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In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

Nature Biotechnology volume 19, pages 125–130 (2001)Cite this article

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Abstract

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype–phenotype relationship for metabolism in bacterial cells.

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Figure 1: From genome sequence to metabolic characteristics.
Figure 2: In silico predictions of growth and metabolic functions and comparisons to experimental data.
Figure 3: Line of optimality (LO) projected onto each pair of basis vectors.
Figure 4: In silico predictions of growth and metabolic functions and comparisons to experimental data.

Change history

  • 21 March 2003

    Supplementary Table 1 HTML format was replaced with an Excel version

Notes

  1. * Note:The original Supplementary Table 1 (HTML format) has been replaced with an Excel file.

References

  1. TIGR Microbial database: a listing of published microbial genomes and chromosomes and those in progress. (The Institute for Genomic Research, Rockville, MD, 2000). http://www.tigr.org/tdb/mdb/mdbcomplete.html

  2. Karp, P.D. et al. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 28, 56–59 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Selkov, E. Jr.,, Grechkin, Y., Mikhailova, N. & Selkov, E. MPW: the Metabolic Pathways Database. Nucleic Acids Res. 26, 43–45 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Karp. P.D., Ouzounis, C. & Paley, S. HinCyc: a knowledge base of the complete genome and metabolic pathways of H. influenzae. Proceedings of the ISMB-96 Conference 4, 116–124 (1996).

    CAS  Google Scholar 

  6. Overbeek, R. et al. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res. 28, 123–125 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Edwards, J.S. & Palsson, B.O. Systems Properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274, 17410–17416 (1999).

    Article  CAS  PubMed  Google Scholar 

  8. Edwards, J.S. & Palsson, B.O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. USA 97, 5528–5533 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Karp, P.D., Krummenacker, M., Paley, S. & Wagg, J. Integrated pathway-genome databases and their role in drug discovery. Trends Biotechnol. 17, 275–281 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. Goryanin, I., Hodgman, T.C. & Selkov, E. Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics 15, 749–758 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Tomita, M. et al. E-CELL: software environment for whole-cell simulation. Bioinformatics 15, 72–84 (1999).

    Article  CAS  PubMed  Google Scholar 

  12. Bailey, J.E. Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnol. Prog. 14, 8–20 (1998).

    Article  CAS  PubMed  Google Scholar 

  13. Fell, D. Understanding the control of metabolism. (Portland Press, London; 1996).

    Google Scholar 

  14. Domach, M.M., Leung, S.K., Cohn, R.E. & Shuler, M.M. Computer model for glucose-limited growth of a single cell of Escherichia coli B/r-A. Biotechnol. Bioeng. 26, 203–216 (1984).

    Article  CAS  PubMed  Google Scholar 

  15. Palsson, B.O. & Lee, I.D. Model complexity has a significant effect on the numerical value and interpretation of metabolic sensitivity coefficients. J. Theor. Biol. 161, 299–315 (1993).

    Article  CAS  PubMed  Google Scholar 

  16. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997).

    Article  CAS  PubMed  Google Scholar 

  17. Liao, J.C. Modelling and analysis of metabolic pathways. Curr. Opin. Biotechnol. 4, 211–216 (1993).

    Article  CAS  PubMed  Google Scholar 

  18. Novak, B. et al. Finishing the cell cycle. J. Theor. Biol. 199, 223–233 (1999).

    Article  CAS  PubMed  Google Scholar 

  19. Kompala, D.S., Ramkrishna, D., Jansen, N.B. & Tsao, G.T. Investigation of bacterial growth on mixed substrates. Experimental evaluation of cybernetic models. Biotechnol. Bioeng. 28, 1044–1056 (1986).

    Article  CAS  PubMed  Google Scholar 

  20. McAdams, H.H. & Arkin, A. It's a noisy business! Genetic regulation at the nanomolar scale. Trends Genet. 15, 65–69 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Palsson, B.O., Joshi, A. & Ozturk, S.S. Reducing complexity in metabolic networks: making metabolic meshes manageable. Fed. Proc. 46, 2485–2489 (1987).

    CAS  PubMed  Google Scholar 

  22. Jamashidi, N., Edwards, J., Fahland, T., Church, G. & Palsson, B. A computer model of human red blood cell metabolism. Bioinformatics, in press (2001).

  23. Lee, I.D. & Palsson, B.O. A Macintosh software package for simulation of human red blood cell metabolism. Comput. Methods Programs Biomed. 38, 195–226 (1992).

    Article  CAS  PubMed  Google Scholar 

  24. Mulquiney, P.J., Bubb, W.A. & Kuchel, P.W. 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 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fell, D.A. & Small, J.A. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. J. Biochem. 238, 781–786 (1986).

    Article  CAS  Google Scholar 

  26. Edwards, J.S., Ramakrishna, R., Schilling, C.H. & Palsson, B.O. In Metabolic engineering. (eds Lee, S.Y. & Papoutsakis, E.T.) 13–57 (Marcel Dekker, New York, NY; 1999).

