Biomolecular circuits rely on shared cellular machinery such as ribosomes for protein synthesis. Levels of these translational resources can vary substantially across growth conditions and cellular contexts, creating potential variability in circuit behavior. While competition for resources among co-expressed genes is recognized, the quantitative relationship between resource fluctuations and the robustness of circuit dynamics has been underexplored. This study addresses that gap by integrating modeling, analytical bounds, and experiments to quantify how changes in translational resources affect circuit stability and to evaluate strategies for mitigation.
The authors combine three elements to study robustness under resource perturbations: a resource-aware gene expression model, analytical bounds based on contraction theory, and experimental tests. The resource-aware model explicitly accounts for shared translational machinery and how its availability influences gene expression dynamics. Contraction theory is used to derive analytical bounds on system behavior—specifically, to characterize the rate at which trajectories converge (the contraction rate) and to bound steady-state deviations caused by perturbations in translational resources.
Using the integrated framework, the authors compare two circuit architectures: a simple constitutive circuit and a circuit redesigned to include negative autoregulatory feedback. The analysis predicts that the constitutive design is sensitive to perturbations in translational resources, showing larger steady-state deviations when resource levels change. In contrast, incorporating negative autoregulation increases the contraction rate, which implies faster convergence of trajectories following perturbations, and reduces the analytical bound on steady-state deviation. The trade-off identified by the model is that the feedback design achieves improved robustness at the expense of lower absolute expression levels.
To test model predictions, the authors performed experiments in E. coli. Two experimental perturbation strategies were reported: variation of plasmid copy number and use of a ribosome sequestration module to alter effective translational resource availability. These perturbations were chosen to produce changes in translational resources that the model was designed to address. Experimental observations were reported as consistent with the model’s predictions regarding sensitivity of the constitutive circuit and improved stability of the feedback circuit under resource variation.
Across modeling and experimental approaches, the constitutive circuit exhibited notable sensitivity to changes in translational resources, with larger changes in expression when resources were perturbed. The negative autoregulatory feedback redesign showed enhanced robustness: an increased contraction rate and a smaller bound on steady-state deviation, resulting in more stable expression across variable resource conditions. However, the feedback circuit reached these stability gains while operating at lower steady-state expression than the constitutive design.
The combined theoretical and experimental findings offer a systematic approach for analyzing and improving the robustness of synthetic biomolecular circuits that must operate in variable cellular environments. Specifically, the work suggests that incorporating negative autoregulatory feedback can be an effective design principle to mitigate the effects of fluctuating translational resources such as ribosomes, with the caveat of reduced expression magnitude. This trade-off can inform design choices in synthetic biology when stability under resource variability is prioritized over maximal expression.
The article is a preprint and has not undergone peer review; readers should interpret results accordingly. The source reports that the authors declared no competing interests. Funder information provided indicates support from the Wellcome Trust/DBT India Alliance. Detailed experimental protocols, quantitative parameter values, and full analytical derivations are contained in the original manuscript; specific numerical results and statistical measures were not reported in the abstract and therefore are not reproduced here.
This study integrates a resource-aware gene expression model, contraction-theory-based analytical bounds, and E. coli experiments to quantify how perturbations in translational resources affect circuit robustness. It shows that constitutive circuits are sensitive to resource changes, while circuits with negative autoregulatory feedback exhibit higher contraction rates and smaller steady-state deviation bounds, albeit with lower expression. The work provides a structured framework for evaluating and improving robustness of biomolecular systems operating under variable translational resource conditions.