Advertisement

Applied Biochemistry and Biotechnology

, Volume 179, Issue 8, pp 1418–1434 | Cite as

Robust Parameter Identification to Perform the Modeling of pta and poxB Genes Deletion Effect on Escherichia Coli

  • V. Guerrero-Torres
  • M. Rios-Lozano
  • J. A. Badillo-Corona
  • I. ChairezEmail author
  • C. Garibay-Orijel
Article
  • 174 Downloads

Abstract

The aim of this study was to design a robust parameter identification algorithm to characterize the effect of gene deletion on Escherichia coli (E. coli) MG1655. Two genes (pta and poxB) in the competitive pathways were deleted from this microorganism to inhibit pyruvate consumption. This condition deviated the E. coli metabolism toward the Krebs cycle. As a consequence, the biomass, substrate (glucose), lactic, and acetate acids as well as ethanol concentrations were modified. A hybrid model was proposed to consider the effect of gene deletion on the metabolism of E. coli. The model parameters were estimated by the application of a least mean square method based on the instrument variable technique. To evaluate the parametric identifier method, a set of robust exact differentiators, based on the super-twisting algorithm, was implemented. The hybrid model was successfully characterized by the parameters obtained from experimental information of E. coli MG1655. The significant difference between parameters obtained with wild-type strain and the modified (with deleted genes) justifies the application of the parametric identification algorithm. This characterization can be used to optimize the production of different byproducts of commercial interest.

