No description available.

Hair cell structures are one of the most common forms of sensing elements found in nature. In humans, approximately 16,000 auditory hair cells can be found in the cochlea of the ear. Each hair cell contains a stereocilia, which is the primary structure for sound transduction. This study looks to develop and characterize a bilayer lipid membrane (BLM) operated artificial hair cell sensor that resembles the stereocilia of the human ear. To develop this sensor, a flexible substrate with internal compartments for hosting the biomolecules and mating cap are constructed and experimentally characterized. The regulated attachment method (RAM) is used to form bilayers within the sealed device. Capacitance measurements of the encapsulated bilayer show that the sealing cap slightly compresses the bottom insert and reduces the size of the enclosed bilayer. Single channel measurements of alamethicin peptides further verify that the encapsulated device can be used to detect the gating activity of transmembrane proteins in the membrane. The flexible substrate was incorporated into a low-noise, portable test fixture. The response of the sensor and tip velocity of the hair were measured with respect to an impulse input on the test fixture and several frequency response functions (FRFs) were created. The FRF between the sensor and the tip velocity was used to show that the hair vibration was transmitted to the bilayer for certain hair lengths. The transfer function between the sensor and the input was used to show the effect of membrane potential on sensor response. / Master of Science

Whole-cell sensors represent an emerging branch in biosensor development since they obviate the need for enzyme/antibody purification and provide the unique opportunity to assess global parameters such as genotoxicity and bioavailability. Yeast species such as Saccharomyces cerevisiae are ideal hosts for whole-cell sensor applications. However, current approaches almost exclusively rely on analyte-induced expression of fluorescent proteins or luciferases that imply issues with light scattering and/or require the supply of additional substrates. In this study, the yeast α-factor mating pheromone, a peptide pheromone involved in cell-cell communication in Saccharomyces cerevisiae, was utilized to create the whole-cell sensor read-out signal, in particular by employing engineered sensor cells that couple the response to a user-defined environmental signal to α-factor secretion. Two novel immunoassays - relying on hydrophobin-based surface engineering - were developed to quantify the α-factor. Hydrophobins are amphiphilic fungal proteins that self-assemble into robust monolayers at hydrophobic surfaces. Two recombinant hydrophobins, either lacking (EAS) or exposing the α-factor pheromone (EAS-α) upon self-assembly, were used to functionalize polystyrene supports. In a first approach (competitive immunoassay), pheromone-specific antibodies initially bound to the functionalized surface (due to the α-factor exposed by the hydrophobin layer) were competitively detached by soluble α-factor. In a second approach, the antibodies were first premixed with pheromone-containing samples and subsequently applied to functionalized surfaces, allowing for the attachment of antibodies that still carried available binding sites (inverse immunoassay). Both immunoassays enabled quantitative assessment of the yeast pheromone in a unique but partially overlapping dynamic range and allowed for facile tuning of the assay sensitivity by adjustment of the EAS-α content of the hydrophobin layer. With a limit of detection of 0.1 nM α-factor, the inverse immunoassay proved to be the most sensitive pheromone quantification assay currently available. Due to the high stability of hydrophobin monolayers, functionalized surfaces could be reused for multiple consecutive measurements. Favorably, both immunoassays proved to be largely robust against the changes in the sample matrix composition, allowing for pheromone quantification in complex sample matrices such as yeast culture supernatants. Hence, these immunoassays could also be applied to study the pheromone secretion of wild-type and engineered Saccharomyces cerevisiae strains. Additionally, a proof-of-concept whole-cell sensor for thiamine was developed by combining the hydrophobin-based immunoassays with engineered sensor cells of Schizosaccharomyces pombe modulating the secretion of the α-factor pheromone in response to thiamine. Since this read-out strategy encompasses intrinsic signal amplification and enables flexible choice of the transducer element, it could contribute to the development of miniaturized, portable whole-cell sensors for on-site application.

Whole-cell sensors represent an emerging branch in biosensor development since they obviate the need for enzyme/antibody purification and provide the unique opportunity to assess global parameters such as genotoxicity and bioavailability. Yeast species such as Saccharomyces cerevisiae are ideal hosts for whole-cell sensor applications. However, current approaches almost exclusively rely on analyte-induced expression of fluorescent proteins or luciferases that imply issues with light scattering and/or require the supply of additional substrates. In this study, the yeast α-factor mating pheromone, a peptide pheromone involved in cell-cell communication in Saccharomyces cerevisiae, was utilized to create the whole-cell sensor read-out signal, in particular by employing engineered sensor cells that couple the response to a user-defined environmental signal to α-factor secretion. Two novel immunoassays - relying on hydrophobin-based surface engineering - were developed to quantify the α-factor. Hydrophobins are amphiphilic fungal proteins that self-assemble into robust monolayers at hydrophobic surfaces. Two recombinant hydrophobins, either lacking (EAS) or exposing the α-factor pheromone (EAS-α) upon self-assembly, were used to functionalize polystyrene supports. In a first approach (competitive immunoassay), pheromone-specific antibodies initially bound to the functionalized surface (due to the α-factor exposed by the hydrophobin layer) were competitively detached by soluble α-factor. In a second approach, the antibodies were first premixed with pheromone-containing samples and subsequently applied to functionalized surfaces, allowing for the attachment of antibodies that still carried available binding sites (inverse immunoassay). Both immunoassays enabled quantitative assessment of the yeast pheromone in a unique but partially overlapping dynamic range and allowed for facile tuning of the assay sensitivity by adjustment of the EAS-α content of the hydrophobin layer. With a limit of detection of 0.1 nM α-factor, the inverse immunoassay proved to be the most sensitive pheromone quantification assay currently available. Due to the high stability of hydrophobin monolayers, functionalized surfaces could be reused for multiple consecutive measurements. Favorably, both immunoassays proved to be largely robust against the changes in the sample matrix composition, allowing for pheromone quantification in complex sample matrices such as yeast culture supernatants. Hence, these immunoassays could also be applied to study the pheromone secretion of wild-type and engineered Saccharomyces cerevisiae strains. Additionally, a proof-of-concept whole-cell sensor for thiamine was developed by combining the hydrophobin-based immunoassays with engineered sensor cells of Schizosaccharomyces pombe modulating the secretion of the α-factor pheromone in response to thiamine. Since this read-out strategy encompasses intrinsic signal amplification and enables flexible choice of the transducer element, it could contribute to the development of miniaturized, portable whole-cell sensors for on-site application.

No description available.

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.