We study the problem of counting the number of particles in a closed volume where the particles motion are modeled by a Brownian motion process. This problem arises in many biological and chemical sensing experiments, e.g.
View Article and Find Full Text PDFThis 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.
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