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Pharmacogenomic systems (PG) are very high dimensional, nonlinear, and stiff systems. Mathematical modeling of these systems, as systems of nonlinear coupled ordinary differential equations (ODE), is considered important for understanding them; unfortunately, it is also a very difficult task. At least as important is to adequately control them through inputs, which are drugs' dosage regimens. In this paper, we investigate new approaches based on compuational intelligences tools - genetic programming (GP), and neural networks (NN) - for these difficult tasks. We use GP to automatically write the model structure in a computer programming language (C++) and to optimize the model's constants. In some circumstances, the proposed methods not only give an accurate mathematical model of the PG system, but they also give insights into the subjacent molecular mechanisms. We also show that NN feedback linearization (FBL) can adequately control these systems, with or without a mathematical model. The drug dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge, this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of GP modeling and NN modeling and control.
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