Application of a fuzzy learning intervention approach to a Glycolytic-Glycogenolytic pathway model

In recent years, may researchers have been interested in modeling and developing therapeutic intervention strategies for biological systems. The objective of intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. It is well known that biological phenomena are complex nonlinear processes that are impossible to perfectly represent using mathematical models, and hence it is of real importance to develop model-free nonlinear intervention strategies that are capable of effectively guiding the target variables to their desired values. Non-adaptive direct fuzzy controllers have been found to be very useful for such applications. However, due to the time-varying nature of biological systems, non-adaptive techniques often fail to maintain the desired closed-loop performance. Hence, there is a need for adaptive strategies that are capable not only of controlling but also maintaining the desired performance in the presence of plant uncertainties or parameter variations. This paper addresses the application problem of controlling a biological system representing the Glycolytic-Glycogenolytic system, where the simulation results show the efficacy of fuzzy controllers in controlling and maintaining the desired performance.