Synthetic biology is an emergent interdisciplinary research field whose aim is to engineer biological systems with novel functionalities. Through the addition of synthetic genes, biomolecules are produced that create networks of interactions, referred to as genetic circuits, which endow cells with sensing, computation, and actuation capabilities. For example, bacteria can be engineered to recognize and kill cancer cells once in the bloodstream, to neutralize radioactive waste, to sense stress levels and release drugs once in the human gut. Despite tremendous progress, it is still often a challenge to engineer cells in such a way that they behave as predicted. On the one hand, synthetic gene activation causes non-physiological burden on cellular resources that cells are unable to adjust to. This leads to physiological changes and growth rate imbalances that affect in subtle ways the function of the engineered cell. On the other hand, the mathematical models that we use to design genetic circuits often miss relevant physical aspects needed to accurately predict circuit’s dynamics. One of these aspects is spatial heterogeneity inside the cell. This thesis addresses both of these two challenges through a combined theoretical and experimental effort. In the first part of the thesis, I introduce a feedforward controller that increases ribosome level upon activation of a gene of interest (GOI) to compensate for the burden on the cell due to the GOI activation. The controller increases ribosome level by activating a modified SpoT enzyme with sole hydrolysis activity, which lowers ppGpp level and thus de-represses ribosomes. That is, the controller increases the availability of resources once they are demanded by the GOI activation by actuating the cell’s endogenous ribosome regulation system. Without the controller, activation of the GOI decreased growth rate by more than 50%. With the controller, we could activate the GOI to the same level without a growth rate decrease. A cell strain armed with the controller in co-culture enabled persistent population-level activation of a GOI, which could not be achieved by a strain devoid of the controller. The feedforward controller is a tunable, modular, and portable tool that for the first time keeps constant growth rate despite synthetic gene activation. In the second part of the thesis, I model spatial heterogeneity inside bacterial cells and propose a simple modeling framework, useful for design, that accounts for spatial information. We start with a generic partial differential equation (PDE) model that describes the rate of change of species concentration as a function of the location inside the cell. Then, we exploit time scale separation between diffusion and the chemical reaction dynamics to derive a reduced order model consisting solely of ordinary differential equations (ODEs). I then apply this result to study enzymatic-like reactions that are used to model most of the cell’s important processes, including transcription, translation, and their regulation, highlighting significant differences with traditional models. This new model provides a general and simple framework to capture spatial heterogeneity in bacterial cells and thus improves upon the predictive power of current models used to design genetic circuits.Ph.D.