Abstract
DNA microarrays detect the presence and quantify the amounts of nucleic acid molecules of interest. They rely on a chemical attraction between the target molecules and their Watson-Crick complements, which serve as biological sensing elements (probes). The attraction between these biomolecules leads to binding, in which probes capture target analytes. Recently developed realtime DNA microarrays are capable of observing kinetics of the binding process. They collect noisy measurements of the amount of captured molecules at discrete points in time. Molecular binding is a random process which, in this paper, is modeled by a stochastic differential equation. The target analyte quantification is posed as a parameter estimation problem, and solved using a Markov Chain Monte Carlo technique. In simulation studies where we test the robustness with respect to the measurement noise, the proposed technique significantly outperforms previously proposed methods. Moreover, the proposed approach is tested and verified on experimental data.
Highlights
Molecular biosensors [1] are devices that contain a biological sensing element closely coupled with a transducer
Detection in affinity biosensors [2] relies on chemical attraction between target analytes and their molecular complements, which serve as biological sensing elements
We propose a comprehensive stochastic model of the binding process and state a Markov Chain Monte Carlo (MCMC) algorithm for the estimation of the target analytes
Summary
Molecular biosensors [1] are devices that contain a biological sensing element closely coupled with a transducer. The sensitivity, dynamic range, and resolution of DNA microarrays are limited by interference, noise, probe saturation, and other sources of errors in the analyte detection procedure Several of these limitations stem from the fact that the molecular binding is a stochastic process, which many of the conventional affinity biosensors attempt to characterize based on a single measurement of its equilibrium, that is, by taking one sample from the steady-state distribution of the binding process. In [11], analyte targets in real-time DNA microarrays are estimated using the temporally sampled kinetics of the binding process. We propose a comprehensive stochastic model of the binding process and state a Markov Chain Monte Carlo (MCMC) algorithm for the estimation of the target analytes.
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