase using 13C and 31P NMR. Biochem. J. 342:567-580.
32. Mulquiney, P. J., and P. W. Kuchel. 1999. Model of 2,3- bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: computer simulation and metabolic control analysis. Biochem. J. 342:597-604.
33. Mulquiney, P. J., and P. W. Kuchel. 2003. Modeling Metabolism with Mathematica: Detailed Examples including Erythrocyte Metabolism. CRC Press, Boca Raton, Fl.
34. Papin, J. A., N. D. Price, and B. O. Palsson. 2002. Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Res. 12:1889-1900.
35. Papin, J. A., N. D. Price, S. J. Wiback, D. A. Fell, and B. O. Palsson. 2003. Metabolic pathways in the post-genome era. Trends Biochem. Sci. 28:250-258.
36. Pfeiffer, T., I. Sanchez-Valdenebro, J. C. Nuno, F. Montero, and S. Schuster. 1999. METATOOL: for studying metabolic networks. Bioinformatics. 15:251-257.
37. Price, N. D., J. A. Papin, and B. O. Palsson. 2002. Determination of redundancy and systems properties of Helicobacter pylori's metabolic network using genome-scale extreme pathway analysis. Genome Res. 12:760-769.
38. Price, N. D., J. A. Papin, C. H. Schilling, and B. O. Palsson. 2003a. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21:162-169.
39. Price, N. D., J. L. Reed, J. A. Papin, S. J. Wiback, and B. O. Palsson. 2003b. Network-based analysis of metabolic regulation in the human red blood cell. J. Theor. Biol. 225:185-194.
40. Reed, J. L., and B. O. Palsson. 2003. Thirteen years of building constraint-based in silico models of Escherichia coli. J. Bacteriol. 185:2692-2699.
41. Reed, J. L., T. D. Vo, C. H. Schilling, and B. O. Palsson. 2003. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4:R54.1-R54.12.
42. Salvador, A., and M. A. Savageau. 2003. Quantitative evolutionary design of glucose 6-phosphate dehydrogenase expression in human erythrocytes. Proc. Natl. Acad. Sci. USA. 100:14463-14468.
43. Savinell, J. M., and B. O. Palsson. 1992a. Optimal selection of metabolic fluxes for in vivo measurement. I. Development of mathematical methods. J. Theor. Biol. 155:201-214.
44. Savinell, J. M., and B. O. Palsson. 1992b. Optimal selection of metabolic fluxes for in vivo measurement. II. Application to Escherichia coli and hybridoma cell metabolism. J. Theor. Biol. 155:215-242.
45. Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards, and B. O. Palsson. 2002. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184:4582-4593.
46. Schilling, C. H., and B. O. Palsson. 2000. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203:249-283.
47. Schilling, C. H., S. Schuster, B. O. Palsson, and R. Heinrich. 1999. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol. Prog. 15:296-303.
48. Schuster, R., and H. G. Holzhutter. 1995. Use of mathematical models for predicting the effect of large scale enzyme activity alterations: application to enzyme deficiencies of red blood cells. Eur. J. Biochem. 229:403-418.
49. Schuster, R., H. G. Holzhütter, and G. Jacobasch. 1988. Interrelations between glycolysis and the hexose monophosphate shunt in erythrocytes as studied on the basis of a mathematical model. Biosystems. 22:19-36.
50. Schuster, S., D. A. Fell, and T. Dandekar. 2000. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnol. 18:326-332.
51. Schuster, S., and C. Hilgetag. 1994. On elementary flux modes in biochemical reaction systems at steady state. J. Biol. Sys. 2:165-182.
52. Segre, D., D. Vitkup, and G. M. Church. 2002. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA. 99:15112-15117.
53. Tanaka, K. R., and C. R. Zerez. 1990. Red cell enzymopathies of the glycolytic pathway. Semin. Hematol. 27:165-185.
54. Thorburn, D. R., and P. W. Kuchel. 1985. Regulation of the human-erythrocyte hexose-monophosphate shunt under conditions of oxidative stress. A study using NMR spectroscopy, a kinetic isotope effect, a reconstituted system and computer simulation. Eur. J. Biochem. 150:371-386.
55. Wiback, S. J., I. Famili, H. J. Greenberg, and B. O. Palsson. 2004. Monte Carlo sampling can be used to determine the size and shape of the steady-state flux space. J. Theor. Biol. 228:437-447.
56. Wiback, S. J., and B. O. Palsson. 2002. Extreme pathway analysis of human red blood cell metabolism. Biophys. J. 83:808-818.
57. Zabinsky, Z. B., R. L. Smith, J. F. McDonald, H. E. Romeijn, and D. E. Kaufman. 1993. Improving hit-and-run for global optimization. J. Global Optimiz. 3:171-192.