http://iet.metastore.ingenta.com
1887

Structural and practical identifiability analysis of S-system

Structural and practical identifiability analysis of S-system

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • E.O. Voit . (2000)
        6. Voit, E.O.: ‘Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists’ (Cambridge University Press, 2000).
        .
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • I.C. Chou , M.H. , V.E.O. .
        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.
        . Theor. Biol. Med. Model , 3 - 25
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • D. Tominaga , N. Koga , M. Okamoto .
        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.
        . Las Vegas , 251 - 258
    15. 15)
      • Y. Maki , D. Tominaga , M. Okamoto .
        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.
        . Pacific Symposium on Biocomputing , 446 - 458
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • M.P. Saccomani .
        20. Saccomani, M.P.: ‘Structural vs practical identifiability in system biology’, IWBBIO, 2013, pp. 305313.
        . IWBBIO , 305 - 313
    21. 21)
      • M.P. Saccomani .
        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.
        . Bioinformatics and Biomedical Engineering , 650 - 658
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
      • C. Zhan , B. Li , L.F. Yeung .
        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.
        . IEEE 2014 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM) , 48 - 54
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
      • M.V. Shcherbakov , A. Brebels , N.L. Shcherbakova .
        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.
        . World Appl. Sci. J. , 171 - 176
    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
This is a required field
Please enter a valid email address