Advertisement

Using Flux Balance Analysis to Guide Microbial Metabolic Engineering

  • Kathleen A. Curran
  • Nathan C. Crook
  • Hal S. AlperEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 834)

Abstract

Metabolic engineers modify biological systems through the use of modern molecular biology tools in order to obtain desired phenotypes. However, due to the extreme complexity and interconnectedness of metabolism in all organisms, it is often difficult to a priori predict which changes will yield the optimal results. Flux balance analysis (FBA) is a mathematical approach that uses a genomic-scale metabolic network models to afford in silico prediction and optimization of metabolic changes. In particular, a genome-scale approach can help select gene targets for knockout and overexpression. This approach can be used to help expedite the strain engineering process. Here, we give an introduction to the use of FBA and provide details for its implementation in a microbial metabolic engineering context.

Key words

Flux balance analysis MOMA Genomic-scale metabolic model Metabolic engineering Strain engineering 

References

  1. 1.
    Savinell, J. M., and Palsson, B. O. (1992) Optimal Selection of Metabolic Fluxes for in vivo Measurement .1. Development of Mathematical Methods, J. Theor. Biol. 155, 201–214.PubMedCrossRefGoogle Scholar
  2. 2.
    Savinell, J. M., and Palsson, B. O. (1992) Optimal Selection of Metabolic Fluxes for in vivo Measurement .2. Application to Escherichia coli and Hybrdoma Cell Metabolism, J. Theor. Biol. 155, 215–242.PubMedCrossRefGoogle Scholar
  3. 3.
    Varma, A., and Palsson, B. O. (1994) Stoichiometric Flux Balance Models Quantitatively Predict Growth and Metabolic By-Product Secretion in Wild-Type Escherichia coli W3110, Appl. Environ. Microbiol. 60, 3724–3731.PubMedGoogle Scholar
  4. 4.
    Varma, A., and Palsson, B. O. (1993) Metabolic Capabilities of Escherichia coli .1. Synthesis of Biosynthetic Precursors and Cofactors, J. Theor. Biol. 165, 477–502.PubMedCrossRefGoogle Scholar
  5. 5.
    Varma, A., and Palsson, B. O. (1993) Metabolic Capabilities of Escherichia coli .2. Optimal Growth Patterns, J. Theor. Biol. 165, 503–522.CrossRefGoogle Scholar
  6. 6.
    Edwards, J. S., and Palsson, B. O. (1999) Systems properties of the Haemophilus influenzae Rd metabolic genotype, J. Biol. Chem. 274, 17410–17416.PubMedCrossRefGoogle Scholar
  7. 7.
    Oberhardt, M. A., Palsson, B. O., and Papin, J. A. (2009) Applications of genome-scale metabolic reconstructions, Mol. Syst. Biol. 5.Google Scholar
  8. 8.
    Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., and Stevens, R. L. (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models, Nat. Biotechnol. 28, 977–U922.PubMedCrossRefGoogle Scholar
  9. 9.
    Alper, H., Jin, Y. S., Moxley, J. F., and Stephanopoulos, G. (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli, Metab. Eng. 7, 155–164.PubMedCrossRefGoogle Scholar
  10. 10.
    Lee, K. H., Park, J. H., Kim, T. Y., Kim, H. U., and Lee, S. Y. (2007) Systems metabolic engineering of Escherichia coli for L-threonine production, Mol. Syst. Biol. 3.Google Scholar
  11. 11.
    Park, J. H., Lee, K. H., Kim, T. Y., and Lee, S. Y. (2007) 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. USA 104, 7797–7802.PubMedCrossRefGoogle Scholar
  12. 12.
    Bro, C., Regenberg, B., Forster, J., and Nielsen, J. (2006) In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production, Metab. Eng. 8, 102–111.PubMedCrossRefGoogle Scholar
  13. 13.
    Song, H., Kim, T. Y., Choi, B. K., Choi, S. J., Nielsen, L. K., Chang, H. N., and Lee, S. Y. (2008) Development of chemically defined medium for Mannheimia succiniciproducens based on its genome sequence, Appl. Microbiol. Biotechnol. 79, 263–272.PubMedCrossRefGoogle Scholar
  14. 14.
    Herrgard, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novere, N., Li, P., Liebermeister, W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasic, I., Weichart, D., Brent, R., Broomhead, D. S., Westerhoff, H. V., Kirdar, B., Penttila, M., Klipp, E., Palsson, B. O., Sauer, U., Oliver, S. G., Mendes, P., Nielsen, J., and Kell, D. B. (2008) A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology, Nat. Biotechnol. 26, 1155–1160.PubMedCrossRefGoogle Scholar
