Dynamic models, usually written in forms of differential equations (DEs), de scribe the rate of change of a process.They are widely used in medicine, engineer ing, ecology and a host of other applications.One central and difficult problem is how to estimate DE parameters from noisy data.We have developed the generalized profiling method to solve this problem.DE solutions arc approximated by non parametric functions, which are estimated by penalized smoothing with DE-defined penalty.The computation is much faster than other methods.A modified delta method is proposed to estimate variances of DE parameters, which include all the uncertainty of the smoothing process.I will demonstrate our method with estimat ing a predator-prey dynamic model and gene regulatory networks.The generalized profiling method can also be used to estimate other statistical models with nuisance parameters.