System identification: theory for the user
System identification: theory for the user
Identifiability of parametric models
Identifiability of parametric models
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Algorithm 573: NL2SOL—An Adaptive Nonlinear Least-Squares Algorithm [E4]
ACM Transactions on Mathematical Software (TOMS)
Estimation Techniques for Distributed Parameter Systems
Estimation Techniques for Distributed Parameter Systems
Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software
Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software
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The purpose of this paper is to present some numerical tools which facilitate the interpretation of simulation or data fitting results and which allow computation of optimal experimental designs. They help to validate mathematical models describing the dynamical behavior of a biological, chemical, or pharmaceutical system, without requiring a priori knowledge about the physical or chemical background. Although the ideas are quite general, we will concentrate our attention to systems of one-dimensional partial differential equations and coupled ordinary differential equations. A special application model serves as a case study and is outlined in detail. We consider the diffusion of a substrate through cutaneous tissue, where metabolic reactions are included in form of Michaelis-Menten kinetics. The goal is to simulate transdermal drug delivery, where it is supposed that experimental data are available for substrate and metabolic fluxes. Numerical results are included based on laboratory data to show typical steps of a model validation procedure, i.e., the interpretation of confidence intervals, the compliance with physical laws, the identification and elimination of redundant model parameters, the computation of optimum experimental designs and the identifiability of parameters by determining weight distributions.