Computational Design of a CRISPR-Cas13-Driven Genetic Logic Gate Biosensor for Early Detection of Breast Cancer Biomarkers

Abstract

Background: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early detection using specific biomarkers and synthetic biology tools can significantly enhance diagnostic precision and therapeutic outcomes. Objective: To develop a computationally validated CRISPR-Cas13-based biosensing system integrated with synthetic genetic logic gates for early and accurate detection of breast cancer biomarkers Methods: A multi-disciplinary approach combining bioinformatics, molecular biology, and computational modeling was employed. Key biomarkers; Human Epidermal Growth Factor Receptor 2 (HER2), Mucin 1 (MUC1), and Epidermal Growth Factor Receptor (EGFR) were identified through literature mining and database analysis (e.g., NCBI, TCGA). Guide RNAs (gRNAs) were designed to specifically target mRNA sequences of these markers using CRISPR- Cas13 technology. Gene circuits were engineered incorporating logic gates (AND, OR, and NOT), promoters, terminators, and fluorescent reporters (e.g., GFP, RFP) to produce a signal only when the correct biomarker combinations were detected. The circuits were computationally modeled and simulated using COPASI for dynamic behavior, logic validation, and parameter sensitivity. In silico simulations guided design refinement prior to in-vitro implementation. Genetic constructs were planned for integration into a microfluidic device using electrochemical biosensing for output signal detection. Results: COPASI-based simulations validated the designed gene circuits, showing correct logic gate operations and stable output signal generation in response to target biomarker inputs. The AND gate circuit showed high specificity for concurrent HER2 and MUC1 detection, while OR and NOT gates enabled flexible detection patterns. Conclusions: The study presents a novel CRISPR-Cas13-based genetic circuit system, computationally designed and validated for breast cancer detection. Integration with genetic logic gates and biosensor interfaces sets the foundation for a precise, programmable diagnostic device.