Improving predictive performance of nucleotide biosensors via equivalent circuit modeling in transcriptomic analysis

Area of Science:

• Biosensor technology
• Electrical engineering
• Molecular diagnostics

Background:

• Consumer-oriented diagnostic devices for early cancer detection are limited by high error rates and low predictive accuracy.
• Existing DNA/RNA-based impedimetric biosensors require improvement for reliable transcriptomics analysis.

Purpose of the Study:

• To develop an equivalent circuit modeling methodology to enhance the predictive accuracy of DNA/RNA-based impedimetric biosensors.
• To minimize errors and achieve label-free, user-friendly biosensor designs for early cancer screening.

Main Methods:

• Developed a biosensor by immobilizing probe DNA on gold nanoparticle-modified screen-printed electrodes.
• Employed simulation and curve-fitting techniques using an extended Randles circuit model to simulate multilayer structures.
• Investigated modified Randles circuits with capacitive, Cole-Cole, and inductive elements for magnetic nanoparticle-based systems.

Main Results:

• The extended Randles circuit model reduced error rates to approximately 1.8%-2.2% for gold nanoparticle systems.
• Magnetic nanoparticle-based biosensors achieved high fitting accuracy with errors as low as 1-2% using a modified Randles circuit.
• The biosensor demonstrated ultra-low detection limits (0.5 ag/mL), suitable for transcriptomic biomarkers like miRNA.

Conclusions:

• Equivalent circuit modeling significantly improves the predictive accuracy of nucleotide-based biosensors.
• The developed methodology supports scalable, label-free, and user-centric applications in transcriptomics-based diagnostics.
• This approach holds potential for decentralized cancer screening in both rural and urban settings.