Intervention in biological phenomena modeled by S-systems
Author | Meskin, Nader |
Author | Nounou, H.N. |
Author | Nounou, M. |
Author | Datta, A. |
Author | Dougherty, E.R. |
Available date | 2022-04-14T08:45:47Z |
Publication Date | 2011 |
Publication Name | IEEE Transactions on Biomedical Engineering |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/TBME.2010.2099658 |
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 glycolyticglycogenolytic pathway and the simulation results presented demonstrate the effectiveness of the proposed schemes. 2006 IEEE. |
Sponsor | Qatar Foundation; Qatar National Research Fund |
Language | en |
Publisher | IEEE |
Subject | Biological phenomena Control algorithms Control inputs Control variable Direct approach Dynamical behaviors Genetic regulatory networks Initial values intervention Intervention strategy Reference values Research activities S-systems Sampled-data control Simulation result Algorithms Mathematical models Predictive control systems Model predictive control algorithm article biological model glycogenolysis glycolysis reference value s system steady state target variable Algorithms Gene Regulatory Networks Gluconeogenesis Glycolysis Models, Biological Models, Statistical Systems Biology |
Type | Article |
Pagination | 1260-1267 |
Issue Number | 5 |
Volume Number | 58 |
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