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Reseach Article

Fuzzy Approach to Regulate S-type Biological Systems

by Shinq-Jen Wu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 10
Year of Publication: 2021
Authors: Shinq-Jen Wu
10.5120/ijca2021921409

Shinq-Jen Wu . Fuzzy Approach to Regulate S-type Biological Systems. International Journal of Computer Applications. 183, 10 ( Jun 2021), 50-55. DOI=10.5120/ijca2021921409

@article{ 10.5120/ijca2021921409,
author = { Shinq-Jen Wu },
title = { Fuzzy Approach to Regulate S-type Biological Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 10 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number10/31967-2021921409/ },
doi = { 10.5120/ijca2021921409 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:28.463507+05:30
%A Shinq-Jen Wu
%T Fuzzy Approach to Regulate S-type Biological Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 10
%P 50-55
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is important to regulate biological systems to return to nominal steady states such that systems are able to maintain normal functions.S-type biological systems(S-systems) are described as power-law-based differential equations which are able to shownetinteractive strength between constitutes and a result, S-system becomes the most potential model for large-scale systems. Biological systems always possess a lot of uncertainties and noises. Fuzzy sets and models are able to describe, recognize and manipulate data that are vague and lack certainty.However, biological systems are different from electromechanical systems thatallow various types of time-varying signalsas system inputs. Therefore, step functions are used as fuzzy outputs to denote constant concentration and the firing strength of each fuzzy rule is the blending or allocating ratio.A cascade pathway is concerned andan exponentially decaying model is used to describe the functional degradation phenomenon. Dry-lab experiments are carried out in five different situations. Simulation results show that the proposed seven-rule fuzzy logic controllers are able to find out the nominal values of independent variables and force systems to return to their nominal steady states.The larger the nominal values are the longer the time to reach targets.

References
  1. Tyson, J.J. 1991. Modeling the cell division cycle: cdc2 and cyclin interactions.Proc. Natl. Acad. Sci. USA 88:7328-7332.
  2. Tyson, J.J., Chen, K.C.,and Novak, B. 2003. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15:221-231.
  3. Tavassoly, I.,Parmar, J., Shajahan-Haq, A,, Clarke, R., Baumann, W., and Tyson, J.J. 2015. Dynamic modeling of the interaction between autophagy and apoptosis in mammalian cells.CPT Pharmacometrics Syst Pharmacol 4(4):263-72.
  4. Liu, P. K., and Wang, F. S. 2010.Inverse problems of biological systems using multi-objective optimization. Journal of the Chinese Institute of Chemical Engineers. 39(5):399-406.
  5. Wu, S. J., Wu, C. T.,and Chang, J. Y. 2012. Fuzzy-based self-interactive multi-objective evolution optimization for reverse engineering of biological networks.IEEE Transactions on Fuzzy Systems20(5):865-882.
  6. Wu, S. J., Wu, C. T.,and Chang, J. Y. 2013.Adaptive neural-based fuzzy modeling for biological systems. Math Biosci 242(2):153-60.
  7. Wu, S. J., and Wu, C. T. 2013.Computational optimization for S-type biological systems: cockroach genetic algorithm.Math Biosci 245(2):299-313.
  8. Wu, S. J, and Wu, C. T. 2014.Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems.Comput Biol Chem 53(2):292-307.
  9. Wu, S. J, and Wu, C. T. 2015.A bio-inspired optimization for inferring interactive networks: cockroach swarm evolution. Expert Syst Appl 42(6): 3253-3267.
  10. Wu, S. J, and Wu, C. T.2018.Smarten up computational intelligence to decipher time series data.Appl Soft Comput72:442-456.
  11. Liu, S., Tao, C., Huang, Z, and Huang S. 2010. Modeling of p53 signaling pathway based on S-system equations.Journal of Biomedical Engineering 27(3):505-10 (Chinese)
  12. Luo, Z.P., An, K.N. 2001.Fuzzy systems in biomedical science. BIOMEDICAL SCIENCE. International Journal of General Systems 30(2):209–217.
  13. Komiyama, M., Yoshimoto, K., Sisido, M., Ariga, K. 2017. Chemistry can make strict and fuzzy controls for bio-Systems: DNA nanoarchitectonics and cell-macromolecular nanoarchitectonics. Bulletin of the Chemical Society of Japan 90(9):967–1004.
  14. Abyad, M., Karama, A., and Khallouq, A. 2017.Modelling and control of a biological process using the fuzzy logic Takagi-Sugeno. In Proceeding of the 2017 International Renewable and Sustainable Energy Conference (IRSEC).
  15. Bordon, J., Moskon, M., Zimic, N., and Mraz, M. 2015. Fuzzy logic as a computational tool for quantitative modelling of biological systems with uncertain kinetic data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 12(5):1199–1205.
  16. Liu, F., Heiner, M., and Gilbert, D.2020.Fuzzy Petri nets for modelling of uncertain biological systems. Briefings in Bioinformatics 21(1):198–210.
  17. Liu, F., Sun, W., Heiner, H., and Gilbert, G. 2021. Hybrid modelling of biological systems using fuzzy continuous Petri nets. Briefings in Bioinformatics22(1):438–450.
  18. Zhu, X.L., Jiang, Z.Y., Wang, B., and He Y.J. 2018. Decoupling control based on fuzzy neural-network inverse system in marine biological enzyme fermentation process. IEEE Access, 6:36168–36175.
  19. Wu, S.J., Wu,C.T., and Chang J.Y.2013.Adaptive neural-based fuzzy modeling for biological systems. Math Biosci242(2):153–160.
  20. http://www.medicineslearningportal.org/2015/07/kidney-and-liver-clearance.html
  21. Dyken, J. D. V. 2017. Noise slows the rate of Michaelis-Menten reaction. J Theor Biol 430:21-31.
  22. Tsai, K.Y., Wang, F.S. 2005.Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics 21(7):1180-8.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy logic control systems biology computational biology