Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing intervention strategies for them. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. One of the main challenges for the development of intervention strategies for biological phenomena is that usually not all the variables (for instance, metabolite concentrations) are available for measurement. This can be due to the difficulty of or the cost associated with obtaining these measurements. Moreover, the available measurements may be noisy with a low sampling rate. In this paper, an intervention strategy is proposed for the S-system model in the presence of partial noisy measurements. In the proposed approach, first a stochastic nonlinear estimation algorithm, namely the unscented Kalman filter, is utilized for estimating the unmeasured variables of the S-system. Then, based on the estimated variables, a model predictive control algorithm is developed to guide the target variables to their desired values. The proposed intervention strategy is applied to the glycolytic-glycogenolytic pathway and the simulation result presented demonstrates the effectiveness of the proposed scheme.