Skip to main content
Log in

Determining the transport mechanism of an enzyme-catalytic complex metabolic network based on biological robustness

  • Original Paper
  • Published:
Bioprocess and Biosystems Engineering Aims and scope Submit manuscript

Abstract

Understanding the transport mechanism of 1,3-propanediol (1,3-PD) is of critical importance to do further research on gene regulation. Due to the lack of intracellular information, on the basis of enzyme-catalytic system, using biological robustness as performance index, we present a system identification model to infer the most possible transport mechanism of 1,3-PD, in which the performance index consists of the relative error of the extracellular substance concentrations and biological robustness of the intracellular substance concentrations. We will not use a Boolean framework but prefer a model description based on ordinary differential equations. Among other advantages, this also facilitates the robustness analysis, which is the main goal of this paper. An algorithm is constructed to seek the solution of the identification model. Numerical results show that the most possible transport way is active transport coupled with passive diffusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bibel H, Memzel K, Zeng AP, Deckwer WD (1999) Microbial production of 1,3-propanediol. Appl Microbiol Biotechnol 52:289–297

    Article  Google Scholar 

  2. Xiu ZL (2000) Research progress on the production of 1,3-propanediol by fermentation. Microbiology 27:300–302

    CAS  Google Scholar 

  3. Xu GX (2010) Robust control of continuous bioprocesses. Math Probl Eng 2010:Article ID 627035

  4. Xu GX (2012) Bi-objective optimization of biochemical systems by linear programming. Appl Math Comput 218:7562–7572

    Article  Google Scholar 

  5. Tian Y, Sun KB, Kasperski A, Chen LS (2010) Studies on the dynamics of a continuous bioprocess with impulsive state feedback control. Chem Eng J 157:558–567

    Article  CAS  Google Scholar 

  6. Liu CY, Gong ZH, Feng EM, Yin HC (2009) Modelling and optimal control for nonlinear multistage dynamical system of microbial fed-batch culture. J Ind Manag Optim 5(4):835–850

    Article  Google Scholar 

  7. Gao CX, Feng EM, Wang ZT, Xiu ZL (2005) Nonlinear dynamical systems of bio-dissimilation of glycerol to 1,3-propanediol and their optimal controls. J Ind Manag Optim 1:377–388

    Article  Google Scholar 

  8. Wang G, Feng EM, Xiu ZL (2008) Modeling and parameter identification of microbial bioconversion in fed-batch cultures. J Process Control 18(5):458–464

    Article  Google Scholar 

  9. Wang L, Xiu ZL, Gong ZH, Feng EM (2012) Modeling and parameter identification for multistage simulation of microbial bioconversion in batch culture. Int J Biomath. doi:10.1142/S179352451100174X

    Google Scholar 

  10. Wang HY, Feng EM, Xiu ZL (2008) Optimality condition of the nonlinear impulsive system in fed-batch fermentation. Nonlinear Anal Theory Methods Appl 68(1):12–23

    Article  Google Scholar 

  11. Li XH, Guo JJ, Feng EM, Xiu ZL (2010) Discrete optimal control model and bound error for microbial continuous fermentation. Nonlinear Anal Real World Appl 11(1):131–138

    Article  Google Scholar 

  12. Wang L, Xiu ZL, Zhang YD, Feng EM (2011) Optimal control for multistage nonlinear dynamic system of microbial bioconversion in batch culture, J Appl Math 2011:Article ID 624516

  13. Wang L (2012) Modelling and regularity of nonlinear impulsive switching dynamical system in fed-batch culture, abstract and applied analysis 2012:Article ID 295627

  14. Zhang QR, Teng H, Wu ZN, Xiu ZL (2008) Process dynamic modeling based on metabolic network of glycerol bioconversion to 1,3-propanediol. J Biotechnol 136S:S22–S71

    Google Scholar 

  15. Tian Y, Sun KB, Kasperski A, Chen LS (2010) Nonlinear modelling and qualitative analysis of a real chemostat with pulse feeding. Discret Dyn Nat Soc 2010:Article ID 640594

