Factor graph-based biomolecular circuit analysis for designing forward error correcting biosensors

We previously reported the fabrication and the verification of novel biomolecular transistors where electrical conductivity of a "polyaniline nanowires" channel is controlled by antigen-antibody interactions. In this paper, we present a simulation framework for analyzing the reliability of biosensor circuits constructed by using these biomolecular transistors. At the core of the proposed framework is a library of electrical circuit models that capture the stochastic interaction between biomolecules and their variability to environmental conditions and experimental protocols. Reliability analysis is then performed by exploiting probabilistic dependencies between multiple circuit elements by using a factor graph-based decoding technique. The proposed computational approach facilitates rapid evaluation of forward error correction (FEC) strategies for biosensors without resorting to painstaking and time-consuming experimental procedures. The analysis presented in this paper shows that an asymmetric FEC biosensor code outperforms a repetition FEC biosensor code which has been proposed for microarray technology. In addition, we also show that the proposed analysis leads to a novel "co- detection" protocol that could be used for reliable detection of trace quantities of pathogens.