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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

Time plots of (a) the local concentration affected by the tumor cell activity and (b) associated stochastic adsorption data, generated by running KMC simulations.
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pone.0141930.g008: Time plots of (a) the local concentration affected by the tumor cell activity and (b) associated stochastic adsorption data, generated by running KMC simulations.

Mentions: Based on this data, stochastic adsorption/desorption profiles were generated from KMC simulation. The number of adsorption sites on the sensor is 10 and the length of each run is 4000 s with the sampling time interval of 1 s. The starting state is assumed to be a random integer less than 10 (partly occupied) and the kA and kD are assumed to be 100 M−1s−1 and 0.001 s−1. Eq (42) is used in the state estimation as the state model. At the ‘normal cell’ state, the local concentration is stable and affected only by low-level noise (). When the cell becomes a tumor cell, the local concentration becomes elevated by high-level noise (). The plots in Fig 8 show a representative concentration variation with the tumor cell activity for the time period from 2000 s to 4000 s and five different realizations of the associated time profile of the number of adsorbed molecules.


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)

Time plots of (a) the local concentration affected by the tumor cell activity and (b) associated stochastic adsorption data, generated by running KMC simulations.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0141930.g008: Time plots of (a) the local concentration affected by the tumor cell activity and (b) associated stochastic adsorption data, generated by running KMC simulations.
Mentions: Based on this data, stochastic adsorption/desorption profiles were generated from KMC simulation. The number of adsorption sites on the sensor is 10 and the length of each run is 4000 s with the sampling time interval of 1 s. The starting state is assumed to be a random integer less than 10 (partly occupied) and the kA and kD are assumed to be 100 M−1s−1 and 0.001 s−1. Eq (42) is used in the state estimation as the state model. At the ‘normal cell’ state, the local concentration is stable and affected only by low-level noise (). When the cell becomes a tumor cell, the local concentration becomes elevated by high-level noise (). The plots in Fig 8 show a representative concentration variation with the tumor cell activity for the time period from 2000 s to 4000 s and five different realizations of the associated time profile of the number of adsorbed molecules.

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