
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.
Research activities, Biological phenomena, Dynamical behaviors, Models, Biological, target variable, Control inputs, Reference values, Predictive control systems, Models, steady state, Gene Regulatory Networks, Model predictive control, Control algorithms, intervention, Mathematical models, algorithm, Models, Statistical, Systems Biology, article, Gluconeogenesis, reference value, Statistical, S-systems, glycolysis, Biological, biological model, glycogenolysis, Control variable, Initial values, Intervention strategy, Simulation result, Sampled-data control, s system, Direct approach, Glycolysis, Genetic regulatory networks, Algorithms
Research activities, Biological phenomena, Dynamical behaviors, Models, Biological, target variable, Control inputs, Reference values, Predictive control systems, Models, steady state, Gene Regulatory Networks, Model predictive control, Control algorithms, intervention, Mathematical models, algorithm, Models, Statistical, Systems Biology, article, Gluconeogenesis, reference value, Statistical, S-systems, glycolysis, Biological, biological model, glycogenolysis, Control variable, Initial values, Intervention strategy, Simulation result, Sampled-data control, s system, Direct approach, Glycolysis, Genetic regulatory networks, Algorithms
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