On noise processes and limits of performance in biosensors

Abstract

In this paper, we present a comprehensive stochastic model describing the measurement uncertainty, output signal, and limits of detection of affinity-based biosensors. The biochemical events within the biosensor platform are modeled by a Markov stochastic process, describing both the probabilistic mass transfer and the interactions of analytes with the capturing probes. To generalize this model and incorporate the detection process, we add noisy signal transduction and amplification stages to the Markov model. Using this approach, we are able to evaluate not only the output signal and the statistics of its fluctuation but also the noise contributions of each stage within the biosensor platform. Furthermore, we apply our formulations to define the signal-to-noise ratio, noise figure, and detection dynamic range of affinity-based biosensors. Motivated by the platforms encountered in practice, we construct the noise model of a number of widely used systems. The results of this study show that our formulations predict the behavioral characteristics of affinity-based biosensors which indicate the validity of the model.

Refinement of the Dichlorofluorescein Assay for Flow Cytometric Measurement of Reactive Oxygen Species in Irradiated and Bystander Cell Populations

“…4). This response is consistent with the probe saturation inherent in detection systems that contain a finite number of probes (44,45). Whereas the dynamic range under the current conditions likely extends beyond 10 Gy, the effects of probe saturation at higher doses can be offset by increasing the concentration of DCFH-DA, which was previously used successfully in vitro up to at least 200 M (46,47).…”

Supersensitive fingerprinting of explosives by chemically modified nanosensors arrays

“…When chemically modified in a multiplexed mode, by the surface immobilization of multiple non-specific small chemical receptors, nanowire-based fieldeffect-transistor arrays (NW-FETs) enable the supersensitive discriminative detection, fingerprinting, of multiple explosive molecules, down to the parts-per-quadrillion concentration range 19 . The differential identification between explosives is achieved by pattern recognizing the naturally inherent interaction, both kinetically and thermodynamically [20][21][22][23][24][25][26][27] , between the chemically modified nanosensors array and the chemical analytes under test. Thus, a unique explosives fingerprinting database can be created, enabling to set apart similar chemical entities, and providing a fast and reliable method to identify individual chemical agents.…”

Antibody-Conjugated Gold Nanoparticle-Based Immunosensor for Ultra-Sensitive Detection of Troponin-T

“…By comparing the impedance values measured for each troponin-T sample with the noise, the lowest antigen concentration that allowed for a 3:1 signal-to-noise ratio or better was determined to be the limit of detection for that experimental condition. 19 …”