Dataset Information

Bridging the Gap between RF and Optical Patch Antenna Analysis via the Cavity Model.

ABSTRACT: Although optical antennas with a variety of shapes and for a variety of applications have been proposed and studied, they are still in their infancy compared to their radio frequency (rf) counterparts. Optical antennas have mainly utilized the geometrical attributes of rf antennas rather than the analysis tools that have been the source of intuition for antenna engineers in rf. This study intends to narrow the gap of experience and intuition in the design of optical patch antennas by introducing an easy-to-understand and easy-to-implement analysis tool in rf, namely, the cavity model, into the optical regime. The importance of this approach is not only its simplicity in understanding and implementation but also its applicability to a broad class of patch antennas and, more importantly, its ability to provide the intuition needed to predict the outcome without going through the trial-and-error simulations with no or little intuitive guidance by the user.

SUBMITTER: Unal GS

PROVIDER: S-EPMC4629117 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Publications

Bridging the Gap between RF and Optical Patch Antenna Analysis via the Cavity Model.

Unal G S GS Aksun M I MI

Scientific reports 20151102

Although optical antennas with a variety of shapes and for a variety of applications have been proposed and studied, they are still in their infancy compared to their radio frequency (rf) counterparts. Optical antennas have mainly utilized the geometrical attributes of rf antennas rather than the analysis tools that have been the source of intuition for antenna engineers in rf. This study intends to narrow the gap of experience and intuition in the design of optical patch antennas by introducing ...[more]

PMID: 26522889

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Project description:BackgroundDespite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.ResultsWe developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.ConclusionsOur simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

Dataset Information

Bridging the Gap between RF and Optical Patch Antenna Analysis via the Cavity Model.

Publications

Bridging the Gap between RF and Optical Patch Antenna Analysis via the Cavity Model.

Similar Datasets

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