Identifiability and observability analysis for experimental design in nonlinear dynamical models
Dezember 2010
Raue A, Becker V, Klingmu?ller U, Timmer J.
Chaos 20(4):045105
Dynamical models of cellular processes promise to yield new insights into the underlying biological systems. Parameter estimation faces the challenge of nonidentifiability. Nonidentifiability of parameters induces nonobservability of trajectories, reducing the predictive power of the model. We will discuss a generic approach for nonlinear models that allows for identifiability and observability analysis. The results will be utilized to design new experiments that enhance model predictiveness.
