CRISPR-Enabled Graphene-Based Bio-Cyber Interface Model for In Vivo Monitoring of Non-Invasive Therapeutic Processes

Area of Science:

• Biotechnology
• Nanotechnology
• Cybernetics

Background:

• The Internet of Bio-Nano Things requires advanced bio-cyber interfaces.
• Integrating Clustered Regularly Interspace Short Palindromic Repeats (CRISPR) with Graphene-Field effect transistors (GFETs) offers novel detection capabilities.

Purpose of the Study:

• To present a computational model of a bio-cyber interface for bio-nano applications.
• To explore the integration of CRISPR technology and GFETs for enhanced biosensing.

Main Methods:

• A bio-cyber interface model was developed, integrating CRISPR and GFET.
• The system detects nucleic acids transcribed by a bioreporter upon exposure to signalling molecules.
• The interface operation was modeled using simultaneous differential equations and solved numerically.

Main Results:

• The model demonstrates label-free, real-time signal transduction with multi-symbol signaling.
• Performance is sensitive to input molecule concentrations, bioreporter surface receptors, and CRISPR complex levels.
• Signalling molecule elimination rates and RNA degradation significantly impact interface performance and inter-symbol interference.

Conclusions:

• The proposed bio-cyber interface model, utilizing CRISPR-GFET integration, shows promise for sensitive and real-time molecular detection.
• Optimizing parameters like molecule concentration, receptor binding, and degradation rates is crucial for effective bio-nano communication.