PAME: Plasmonic Assay Modeling Environment
- Published
- Accepted
- Subject Areas
- Computational Biology, Scientific Computing and Simulation, Software Engineering
- Keywords
- Assays, Bioengineering, Python, Modeling, Biosensing, Simulation, Software, Plasmonics, Nanoparticles, Fiberoptics, Thin Films
- Copyright
- © 2015 Hughes et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. PAME: Plasmonic Assay Modeling Environment. PeerJ PrePrints 3:e1085v1 https://doi.org/10.7287/peerj.preprints.1085v1
Abstract
Plasmonic assays are an important class of optical sensors that measure biomolecular interactions in real-time without the need for labeling agents, making them especially well-suited for clinical applications. Through the incorporation of nanoparticles and fiberoptics, these sensing systems have been successfully miniaturized and show great promise for in-situ probing and implantable devices, yet it remains challenging to derive meaningful, quantitative information from plasmonic responses. This is in part due to a lack of dedicated modeling tools, and therefore we introduce PAME, an open-source Python application for modeling plasmonic systems of bulk and nanoparticle-embedded metallic films. PAME combines aspects of thin-film solvers, nanomaterials and fiber-optics into an intuitive graphical interface. Some of PAME’s features include a simulation mode, a database of hundreds of materials, and an object-oriented framework for designing complex nanomaterials, such as a gold nanoparticles encased in a protein shell. An overview of PAME’s theory and design is presented, followed by example simulations of a fiberoptic refractometer, as well as protein binding to a multiplexed sensor composed of a mixed layer of gold and silver colloids. These results provide new insights into observed responses in reflectance biosensors.
Author Comment
This is a submission to PeerJ Computer Science for review.