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State Estimation of the Time-Varying and Spatially Localized Concentration of Signal Molecules from the Stochastic Adsorption Dynamics on the Carbon Nanotube-Based Sensors and Its Application to Tumor Cell Detection.

Jang H, Lee JH, Braatz RD - PLoS ONE (2015)

Bottom Line: Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules.State estimators are proposed for these types of sensors that fully address their stochastic nature.In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.

ABSTRACT
This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.

No MeSH data available.


Related in: MedlinePlus

An example of a SWNT-based sensor array system.
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pone.0141930.g001: An example of a SWNT-based sensor array system.

Mentions: The basic mechanism of SWNT-based sensors is optical detection of discretized light intensity changes induced by adsorption and desorption of target molecules on the sensor’s surface at nano-scale. To enhance the sensitivity and selectivity for target molecules, usually present at the micromolar (μM) concentration level, the SWNT surface is functionalized by wrapping the nanotube with various polymers such as collagen [7] or certain DNA sequences [8] (Fig 1). The variation in the SWNT wrapping controls the adsorption rates of different analytes present. For example, collagen-SWNTs have shown different, selective time-profiles of adsorption and desorption events for H2O2, H+, and Fe(CN)63− in different concentration ranges [6]. Importantly, all time-profile data had reversible features, which indicate adsorption and desorption rates of similar magnitudes.


State Estimation of the Time-Varying and Spatially Localized Concentration of Signal Molecules from the Stochastic Adsorption Dynamics on the Carbon Nanotube-Based Sensors and Its Application to Tumor Cell Detection.

Jang H, Lee JH, Braatz RD - PLoS ONE (2015)

An example of a SWNT-based sensor array system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631460&req=5

pone.0141930.g001: An example of a SWNT-based sensor array system.
Mentions: The basic mechanism of SWNT-based sensors is optical detection of discretized light intensity changes induced by adsorption and desorption of target molecules on the sensor’s surface at nano-scale. To enhance the sensitivity and selectivity for target molecules, usually present at the micromolar (μM) concentration level, the SWNT surface is functionalized by wrapping the nanotube with various polymers such as collagen [7] or certain DNA sequences [8] (Fig 1). The variation in the SWNT wrapping controls the adsorption rates of different analytes present. For example, collagen-SWNTs have shown different, selective time-profiles of adsorption and desorption events for H2O2, H+, and Fe(CN)63− in different concentration ranges [6]. Importantly, all time-profile data had reversible features, which indicate adsorption and desorption rates of similar magnitudes.

Bottom Line: Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules.State estimators are proposed for these types of sensors that fully address their stochastic nature.In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.

ABSTRACT
This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.

No MeSH data available.


Related in: MedlinePlus