Keywords

E. coli Gene deletion Hybrid model Robust exact differentiator Parametric modeling 

References

  1. 1.
    Porro, D., Gasser, B., Fossati, T., Maurer, M., Branduardi, P., Sauer, M., & Mattanovich, D. (2011). Production of recombinant proteins and metabolites in yeasts: when are these systems better than bacterial production systems?. Applied Microbiology and Biotechnology, 89(4), 939–948.CrossRefGoogle Scholar
  2. 2.
    De Mey, M., De Maeseneire, S., Soetaert, W., & Vandamme, E. (2007). Minimizing acetate formation in e. coli fermentations. Journal of Industrial Microbiology & Biotechnology, 34(11), 689–700.CrossRefGoogle Scholar
  3. 3.
    Eiteman, M.A., & Altman, E. (2006). Overcoming acetate in escherichia coli recombinant protein fermentations. Trends in Biotechnology, 24(11), 530–536.CrossRefGoogle Scholar
  4. 4.
    J. Heyland, L., & Blank, S.A. (2011). Quantification of metabolic limitations during recombinant protein production in Escherichia coli. Journal of Biotechnology, 155(2), 178–184.CrossRefGoogle Scholar
  5. 5.
    Kromer, J.O., Wittmann, C., Schroder, H., & Heinzle, E. (2006). Metabolic pathway analysis for rational design of l-methionine production by Escherichia coli and Corynebacterium glutamicum. Metabolic Engineering, 8(4), 353–369.CrossRefGoogle Scholar
  6. 6.
    Sanchez, A.M., Bennet, G.N., & San, K.Y. (2005). Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metabolic Engineering, 7(3), 229–239.CrossRefGoogle Scholar
  7. 7.
    Holms, H. (1996). Flux analysis and control of the central metabolic pathways in Escherichia coli. FEMS Microbiology Reviews, 19, 85–116.CrossRefGoogle Scholar
  8. 8.
    Foster, A.H., & Gescher, J. (2014). Metabolic engineering of escherichia coli for production of mixed-acid fermentation end products. Frontiers in Bioengineering and Biotechnology, 2, 1–12.CrossRefGoogle Scholar
  9. 9.
    Sanchez, A.M., Bennet, G.N., & San, K.Y. (2006). Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. Metabolic Engineering, 8(3), 209–226.CrossRefGoogle Scholar
  10. 10.
    De Mey, M., Lequeux, G.J., Beauprex, J.J., Maertens, J., Van Horen, E., Soetaert, W.K., Vanrolleghem, P.A., & Vandamme, E.J. (2007). Comparison of different strategies to reduce acetate formation in escherichia coli. Biotechnology Progress, 23, 1053–1063.Google Scholar
  11. 11.
    Contiero, J., Beatty, C., Kumari, S., DeSanti, C.L., Strohl, W.R., & Wolfe, A. (2000). Effects of mutations in acetate metabolism on high-cell-density growth of Escherichia coli. Journal of Industrial Microbiology and Biotechnology, 24 (6), 421–430.CrossRefGoogle Scholar
  12. 12.
    Catano-Cerezo, S., Pastor, J.M., Renilla, S., Bernal, V., Iborra, J.L., & Canovas, M. (2009). An insight into the role of phosphotransacetylase (pta) and the acetate/acetyl-coa node in Escherichia coli. Microbial Cell Factories, 8(54), 1–19.Google Scholar
  13. 13.
    Wolfe, A.J. (2005). The acetate switch. Microbiology and Molecular Biology Reviews, 69(1), 12–50.CrossRefGoogle Scholar
  14. 14.
    Cozzone, A.J., & El-Mansi, M. (2005). Control of isocitrate dehydrogenase catalytic activity by protein phosphorylation in Escherichia coli. Journal of Molecular Microbiology and Biotechnology, 9(3-4), 132–146.CrossRefGoogle Scholar
  15. 15.
    Moon, S.Y., Hong, S.H., Kim, T.Y., & Lee, S.Y. (2008). Metabolic engineering of Escherichia coli for the production of Malic acid. Biochemical Engineering Journal, 40(2), 312–320.CrossRefGoogle Scholar
  16. 16.
    Li, Z., Zhi-Riu, Z., Xiang-Zhong, C., Dan-Dan, N., Kang-Ming, T., Bernanrd, A.P., Wei, S., Gui-Yang, S., Suren, S., & Zheng-Xiang, W. (2010). Evaluation of genetic manipulation strategies on d-lactate production by Escherichia coli. Current Microbiollogy, 62(3), 981–989.Google Scholar
  17. 17.
    Atsumi, S., Cann, A.F., Connor, M.R., Shen, C.R., Smith, K.M., Brynildsen, M.P., Chou, K.J.Y., Hanai, T., & Liao, J.C. (2008). Metabolic engineeringof Escherichia coli for 1-butanolproduction. Metabolic Engineering, 10, 305–311.CrossRefGoogle Scholar
  18. 18.
    Dittrich, C.R., Vadali, R.V., Bennet, G.N., & San, K.Y. (2005). Redistribution of metabolic fluxes in the central aerobic metabolic pathway of E. coli mutant strains with deletion of the acka-pta and poxb pathways for the synthesis of isoamyl acetate. Biotechnology Progress, 21, 627–631.CrossRefGoogle Scholar
  19. 19.
    Abdul Kadir, T.A., Mannan, A.A., Kierzek, A.M., McFadden, J., & Shimizu, K. (2010). Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification. Microbial Cell Factories, 9(88), 1–21.Google Scholar
  20. 20.
    Bernard, O., & Queinnec, I. (2008). Dynamic models of biochemical processes: properties of the models. Paris: Hermes Science.Google Scholar
  21. 21.
    Faugeras, B., Bernard, O., Sciandra, A., & Levy, M. (2004). A mechanistic modelling and data assimilation approach to estimate the carbon/chlorophyll and carbon/nitrogen ratios in a coupled hydrodynamical-biological model. Nonlinear Processes in Geophysics, 11, 515–533.CrossRefGoogle Scholar
