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AuthorMeskin, Nader
AuthorNounou, H.N.
AuthorNounou, M.
AuthorDatta, A.
AuthorDougherty, E.R.
Available date2022-04-14T08:45:47Z
Publication Date2011
Publication NameIEEE Transactions on Biomedical Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TBME.2010.2099658
URIhttp://hdl.handle.net/10576/29839
AbstractRecent 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.
SponsorQatar Foundation; Qatar National Research Fund
Languageen
PublisherIEEE
SubjectBiological 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
TitleIntervention in biological phenomena modeled by S-systems
TypeArticle
Pagination1260-1267
Issue Number5
Volume Number58


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