Abstract
The complexity of gene regulatory networks described by coupled nonlinear differential equations is often an obstacle for analysis purposes. Therefore, the development of effective model reduction techniques is of paramount importance in the field of systems biology. In this paper, we apply the theory of nonlinear balanced truncation for model reduction for gene regulatory networks based only on standard matrix computations. The method is based on finding a controllability and observability function of the nonlinear system and thus obtain a balanced representation that produces singular value functions which are functions of the state. As a result, we obtain a ranked contribution of the states from an input - output perspective.
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