Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.

The processes that keep a cell alive are constantly challenged by unpredictable changes in its environment. Cells manage to counteract these changes by employing sophisticated regulatory strategies that maintain a steady internal milieu. Recently, the antithetic integral feedback motif has been demonstrated to be a minimal and universal biological regulatory strategy that can guarantee robust perfect adaptation for noisy gene regulatory networks in Escherichia coli . Here, we present a realization of the antithetic integral feedback motif in a synthetic gene circuit in mammalian cells. We show that the motif robustly maintains the expression of a synthetic transcription factor at tunable levels even when it is perturbed by increased degradation or its interaction network structure is perturbed by a negative feedback loop with an RNA-binding protein. We further demonstrate an improved regulatory strategy by augmenting the antithetic integral motif with additional negative feedback to realize antithetic proportional–integral control. We show that this motif produces robust perfect adaptation while also reducing the variance of the regulated synthetic transcription factor. We demonstrate that the integral and proportional–integral feedback motifs can mitigate the impact of gene expression burden, and we computationally explore their use in cell therapy. We believe that the engineering of precise and robust perfect adaptation will enable substantial advances in industrial biotechnology and cell-based therapeutics.

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Mammalian cells collectively maintain a consistent internal milieu that supports their host’s survival in varying and uncertain environments. This homeostasis is often achieved through negative feedback loops that act at various levels of biological organization, from the system and organ levels down to gene expression at the molecular scale. Recently, a molecular regulatory motif has been discovered that enables a regulated variable to adapt perfectly (at the steady state) to network and parameter changes and to persistent environmental perturbations. The regulatory motif that achieves this robust perfect adaptation property realizes integral feedback, a control strategy that employs mathematical integration in a negative feedback loop. Here, we present the first synthetic implementation of integral feedback in mammalian cells. We show that this implementation successfully maintains constant levels of a transcription factor, even when its degradation is significantly increased. Furthermore, we establish the structural robustness properties of our controlled system by demonstrating that perturbing the network topology does not affect the transcription factor levels. We believe that the ability to robustly and predictably regulate the expression levels of genes will both become an indispensable tool for basic research as well as lead to substantial advances in the development of industrial biotechnology and cell-based therapies.