access icon free Structural and practical identifiability analysis of S-system

In the field of systems biology, biological reaction networks are usually modelled by ordinary differential equations. A sub-class, the S-systems representation, is a widely used form of modelling. Existing S-systems identification techniques assume that the system itself is always structurally identifiable. However, due to practical limitations, biological reaction networks are often only partially measured. In addition, the captured data only covers a limited trajectory, therefore data can only be considered as a local snapshot of the system responses with respect to the complete set of state trajectories over the entire state space. Hence the estimated model can only reflect partial system dynamics and may not be unique. To improve the identification quality, the structural and practical identifiablility of S-system are studied. The S-system is shown to be identifiable under a set of assumptions. Then, an application on yeast fermentation pathway was conducted. Two case studies were chosen; where the first case is based on a larger state trajectories and the second case is based on a smaller one. By expanding the dataset which span a relatively larger state space, the uncertainty of the estimated system can be reduced. The results indicated that initial concentration is related to the practical identifiablity.

Inspec keywords: microorganisms; biochemistry; cellular biophysics; fermentation; differential equations

Other keywords: relatively larger state space; system biology; identification quality; S-system; structural identifiability analysis; local snapshot; partial system dynamics; ordinary differential equations; biological reaction networks; practical identifiability analysis; estimated model; state trajectories; yeast fermentation pathway

Subjects: Cellular biophysics; Physical chemistry of biomolecular solutions and condensed states; Numerical approximation and analysis; Function theory, analysis

References

    1. 1)
      • 6. Voit, E.O.: ‘Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists’ (Cambridge University Press, 2000).
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 36. Shcherbakov, M.V., Brebels, A., Shcherbakova, N.L., et al: ‘A survey of forecast error measures’, World Appl. Sci. J., 2013, 24, pp. 171176.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 10. Chou, I.C., , M.H., , V.E.O.: ‘Parameter estimation in biochemical systems models with alternating regression’, Theor. Biol. Med. Model, 2006, 19, pp. 325.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • 20. Saccomani, M.P.: ‘Structural vs practical identifiability in system biology’, IWBBIO, 2013, pp. 305313.
    24. 24)
    25. 25)
      • 21. Saccomani, M.P.: ‘Identifiability of nonlinear ode models in systems biology: Results from both structural and data-based methods’. Bioinformatics and Biomedical Engineering, Springer, 2015, pp. 650658.
    26. 26)
    27. 27)
    28. 28)
      • 15. Maki, Y., Tominaga, D., Okamoto, M., et al: ‘Development of a system for the inference of large scale genetic networks’. Pacific Symposium on Biocomputing, Citeseer, 2001, vol. 6, pp. 446458.
    29. 29)
    30. 30)
    31. 31)
    32. 32)
      • 14. Tominaga, D., Koga, N., Okamoto, M.: ‘Efficient numerical optimization algorithm based on genetic algorithm for inverse problem’. Las Vegas, Nevada, USA, July 2000, pp. 251258.
    33. 33)
      • 30. Zhan, C., Li, B., Yeung, L.F.: ‘On structural identifiability of s-system’. IEEE 2014 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), 2014, pp. 4854.
    34. 34)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2015.0014
Loading

Related content

content/journals/10.1049/iet-syb.2015.0014
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading