Effects of scaling on the SNR and speed of biosensors

This work presents a stochastic model for the observed signal of biosensors, a model that predicts the signal fluctuation of the system and the SNR associated with it using a Markov chain process. In the process, transition probabilities are derived from the target and probe binding kinetics in view of statistical motion and random walk events. Based on this model, we are able to estimate the settling time, power-spectral density (PSD), and signal to noise ratio (SNR) of general affinity-based biosensors. The effects of scaling from macroscopic to microscopic regimes are also studied, which indicate a fundamental tradeoff between settling time (speed) and signal fluctuation (noise). The model is also applied to analyze the behavior of a DNA hybridization electronic detector.