• DocumentCode
    1404392
  • Title

    Intervention in Biological Phenomena Modeled by S-Systems

  • Author

    Meskin, N. ; Nounou, H. ; Nounou, M. ; Datta, A. ; Dougherty, E.R.

  • Author_Institution
    Dept. of Electr. Eng., Qatar Univ., Doha, Qatar
  • Volume
    58
  • Issue
    5
  • fYear
    2011
  • fDate
    5/1/2011 12:00:00 AM
  • Firstpage
    1260
  • Lastpage
    1267
  • Abstract
    Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect approach, the prespecified desired values for the target variables are used to compute the reference values for the control inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies are applied to the glycolytic-glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of the proposed schemes.
  • Keywords
    biology computing; genetics; physiological models; predictive control; MPC algorithm; S-system model; biological phenomena modeling; glycolytic-glycogenolytic pathway; mathematical flexibility; model predictive control; sampled-data control; Biological system modeling; Equations; Mathematical model; Optimization; Predictive models; Steady-state; Genetic regulatory networks; S-systems; intervention; model predictive control; Algorithms; Gene Regulatory Networks; Gluconeogenesis; Glycolysis; Models, Biological; Models, Statistical; Systems Biology;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2010.2099658
  • Filename
    5668500