    Google Scholar 

  27. Pramanik, J. & Keasling, J.D. Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng. 56, 398–421 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Sauer, U., Cameron, D.C. & Bailey, J.E. Metabolic capacity of Bacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid. Biotechnol. Bioeng. 59, 227–238 (1998).

    Article  CAS  PubMed  Google Scholar 

  29. Bonarius, H.P.J., Schmid, G. & Tramper, J. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotechnol. 15, 308–314 (1997).

    Article  CAS  Google Scholar 

  30. Varma, A. & Palsson, B.O. Metabolic flux balancing: basic concepts, scientific and practical use. Bio/Technology 12, 994–998 (1994).

    Article  CAS  Google Scholar 

  31. Edwards, J.S. Functional genomics and the computational analysis of bacterial metabolism (PhD Thesis). (Department of Bioengineering, University of California San Diego, La Jolla, CA; 1999).

  32. Chvatal, V. Linear programming. (W.H. Freeman, New York, NY; 1983).

    Google Scholar 

  33. Varma, A. & Palsson, B.O. Metabolic capabilities of Escherichia coli: II. Optimal growth patterns. J. Theor. Biol. 165, 503–522 (1993).

    Article  CAS  Google Scholar 

  34. Schilling, C.H. & Palsson, B.O. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203, 249–83 (2000).

    Article  CAS  PubMed  Google Scholar 

  35. Schilling, C.H., Schuster, S., Palsson, B.O. & Heinrich, R. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol. Prog. 15, 296–303 (1999).

    Article  CAS  PubMed  Google Scholar 

  36. Schilling, C.H., Edwards, J.S., Letscher, D. & Palsson, B.O. Pathway analysis and flux balance analysis: a comprehensive study of metabolic systems. Biotechnol. Bioeng. in press (2001).

  37. Sauer, U. et al. Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J. Bacteriol. 181, 6679–6688 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Klapa, M.I., Park, S.M., Sinskey, A.J. & Stephanopoulos, G. Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory. Biotechnol. Bioeng. 62, 375–391 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. DeRisi, J.L., Iyer, V.R. & Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997).

    Article  CAS  PubMed  Google Scholar 

  40. Richmond, C.S., Glasner, J.D., Mau, R., Jin, H. & Blattner, F.R. Genome-wide expression profiling in Escherichia coli K-12. Nucleic Acids Res. 27, 3821–3835 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hacia, J.G., Brody, L.C., Chee, M.S., Fodor, S.P.A. & Collins, F.S. Detection of heterozygous mutations in BRCA1 using high-density oligonucleotide arrays and two-colour fluorescence analysis. Nat. Genet. 14, 441–447 (1996).

    Article  CAS  PubMed  Google Scholar 

  42. Link, A.J., Robison, K. & Church, G.M. Comparing the predicted and observed properties of proteins encoded in the genome of Escherichia coli K-12. Electrophoresis 18, 1259–1313 (1997).

    Article  CAS  PubMed  Google Scholar 

  43. Vanbogelen, R.A., Abshire, K.Z., Moldover, B., Olson, E.R. & Neidhardt, F.C. Escherichia coli proteome analysis using the gene-protein database. Electrophoresis 18, 1243–1251 (1997).

    Article  CAS  PubMed  Google Scholar 

  44. Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999).

    Article  CAS  PubMed  Google Scholar 

  45. Schilling, C.H., Edwards, J.S. & Palsson, B.O. Towards metabolic phenomics: analysis of genomic data using flux balances. Biotechnol. Prog. 15, 288–295 (1999).

    Article  CAS  PubMed  Google Scholar 

  46. Neidhardt, F.C. (ed.) Escherichia coli and Salmonella: cellular and molecular biology. (ASM Press, Washington, DC; 1996).

    Google Scholar 

  47. Maniatis, T., Fritsch, E.F. & Sambrook, J. Molecular cloning: a laboratory manual. (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY; 1982).

    Google Scholar 

  48. Neidhardt, F.C. & Umbarger, H.E. In Escherichia coli and Salmonella: cellular and molecular biology. (ed. Neidhardt, F.C.) 13–16 (ASM Press, Washington, DC; 1996).

    Google Scholar 

  49. Strang, G. Linear algebra and its applications. (Saunders, Fort Worth, TX; 1988).

    Google Scholar 

  50. Varma, A., Boesch, B.W. & Palsson, B.O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 59, 2465–2473 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Covert, M.W. et al. Metabolic modeling of microbial strains in silico. Trends Biochem. Sci. in press (2001).

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Acknowledgements

This work was funded by the NIH (GM57089) and the NSF (MCB 98-73384 and BES 98-14092). We would like to thank Christophe Schilling and George M. Church for insightful discussions during the preparation of this manuscript, and Markus Covert and Iman Famili for technical assistance.

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  1. Jeremy S. Edwards

    Present address: Department of Chemical Engineering, University of Delaware, Newark, DE, 19716

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  1. Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, CA

    Jeremy S. Edwards, Rafael U. Ibarra & Bernhard O. Palsson

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Correspondence to Bernhard O. Palsson.

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Edwards, J., Ibarra, R. & Palsson, B. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19, 125–130 (2001). https://doi.org/10.1038/84379

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