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Biochemical Metabolic Modelling Using Fuzzy Type-2.

Published: 30 December 2012
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Abstract

In his study a new approach, the use of fuzzy logic type-2 in modeling biochemical reactions is shown. In fact, each enzymatic reaction is modeled by means of a "sigmoid transfer function" relating input and output substrate concentrations. The slant of this function is adjusted using fuzzy type-2. This adjustment is conducted depending on the enzymatic reaction type (having activator/inhibitors or not). The obtained model seems promising in order to permit quantitative results to process data concerning adverse drugs reactions. In this paper it is also proved that by fuzzy type-2 logic, the performance characteristics of the modeling will be improved using the proposed method.

Published in American Journal of Physical Chemistry (Volume 1, Issue 1)
DOI 10.11648/j.ajpc.20120101.13
Page(s) 14-17
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2012. Published by Science Publishing Group

Keywords

Biochemical, Metabolic, Modeling, Fuzzy Type-2

References
[1] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoningC1, Information Sciences, vol. 8, pp. 199C249, 1975.
[2] J.M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper-Saddle River, NJ, 2001.
[3] F. Shabaninia, "Type-2 Fuzzy Multiagent Traffic Signal Control", IEEE-IRI Conference, Las Vegas 2012.
[4] F. Shabaninia, "Fuzzy Type-2 Electrode Position Controls for an Electric Arc Furnace ", IEEE-IRI Conference, Las Vegas 2012.
[5] J. M. Mendel, Type-2 Fuzzy Sets and Systems: An Overview, IEEE Computational Intelligence Magazine, 2007.
[6] N. N. Karnik, J. M. Mendel, Q. Liang, Type-2 Fuzzy Logic Systems, IEEE Transactions on Fuzzy Systems, vol. 7, NO. 6, 1999.
[7] G.B. Grindey and Y.C. Cheng, "Biochemical and kinetic approaches to inhibition of multiple pathways".  Pharmacy and therapy, 4, pp.307-327, 1979
[8] C. Reder, "Metabolic control theory", Journal of Theoretical Biology, 135, pp. 175-201, 1988.
[9] B. Solaiman and D. Picart, "Neural networks modeling of biochemical reactions",  IEEE Intenational Conference on Neural Networks,  ICNN94,  June 26-Jul 2, Orlando, USA 1994
[10] G. F. Cerofolini and P. Amato, "Fuzzy Chemistry-An Axiomatic Theory for General Chemistry", IEEE 2007
[11] Christian Wagner and Simon Miller, "A Fuzzy Toolbox for the R Programming Language", IEEE 2011.
[12] Xiang Li, Xin Lu, Jing Tian,, Peng Gao, Hongwei Kong, and Guowang Xu*,?Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics", Anal. Chem. 2009, 81, 4468C447.
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  • APA Style

    Zahra Shabaninia. (2012). Biochemical Metabolic Modelling Using Fuzzy Type-2.. American Journal of Physical Chemistry, 1(1), 14-17. https://doi.org/10.11648/j.ajpc.20120101.13

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    ACS Style

    Zahra Shabaninia. Biochemical Metabolic Modelling Using Fuzzy Type-2.. Am. J. Phys. Chem. 2012, 1(1), 14-17. doi: 10.11648/j.ajpc.20120101.13

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    AMA Style

    Zahra Shabaninia. Biochemical Metabolic Modelling Using Fuzzy Type-2.. Am J Phys Chem. 2012;1(1):14-17. doi: 10.11648/j.ajpc.20120101.13

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  • @article{10.11648/j.ajpc.20120101.13,
      author = {Zahra Shabaninia},
      title = {Biochemical Metabolic Modelling Using Fuzzy Type-2.},
      journal = {American Journal of Physical Chemistry},
      volume = {1},
      number = {1},
      pages = {14-17},
      doi = {10.11648/j.ajpc.20120101.13},
      url = {https://doi.org/10.11648/j.ajpc.20120101.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpc.20120101.13},
      abstract = {In his study a new approach, the use of fuzzy logic type-2 in modeling biochemical reactions is shown. In fact, each enzymatic reaction is modeled by means of a "sigmoid transfer function" relating input and output substrate concentrations. The slant of this function is adjusted using fuzzy type-2. This adjustment is conducted depending on the enzymatic reaction type (having activator/inhibitors or not). The obtained model seems promising in order to permit quantitative results to process data concerning adverse drugs reactions. In this paper it is also proved that by fuzzy type-2 logic, the performance characteristics of the modeling will be improved using the proposed method.},
     year = {2012}
    }
    

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  • TY  - JOUR
    T1  - Biochemical Metabolic Modelling Using Fuzzy Type-2.
    AU  - Zahra Shabaninia
    Y1  - 2012/12/30
    PY  - 2012
    N1  - https://doi.org/10.11648/j.ajpc.20120101.13
    DO  - 10.11648/j.ajpc.20120101.13
    T2  - American Journal of Physical Chemistry
    JF  - American Journal of Physical Chemistry
    JO  - American Journal of Physical Chemistry
    SP  - 14
    EP  - 17
    PB  - Science Publishing Group
    SN  - 2327-2449
    UR  - https://doi.org/10.11648/j.ajpc.20120101.13
    AB  - In his study a new approach, the use of fuzzy logic type-2 in modeling biochemical reactions is shown. In fact, each enzymatic reaction is modeled by means of a "sigmoid transfer function" relating input and output substrate concentrations. The slant of this function is adjusted using fuzzy type-2. This adjustment is conducted depending on the enzymatic reaction type (having activator/inhibitors or not). The obtained model seems promising in order to permit quantitative results to process data concerning adverse drugs reactions. In this paper it is also proved that by fuzzy type-2 logic, the performance characteristics of the modeling will be improved using the proposed method.
    VL  - 1
    IS  - 1
    ER  - 

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Author Information
  • Faridoon Shabaninia, Senior Member, IEEE, Shiraz University, Iran

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