Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
IEEE Trans Biomed Eng. 2011 May;58(5):1260-7. doi: 10.1109/TBME.2010.2099658. Epub 2010 Dec 17.
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.
近年来，人们在生物现象建模以及干预策略的开发方面开展了广泛的研究。S 系统在准确性和数学灵活性之间提供了很好的折衷，是建模生物现象动态行为的有前途的框架。在本文中，针对 S 系统模型提出了两种不同的干预策略，即直接和间接。在间接方法中，使用目标变量的预定期望值来计算控制输入的参考值，并为将控制变量从初始值转移到计算出的参考值，开发了两种控制算法，即简单采样数据控制和模型预测控制（MPC）。在直接方法中，开发了一种 MPC 算法，可直接将目标变量引导至期望的值。将所提出的干预策略应用于糖酵解-糖原分解途径，给出的仿真结果表明了所提出方案的有效性。
