An important objective of modeling biological phenomena is to develop therapeutic intervention strategies to move an undesirable state of a diseased network towards a more desirable one. Such transitions can be achieved by the use of drugs to act on some genes/metabolites that affect the undesirable behavior. Biological phenomena are complex processes with nonlinear dynamics that cannot be perfectly described by a mathematical model due to several challenges such as the scarcity of biological data. Therefore, the need for model-free nonlinear intervention strategies that are capable of guiding the target variables to their desired values often arises. Addressing such a need is the main focus of this work. In many applications, fuzzy systems have been found to be very useful for parameter estimation, model development and control design of nonlinear processes. In this work, a model-free fuzzy intervention strategy (that does not require a mathematical model of the biological phenomenon) is proposed to guide the target variables of biological systems to their desired values. The proposed fuzzy intervention strategy is applied to two biological models: a glycolytic-glycogenolytic pathway model and a purine metabolism pathway model. The simulation results of the two case studies show that the fuzzy intervention schemes are able to guide the target variables to their desired values. Moreover, sensitivity analyses are conducted to study the robustness of the fuzzy intervention algorithm to variations in model parameters, and contamination due to measurement noise, in the two case studies, respectively.


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