title: Identifiability and observability analysis for experimental design in nonlinear dynamical models.
abstract: Dynamical models of cellular processes promise to yield new insights into the underlying systems and their biological interpretation. The processes are usually nonlinear, high dimensional, and time-resolved experimental data of the processes are sparse. Therefore, parameter estimation faces the challenges of structural and practical 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 by means of a realistic example from systems biology. The results will be utilized to design new experiments that enhance model predictiveness, illustrating the iterative cycle between modeling and experimentation in systems biology.
authors.affiliation: Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@fdm.uni-freiburg.de
authors.forename: A
authors.initials: A
authors.lastname: Raue
authors.forename: V
authors.initials: V
authors.lastname: Becker
authors.forename: U
authors.initials: U
authors.lastname: Klingmüller
authors.forename: J
authors.initials: J
authors.lastname: Timmer
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D004921:Erythropoietin / Q000378:metabolism; D042541:Intracellular Space / Q000378:metabolism; D008024:Ligands; D008954:Models, Biological; D017711:Nonlinear Dynamics; D017467:Receptors, Erythropoietin / Q000378:metabolism; D012107:Research Design
chemical_list: D008024:Ligands; D017467:Receptors, Erythropoietin; D004921:Erythropoietin