  22. 22.
    Muoz-Palacios, F., & Ben-Youssef, C. (2006). Biomass and phenol estimation using dissolved oxygen measurement, Proceedings of the Electronics. Robotics and Automotive Mechanics, 2, 206–214.CrossRefGoogle Scholar
  23. 23.
    Ross, O.N., & Geider, R.J. (2009). New cell-based model of photosynthesis and photo-acclimation: accumulation and mobilisation of energy reserves in phytoplankton. Marine Ecology Progress Series, 383, 53–71.CrossRefGoogle Scholar
  24. 24.
    Pahlow, M., Dietze, H., & Oschlies, A. (2013). OptiMality-based model of phytoplankton growth and diazotrophy. Marine Ecology Progress Series, 489, 1–16.CrossRefGoogle Scholar
  25. 25.
    Levant, A. (2002). Sliding mode control in engineering. Marcel Dekker: Ch. High Order Sliding Modes.Google Scholar
  26. 26.
    Levant, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control, 58(6), 1247–1263.CrossRefGoogle Scholar
  27. 27.
    Fridman, L., & Levant, A. Sliding Mode in Control in Engineering, no. 3, Marcel Dekker, 2002, Ch. High Order Sliding Modes, pp. 53–101.Google Scholar
  28. 28.
    Picard, R., & Cook, D. (1984). Cross-validation of regression models. Journal of the American Statistical Society, 79(387), 575–583.CrossRefGoogle Scholar
  29. 29.
    Datsenko, K.A., & Wanner, B.L. (2000). One-step inactivation of chromosomal genes in Escherichia coli k-12 using pcr products. Proceedings of the National Academy of Sciences, 97(12), 6640–6645.CrossRefGoogle Scholar
  30. 30.
    Ljung, L. (1999). System Identification: Theory for the User (2nd Edition), 2nd edn.: Prentice Hall.Google Scholar
  31. 31.
    Moreno, J.A., & Osorio, M. (2012). Strict lyapunov functions for the super-twisting algorithm. IEEE Transactions on Automatic Control, 57(4), 1035–1040.CrossRefGoogle Scholar
  32. 32.
    Cruz-Zavala, E., Moreno, J., & Fridman, L. (2011). Uniform robust exact differentiator. IEEE Transactions on Automatic Control, 56, 11.CrossRefGoogle Scholar
  33. 33.
    Salgado, I., Moreno, A., & Chairez, I. (2010). Sampled output based continuous second-order sliding mode observer. In Workshop on variable structure systems.Google Scholar
  34. 34.
    Han, K., Fridman, E., & Spurgeon, S.K. Sampled-data sliding mode observer for robust fault reconstruction: A time-delay approach, Journal of the Franklin Institute doi: 10.1016/j.jfranklin.2013.04.004.
  35. 35.
    Levant, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control, 58(6), 1247–1263.CrossRefGoogle Scholar
  36. 36.
    Levant, A. (2007). Finite differences in homogeneous discontinuous control. IEEE Transactions on Automatic Control, 52(7), 1208–1217.CrossRefGoogle Scholar
  37. 37.
    Xu, B., Jahic, M., Blomsten, G., & Enfors, S.O. (1999). Glucose overflow metabolism and mixed-acid fermentation in aerobic large-scale fed-batch processes with Escherichia coli. Applied Microbiology and Biotechnology, 51(5), 564–571.CrossRefGoogle Scholar
  38. 38.
    Clarck, D.P., & Cronan, J.E. (1980). Acetaldehyde coenzyme a dehydrogenase of Escherichia coli. Journal of Bacteriology, 144(1), 179–184.Google Scholar
  39. 39.
    Membrillo-Hernn̈dez, J., Echave, P., Cabiscol, E., Tamarit, J., Ros, J., & Lin, E.C. (2000). Evolution of the adhe gene product of Escherichia coli from a functional reductase to a dehydrogenase, genetic and biochemical studies of the mutant proteins. Journal of Biological Chemistry, 275(43), 33869–33875.CrossRefGoogle Scholar
  40. 40.
    Bodel, C., Bortolussi, L., Chiarugi, D., Guerrero, M.L., Policriti, A., & Romanel, A. (2015). On the impact of discreteness and abstractions on modelling noise in gene regulatory networks. Computational Biology and Chemistry, 56, 98–108.CrossRefGoogle Scholar
  41. 41.
    Rudnicki, R., & Tomski, A. (2015). On a stochastic gene expression with pre-mrna, mrna and protein contribution. Journal of Theoretical Biology, 387, 54–67. doi: 10.1016/j.jtbi.2015.09.012.CrossRefGoogle Scholar
  42. 42.
    Gomez-Vela, F., Lagares, J.A., & Diaz-Diaz, N. (2015). Gene network coherence based on prior knowledge using direct and indirect relationships. Computational Biology and Chemistry, 56, 142 – 151. doi: 10.1016/j.compbiolchem.2015.03.002.CrossRefGoogle Scholar
  43. 43.
    Costa, R.S., Hartmann, A., & Vinga, S. (2016). Kinetic modeling of cell metabolism for microbial production. Journal of Biotechnology, 219, 126 – 141. doi: 10.1016/j.jbiotec.2015.12.023.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • V. Guerrero-Torres
    • 1
  • M. Rios-Lozano
    • 1
  • J. A. Badillo-Corona
    • 1
  • I. Chairez
    • 2
    Email author
  • C. Garibay-Orijel
    • 2
  1. 1.SEPI-UPIBIInstituto Politécnico NacionalMexico CityMexico
  2. 2.Department of Bioprocesses-UPIBIInstituto Politécnico NacionalMexico CityMexico

Personalised recommendations