FAST: a framework for simulation and analysis of large-scale protein-silicon biosensor circuits

Area of Science:

• Biomedical Engineering
• Computer Science
• Materials Science

Background:

• Protein-silicon hybrid circuits are emerging components in advanced biosensor technology.
• Current design and analysis methods for these circuits are often laborious and experimental.
• System-level optimization and discovery of novel sensing modalities are hindered by these limitations.

Purpose of the Study:

• To present a novel computer-aided design (CAD) framework, named FAST, for the verification and reliability analysis of protein-silicon hybrid circuits.
• To enable system-level optimization and the exploration of new biosensing modalities.
• To reduce the need for extensive fabrication and experimental procedures in biosensor development.

Main Methods:

• The FAST framework analyzes protein-based circuits by solving inverse problems using stochastic functional elements.
• It employs a factor-graph netlist as a user interface, with signal passing between internal nodes to solve inverse problems.
• Stochastic analysis techniques, such as density evolution, are utilized for circuit dynamics and reliability estimation.

Main Results:

• A complete design flow using FAST for synthesis, analysis, and verification of a conductometric immunoassay is demonstrated.
• The framework successfully analyzed antibody-based circuits implementing forward error correction (FEC).
• The study validates the framework's capability in handling non-linear relationships within circuit variables.

Conclusions:

• The FAST framework offers a powerful computational tool for designing and analyzing protein-silicon hybrid circuits.
• It facilitates efficient system-level optimization and the discovery of new biosensing applications.
• This approach streamlines biosensor development, reducing reliance on time-consuming experimental validation.