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.