title: FAST: a framework for simulation and analysis of large-scale protein-silicon biosensor circuits.
abstract: This paper presents a computer aided design (CAD) framework for verification and reliability analysis of protein-silicon hybrid circuits used in biosensors. It is envisioned that similar to integrated circuit (IC) CAD design tools, the proposed framework will be useful for system level optimization of biosensors and for discovery of new sensing modalities without resorting to laborious fabrication and experimental procedures. The framework referred to as FAST analyzes protein-based circuits by solving inverse problems involving stochastic functional elements that admit non-linear relationships between different circuit variables. In this regard, FAST uses a factor-graph netlist as a user interface and solving the inverse problem entails passing messages/signals between the internal nodes of the netlist. Stochastic analysis techniques like density evolution are used to understand the dynamics of the circuit and estimate the reliability of the solution. As an example, we present a complete design flow using FAST for synthesis, analysis and verification of our previously reported conductometric immunoassay that uses antibody-based circuits to implement forward error-correction (FEC).
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Gu
authors.forename: Shantanu
authors.initials: S
authors.lastname: Chakrabartty
mesh_terms: D000465:Algorithms; D000818:Animals; D015374:Biosensing Techniques; D003198:Computer Simulation; D007118:Immunoassay; D007074:Immunoglobulin G / Q000032:analysis; D051379:Mice; D011506:Proteins / Q000032:analysis*; D011817:Rabbits; D015203:Reproducibility of Results; D012825:Silicon / Q000737:chemistry*; D012984:Software
chemical_list: D007074:Immunoglobulin G; D011506:Proteins; D012825:Silicon