Synthetic biology is an engineering discipline in which biological components are assembled to form devices with user-defined functions. As a nascent discipline, genetic circuit design is reserved only for experienced researchers with an in-depth knowledge of biology. This work aims to alleviate some of these constraints by developing software to facilitate genetic circuit design and analysis so that more researchers can participate in this thriving discipline and help elucidate the causes of circuit failures. Firstly, an automatic dynamic model generator can be implemented to predict a circuit’s behavior between steady states and determine the amount of time needed to reach such steady states. Moreover, the analysis of the predicted dynamic behavior will help the designers understand the risks of applying specific input changes and decide whether the risk is critical for the designed systems’ intended purposes. Extrinsic and intrinsic noise can contribute to the observed output variability of a clonal population. Therefore, to account for a genetic circuits’ stochastic behavior, this work aims to develop stochastic modeling using extrinsic and intrinsic noise contributions that can help infer glitch probabilities and elucidate the causes of circuit failure. All the methodologies developed in this work will serve the overacting aim of redesigning genetic circuits to avoid circuit failures. Dynamic ODE modeling will predict glitching behavior and the time to reach said states; stochastic modeling will be used to predict glitch propensities; hazard-preserving transformations will be used to avoid solvable hazards. Facilitated dynamic modeling of genetic circuits would be an instrumental technique for synthetic biologists, especially if it can be accompanied by a circuit design automation tool, such as those proposed in this work. This would help automation in synthetic biology and provide a way to debug circuit designs before construction and compare predictions with experimental data once the synthesized circuit is implemented, saving time, effort, and money. This project aims to expand such capabilities to help researchers through the design process with the development of automated modeling, logic synthesis, hazard identification, and genetic circuit redesign.