Synthetic biology-driven CRISPR-Cas13 biosensing system for early detection of breast cancer biomarkers

Abstract

Breast cancer is a significant global health concern and one of the primary causes of cancer related mortality among women. The current diagnostic methods often suffer from limitations in sensitivity, specificity, and multiplex detection capacity, especially in early-stage cancers. This study presents a novel approach that leverages synthetic biology and CRISPR technology to design a programmable biosensing system for breast cancer detection. The core objective was to computationally develop a Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-associated protein 13 (CRISPRCas13) based biosensor capable of detecting three critical breast cancer biomarkers HER2, MUC1, and EGFR, using synthetic genetic logic circuits. Specific guide RNAs (gRNAs) were designed to selectively bind and activate the Cas13 enzyme in response to target mRNA sequences. Logic circuits implementing AND, OR, and NOT gates were constructed using synthetic promoters, terminators, and fluorescent reporters such as GFP and RFP. These gene circuits were modeled and simulated using COPASI software to assess logic functionality, signal dynamics, and noise robustness. Results demonstrated that the AND gate activated only when HER2 and MUC1 were simultaneously present, the OR gate responded to any of the three biomarkers, and the NOT gate suppressed output in the presence of EGFR. Simulation outputs confirmed high specificity, sensitivity, and system stability under varying biomarker concentrations. The circuits are conceptually adaptable for microfluidic integration and point of care applications. This computational framework shows significant promise in building next-generation diagnostic tools with customisable logic-based biosensing capabilities. Future directions include experimental validation, real-time reporter optimisation, and integration into portable diagnostic platforms. This work lays the groundwork for personalised and logic-based diagnostics, offering a flexible platform for broader clinical and translational applications.