Synthetic biology is an engineering discipline in which biological components are assembled to form devices with user-defined functions. As in any engineering discipline, modeling is a big part of the design process, since it helps to predict, control, and debug systems in an efficient manner. Systems biology has always been concerned with dynamic models, and a recent increase in high-throughput of experimental data has made it essential to develop dynamic models that can be used for an iterative learning process in a design/build/test workflow. In this thesis work, an automated model generator is created to automatically generate dynamic models for genetic regulatory networks, implemented in the genetic design automation tool, iBioSim. This automated model generator uses parameters stored at an online parts repository and encodes the mathematical models it generates using Systems Biology Markup Language. The automated model generator is then used to model and simulate genetic circuits created with the design environment referred to as Cello. The simulation of the mathematical models produces a dynamical response prediction of each of the circuits, which is unavailable with steady-state modeling. Some of these dynamical responses present unexpected behavior. Using the dynamic models generated with the automatic model generator of this work, an analysis of the predicted behaviors yielded insight into the underlying biology phenomena that cause the observed glitching behavior of these circuits. The last chapter of this thesis is focused mainly on future enhancements to the automated model generator of this work to produce more accurate and precise models not only for genetic regulatory networks in emphEscherichia coli, but any organism where parametrization exists as proposed in this thesis work. It also explores different analysis that could be implemented into the automated model generator of this work, in order to expand the assessment done on genetic circuits.