Skip to main content
PMC home page
Biophysical Journal logoLink to Biophysical Journal
. 2002 Sep;83(3):1331–1340. doi: 10.1016/S0006-3495(02)73903-9

Dynamic flux balance analysis of diauxic growth in Escherichia coli.

Radhakrishnan Mahadevan 1, Jeremy S Edwards 1, Francis J Doyle 3rd 1
PMCID: PMC1302231  PMID: 12202358

Abstract

Flux Balance Analysis (FBA) has been used in the past to analyze microbial metabolic networks. Typically, FBA is used to study the metabolic flux at a particular steady state of the system. However, there are many situations where the reprogramming of the metabolic network is important. Therefore, the dynamics of these metabolic networks have to be studied. In this paper, we have extended FBA to account for dynamics and present two different formulations for dynamic FBA. These two approaches were used in the analysis of diauxic growth in Escherichia coli. Dynamic FBA was used to simulate the batch growth of E. coli on glucose, and the predictions were found to qualitatively match experimental data. The dynamic FBA formalism was also used to study the sensitivity to the objective function. It was found that an instantaneous objective function resulted in better predictions than a terminal-type objective function. The constraints that govern the growth at different phases in the batch culture were also identified. Therefore, dynamic FBA provides a framework for analyzing the transience of metabolism due to metabolic reprogramming and for obtaining insights for the design of metabolic networks.

Full Text

The Full Text of this article is available as a PDF (262.8 KB).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Cohen B. A., Mitra R. D., Hughes J. D., Church G. M. A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression. Nat Genet. 2000 Oct;26(2):183–186. doi: 10.1038/79896. [DOI] [PubMed] [Google Scholar]
  2. Edwards J. S., Ibarra R. U., Palsson B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001 Feb;19(2):125–130. doi: 10.1038/84379. [DOI] [PubMed] [Google Scholar]
  3. Giuseppin M. L., van Riel N. A. Metabolic modeling of Saccharomyces cerevisiae using the optimal control of homeostasis: a cybernetic model definition. Metab Eng. 2000 Jan;2(1):14–33. doi: 10.1006/mben.1999.0134. [DOI] [PubMed] [Google Scholar]
  4. Ideker T., Thorsson V., Ranish J. A., Christmas R., Buhler J., Eng J. K., Bumgarner R., Goodlett D. R., Aebersold R., Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001 May 4;292(5518):929–934. doi: 10.1126/science.292.5518.929. [DOI] [PubMed] [Google Scholar]
  5. Kirkpatrick C., Maurer L. M., Oyelakin N. E., Yoncheva Y. N., Maurer R., Slonczewski J. L. Acetate and formate stress: opposite responses in the proteome of Escherichia coli. J Bacteriol. 2001 Nov;183(21):6466–6477. doi: 10.1128/JB.183.21.6466-6477.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Lendenmann U., Egli T. Kinetic models for the growth of Escherichia coli with mixtures of sugars under carbon-limited conditions. Biotechnol Bioeng. 1998 Jul 5;59(1):99–107. doi: 10.1002/(sici)1097-0290(19980705)59:1<99::aid-bit13>3.0.co;2-y. [DOI] [PubMed] [Google Scholar]
  7. Oh M. K., Liao J. C. Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol Prog. 2000 Mar-Apr;16(2):278–286. doi: 10.1021/bp000002n. [DOI] [PubMed] [Google Scholar]
  8. Schilling C. H., Edwards J. S., Letscher D., Palsson B. Ø. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng. 2000;71(4):286–306. [PubMed] [Google Scholar]
  9. Schilling C. H., Letscher D., Palsson B. O. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol. 2000 Apr 7;203(3):229–248. doi: 10.1006/jtbi.2000.1073. [DOI] [PubMed] [Google Scholar]
  10. Selinger D. W., Cheung K. J., Mei R., Johansson E. M., Richmond C. S., Blattner F. R., Lockhart D. J., Church G. M. RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nat Biotechnol. 2000 Dec;18(12):1262–1268. doi: 10.1038/82367. [DOI] [PubMed] [Google Scholar]
  11. Stephanopoulos G. Metabolic fluxes and metabolic engineering. Metab Eng. 1999 Jan;1(1):1–11. doi: 10.1006/mben.1998.0101. [DOI] [PubMed] [Google Scholar]
  12. Tao H., Bausch C., Richmond C., Blattner F. R., Conway T. Functional genomics: expression analysis of Escherichia coli growing on minimal and rich media. J Bacteriol. 1999 Oct;181(20):6425–6440. doi: 10.1128/jb.181.20.6425-6440.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Tao H., Gonzalez R., Martinez A., Rodriguez M., Ingram L. O., Preston J. F., Shanmugam K. T. Engineering a homo-ethanol pathway in Escherichia coli: increased glycolytic flux and levels of expression of glycolytic genes during xylose fermentation. J Bacteriol. 2001 May;183(10):2979–2988. doi: 10.1128/JB.183.10.2979-2988.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Tavazoie S., Hughes J. D., Campbell M. J., Cho R. J., Church G. M. Systematic determination of genetic network architecture. Nat Genet. 1999 Jul;22(3):281–285. doi: 10.1038/10343. [DOI] [PubMed] [Google Scholar]
  15. Varma A., Boesch B. W., Palsson B. O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl Environ Microbiol. 1993 Aug;59(8):2465–2473. doi: 10.1128/aem.59.8.2465-2473.1993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Varma A., Palsson B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol. 1994 Oct;60(10):3724–3731. doi: 10.1128/aem.60.10.3724-3731.1994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Varner J. D. Large-scale prediction of phenotype: concept. Biotechnol Bioeng. 2000 Sep 20;69(6):664–678. doi: 10.1002/1097-0290(20000920)69:6<664::aid-bit11>3.0.co;2-h. [DOI] [PubMed] [Google Scholar]
  18. Varner J, Ramkrishna D. Metabolic engineering from a cybernetic perspective. 1. Theoretical preliminaries . Biotechnol Prog. 1999 May;15(3):407–425. doi: 10.1021/bp990017p. [DOI] [PubMed] [Google Scholar]
  19. Wei Y., Lee J. M., Richmond C., Blattner F. R., Rafalski J. A., LaRossa R. A. High-density microarray-mediated gene expression profiling of Escherichia coli. J Bacteriol. 2001 Jan;183(2):545–556. doi: 10.1128/JB.183.2.545-556.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Wong P., Gladney S., Keasling J. D. Mathematical model of the lac operon: inducer exclusion, catabolite repression, and diauxic growth on glucose and lactose. Biotechnol Prog. 1997 Mar-Apr;13(2):132–143. doi: 10.1021/bp970003o. [DOI] [PubMed] [Google Scholar]
  21. de Saizieu A., Certa U., Warrington J., Gray C., Keck W., Mous J. Bacterial transcript imaging by hybridization of total RNA to oligonucleotide arrays. Nat Biotechnol. 1998 Jan;16(1):45–48. doi: 10.1038/nbt0198-45. [DOI] [PubMed] [Google Scholar]

Articles from Biophysical Journal are provided here courtesy of The Biophysical Society

RESOURCES