Parameter Estimation by Picard-Iteration for Biochemical Networks with Noisy Data

摘要

Mathematical models of biochemical reaction networks are widespread used to shade light and understand biological process. The validation and parametert estimation of such models is an important task to ensure the quality of the model. Many parameter estimation strategies for biochemical reaction networks exist by now. This work focuses on the use of an Picard iteration based parameter estimation approach for ODE-based dynamical models, which inherently exploits the model structure. In the frame of this work the influence of measurement errors and noise on the resulting parameter estimation is examined.