Application of a fuzzy learning intervention approach to a purine metabolism pathway model

Adaptive fuzzy control is used here to enforce a concentration level of some metabolite of a biological system representing a purine metabolism pathway model to track a reference trajectory in the presence of uncertainties. In contrast to the direct fuzzy controller, the adaptive fuzzy controller is able to reduce the variance of both the system's response and the controller's output. In this paper, we will apply the adaptive fuzzy intervention strategy to the purine metabolism pathway model in the presence of output noise, which is the source of the model's uncertainties, and carry out a sensitivity analysis of the controller's behavior. The simulation will also be carried out using the direct fuzzy controllers, as described in [1], and the results will be compared and analyzed.