The behavior of gene circuits is context-dependent, that is, the input/output functionality of a circuit depends on its context. Context includes other systems to which the circuit directly connects, which apply a load (retroactivity), and systems that are simply present in the cellular environment. The latter ones, in particular, also affect the functionality of the circuit due to sharing a common pool of limited resources. Because of these context-effects, a set of new â€œhiddenâ€? interactions appear in gene networks, which dramatically change the expected networkâ€™s behavior. These hidden interactions confound both the design of de novo systems in synthetic biology and the analysis of existing natural systems. In this talk, I will present a systematic modeling framework that captures hidden interactions in a networkâ€™s description and provides simple graphical rules to draw them. I will then present recent experimental results performed in our lab that validate these predictions. Finally, I will illustrate that a distributed control scheme, in which the local negative feedback at each node is realized through mRNA interference, can mitigate the effects of those hidden interactions due to scarcity of resources needed for gene expression.