  15. 15.
    Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., Cuellar, A. A., Dronov, S., Gilles, E. D., Ginkel, M., Gor, V., Goryanin, II, Hedley, W. J., Hodgman, T. C., Hofmeyr, J. H., Hunter, P. J., Juty, N. S., Kasberger, J. L., Kremling, A., Kummer, U., Le Novere, N., Loew, L. M., Lucio, D., Mendes, P., Minch, E., Mjolsness, E. D., Nakayama, Y., Nelson, M. R., Nielsen, P. F., Sakurada, T., Schaff, J. C., Shapiro, B. E., Shimizu, T. S., Spence, H. D., Stelling, J., Takahashi, K., Tomita, M., Wagner, J., Wang, J., and Forum, S. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models, Bioinformatics 19, 524–531.PubMedCrossRefGoogle Scholar
  16. 16.
    Reed, J. L., Vo, T. D., Schilling, C. H., and Palsson, B. O. (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR), Genome Biol. 4.Google Scholar
  17. 17.
    Kim, T. Y., and Lee, S. Y. (2006) Accurate metabolic flux analysis through data reconciliation of isotope balance-based data, J. Microbiol. Biotechnol. 16, 1139–1143.Google Scholar
  18. 18.
    Covert, M. W., Schilling, C. H., and Palsson, B. (2001) Regulation of gene expression in flux balance models of metabolism, J. Theor. Biol. 213, 73–88.PubMedCrossRefGoogle Scholar
  19. 19.
    Chandrasekaran, S., and Price, N. D. (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis, Proceedings of the National Academy of Sciences 107, 17845–17850.CrossRefGoogle Scholar
  20. 20.
    Segre, D., Vitkup, D., and Church, G. M. (2002) Analysis of optimality in natural and perturbed metabolic networks, Proc. Natl. Acad. Sci. USA. 99, 15112–15117.PubMedCrossRefGoogle Scholar
  21. 21.
    Vaderbei, R. J. (2001) Linear Programming: Foundations and Extensions, 2 ed., Kluwer Academic Publishers, Boston.Google Scholar
  22. 22.
    Edwards, J. S., Ibarra, R. U., and Palsson, B. O. (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data, Nat. Biotechnol. 19, 125–130.PubMedCrossRefGoogle Scholar
  23. 23.
    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. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, Mol. Syst. Biol. 3.Google Scholar
  24. 24.
    Burgard, A. P., Pharkya, P., and Maranas, C. D. (2003) OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization, Biotechnol. Bioeng. 84, 647–657.PubMedCrossRefGoogle Scholar
  25. 25.
    Patil, K. R., Rocha, I., Forster, J., and Nielsen, J. (2005) Evolutionary programming as a platform for in silico metabolic engineering, BMC Bioinformatics 6.Google Scholar
  26. 26.
    Pharkya, P., Burgard, A. P., and Maranas, C. D. (2004) OptStrain: A computational framework for redesign of microbial production systems, Genome Res. 14, 2367–2376.PubMedCrossRefGoogle Scholar
  27. 27.
    Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. O., and Herrgard, M. J. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, Nat. Protoc. 2, 727–738.PubMedCrossRefGoogle Scholar
  28. 28.
    Orth, J. D., Thiele, I., and Palsson, B. O. (2010) What is flux balance analysis?, Nat. Biotechnol. 28, 245–248.PubMedCrossRefGoogle Scholar
  29. 29.
    Rocha, I., Maia, P., Evangelista, P., Vilaca, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., and Rocha, M. (2010) OptFlux: an open-source software platform for in silico metabolic engineering, BMC Syst. Biol. 4.Google Scholar
  30. 30.
    Klamt, S., Saez-Rodriguez, J., and Gilles, E. D. (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer, BMC Syst. Biol. 1.Google Scholar
  31. 31.
    Wright, J., and Wagner, A. (2008) The Systems Biology Research Tool: evolvable open-source software, BMC Syst. Biol. 2.Google Scholar
  32. 32.
    Ibarra, R. U., Edwards, J. S., and Palsson, B. O. (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth, Nature 420, 186–189.PubMedCrossRefGoogle Scholar
  33. 33.
    Shastri, A. A., and Morgan, J. A. (2007) A transient isotopic labeling methodology for C-13 metabolic flux analysis of photo auto trophic microorganisms, Phytochemistry 68, 2302–2312.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Kathleen A. Curran
    • 1
  • Nathan C. Crook
    • 1
  • Hal S. Alper
    • 1
    Email author
  1. 1.Department of Chemical EngineeringThe University of Texas at AustinAustinUSA

Personalised recommendations