Automatic feedback control for resource-aware characterisation of genetic circuits

Many applications of engineered cells are enabled by genetic circuits - networks of genes regulating each other to process signals. Complex circuits are built by combining standardised modular components with different functions. Nonetheless, genes in a cell compete for the same limited pool of cellular resources, causing unintended interactions that violate modularity. Thus, circuit components behave differently when combined versus when observed in isolation, which can compromise a biotechnologys predictability and reliability. To forecast steady-state interactions between modules, experimental protocols for characterising their resource competition properties have been proposed. However, they rely on open-loop batch culture techniques in which dynamic control signals cannot be applied to cells. Consequently, these experimental methods have limited predictive power, as they may fail to capture all possible steady states, such as repelling equilibria that would not be approached by a system without external forcing. In contrast, we propose a novel, comprehensive protocol for characterising the resource-dependence of genetic modules performance. Based on the control-based continuation technique, it captures both stable and unstable steady states by applying stabilising cybergenetic feedback with an automated cell culturing platform. Using several models with different degrees of complexity, we simulate applying our pipeline to a self-activating genetic switch. This case study illustrates how informative characterisation of a genetic module with automatic feedback control enables reliable forecasting of its performance when combined with any other circuit component. Hence, our protocol promises to restore predictability to the design of genetic circuits from standardised components.