  16. Ashoori A, Moshiri B, Khaki-Sedigh A, Bakhtiari MR (2009) Optimal control of a nonlinear fed-batch fermentation process using model predictive approach. J Process Control 19:1162–1173

    Article  CAS  Google Scholar 

  17. Gong ZH, Liu CY, Feng EM, Zhang QR (2010) Computational method for Infer objective function of glycerol metabolism in klebsiella pneumonia basing on bilevel programming. J Syst Sci Complex 23(2):334–342

    Article  Google Scholar 

  18. Sun YQ, Qi WT, Teng H, Xiu ZL, Zeng AP (2008) Mathematical modeling of glycerol fermentation by klebsiella pneumoniae: concerning enzyme catalytic reductive pathway and transport of glycerol and 1,3-propanediol across cell membrane. Biochem Eng J 38:22–32

    Article  CAS  Google Scholar 

  19. Zhang Q, Teng H, Sun Y, Xiu Z, Zeng A (2008) Metabolic flux and robustness analysis of glycerol metabolism in Klebsiella pneumonia. Bioprocess Biosyst Eng 31:127–135

    Article  CAS  Google Scholar 

  20. Ye J, Feng E et al (2009) Modeling and robustness analysis of biochemical networks of glycerol metabolism by Klebsiella Pneumoniae. Lecture Notes Inst Comput Sci Soc Informa Telecommun Eng 4(1):446–457

    Article  Google Scholar 

  21. Wang J et al (2011) Complex metabolic network of glycerol fermentation by Klebsiella pneumoniae and its system identification via biological robustness. Nonlinear Anal Hybrid Syst 5:102–112

    Article  Google Scholar 

  22. Wang J et al (2011) Modeling and identification of a nonlinear hybrid dynamical system in batch fermentation of glycerol. Math Comput Model 54:618–624

    Article  Google Scholar 

  23. Yan H, Zhang X, Ye J, Feng E (2012) Identification and robustness analysis of nonlinear hybrid dynamical system concerning glycerol transport mechanism. Comput Chem Eng 40:171–180

    Article  CAS  Google Scholar 

  24. Zhai J, Ye J et al (2011) Pathway identification using parallel optimization for a complex metabolic system in microbial continuous culture. Nonlinear Anal Real World Appl 12(5):2730–2741

    Article  CAS  Google Scholar 

  25. Zeng AP, Biebl H (2002) Bulk chemicals from biotechnology: the case of 1.3-propanediol production and the new trends. Adv Biochem Eng Biotechnol 74:239–259

    CAS  Google Scholar 

  26. Kitano Hiroaki (2004) Biological robustness. Nat Rev Genet 5:826–837

    Article  CAS  Google Scholar 

  27. Stelling Jorg, Sauer Uwe, Szallasi Zoltan (2004) Robustness of cellular functions. Cell 118:675–685

    Article  CAS  Google Scholar 

  28. Sleuer Ralf (2007) Computational approaches to the topology, stability and dynamics of metabolic networks. Phytochemistry 68:2139–2151

    Article  Google Scholar 

  29. Sun J, van den Heuvel J, Soucaille P, Qu Y, Zeng AP (2003) Comparative genomic analysis of dha regulon and related genes for anaerobic glycerol metabolism in bacteria. Biotechnol Prog 19:263–272

    Article  CAS  Google Scholar 

  30. Wang SY, Feng EM (2010) Stability of nonlinear microbial bioconversion system concerning glycerol’s active transport and 1, 3-PD’s passive transport. Nonlinear Analysis: Real World Applications 11:3501–3511

    Article  CAS  Google Scholar 

  31. Torres N’estor V, Voit Eberhard O (2002) Pathway analysis and optimization in metabolic engineering. Cambridge University Press, Cambridge

    Book  Google Scholar 

Download references

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No. DUT12LK27), the National Natural Science Foundation of China (Grant Nos. 10671126, 10871033 and 11171050) and the Natural Science Foundation for the Youth of China (No. 11001153).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, L. Determining the transport mechanism of an enzyme-catalytic complex metabolic network based on biological robustness. Bioprocess Biosyst Eng 36, 433–441 (2013). https://doi.org/10.1007/s00449-012-0800-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00449-012-0800-7

Keywords

Navigation