The problems of modeling and intervention of biological phenomena have captured the interest of many researchers in the past few decades. The aim of the therapeutic intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. Such an objective can be achieved by the application of drugs to act on some genes/metabolites that experience the undesirable behavior. For the purpose of design and analysis of intervention strategies, mathematical models that can capture the complex dynamics of the biological systems are needed. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. Due to the complex nonlinear dynamics of the biological phenomena represented by S-systems, nonlinear intervention schemes are needed to cope with the complexity of the nonlinear S-system models. Here, we present an intervention technique based on feedback linearization for biological phenomena modeled by S-systems. This technique is based on perfect knowledge of the S-system model. The proposed intervention technique is applied to the glycolytic-glycogenolytic pathway, and simulation results presented demonstrate the effectiveness of the proposed technique. 1. Introduction Biological systems are complex processes with nonlinear dynamics. S-systems are proposed in [1, 2] as a canonical nonlinear model to capture the dynamical behavior of a large class of biological phenomena [3, 4]. They are characterized by a good tradeoff between accuracy and mathematical flexibility [5]. In this modeling approach, nonlinear systems are approximated by products of power-law functions which are derived from multivariate linearization in logarithmic coordinates. It has been shown that this type of representation is a valid description of biological processes in a variety of settings. S-systems have been proposed in the literature to mathematically capture the behavior of genetic regulatory networks [6–13]. Moreover, the problem of estimating the S-system model parameters, the rate coefficients and the kinetic orders, has been addressed by several researchers [12, 14–16]. In [17], the authors studied the controllability of S-systems based on feedback linearization approach. Recently, the authors in [18] developed two different intervention strategies, namely, indirect and direct, for biological phenomena modeled by S-systems. The goal of these intervention strategies is to transfer the target variables from an initial steady-state level
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