Model-guided design of single-cargo, dual-attractor gene circuits

Abstract

Dual-attractor gene networks program distinct states, guiding native cellular decision-making processes such as differentiation. Synthetic dual-attractor circuits (DACs) emulate native gene networks, directing cells to two different states starting from a single, genetically uniform population. However, existing DAC designs for mammalian cells often require multiple genetic cargoes, limiting their application to control cell fate in therapeutically relevant contexts. Here, we leverage model-guided tuning of compact biochemical and biophysical layers of regulation to program two cellular states using a single-cargo DAC. Through activator selection and layering microRNA-mediated mutual repression, we identify and resolve an imbalance between modules that biases states, allowing us to achieve balanced adoption of both states without relying on copy-number variation. Consolidating this optimized DAC into a single cargo, we use gene syntax to minimize bias, generating two balanced states from a single cargo of less than 11 kilobases. Overall, this work establishes design rules for combined biochemical and biophysical tuning of DACs to generate balanced cell states from a single-cargo design, paving the way for programming divergent cell-state trajectories and dual-fate systems.