A model-based design strategy to engineer miRNA-regulated detection systems.

miRNAs are promising diagnostic biomarkers. These small RNA molecules are always present in the human body but become dysregulated when a person develops certain diseases. Although the detection of these biomarkers in cell-free tests is ongoing work, current systems often focus solely on detecting the presence or absence of a specific miRNA, rather than the miRNAs concentration. Thus, these tests may miss relative changes in miRNA concentration when disease-induced dysregulation occurs. This work, part of the WUR iGEM 2024 project (miRADAR), aimed to address this gap by incorporating an miRNA concentration-dependent threshold mechanism in a cell-free diagnostic test. In this system, continuous miRNA input concentrations need to be converted into a binary output signal, classifying the miRNA concentration as healthy (no output signal) or indicative of disease (strong output signal). To aid the experimental engineering of the test, here we use mathematical models to evaluate and assess different candidate networks. We apply a previously published multi-objective optimisation strategy to obtain designs that satisfy relevant constraints, such as low basal expression, high readout levels, and steep switching behaviour between low and high input miRNA concentrations. Models for three different biological mechanisms were compared based on their ability to generate the desired binary output signal. One approach used three-node protein networks (such as feed-forward loops), while the other two utilised RNA-based toehold systems. Overall, the toehold-mediated strand displacement systems demonstrated the most potential for experimental implementation. These systems are believed to be less burdensome in a cell-free environment, can be more readily engineered for new miRNA sequences, and showed high detection accuracy. Based on our results, we discuss how the inclusion of sequence-specific parameters could expand the design space of our mathematical models and how careful engineering of optimisation criteria is required to evaluate designs. Ultimately, our model-based study highlights that toehold-mediated strand displacement networks have the potential to be efficient miRNA detection systems for biosensing tools in the future.