+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Application of a computational neural network to optimize the fluorescence signal from a receptor-ligand interaction on a microfluidic chip

Application of a computational neural network to optimize the fluorescence signal from a receptor-ligand interaction on a microfluidic chip

Electrophoresis 36(3): 393-397

We describe the use of a computational neural network platform to optimize the fluorescence upon binding 5-carboxyfluorescein-d-Ala-d-Ala-d-Ala (5-FAM(DA)3 ) (1) to the antibiotic teicoplanin covalently attached to a glass slide. A three-level response surface experimental design was used as the first stage of investigation. Subsequently, three defined experimental parameters were examined by the neural network approach: (i) the concentration of teicoplanin used to derivatize a glass platform on the microfluidic device, (ii) the time required for the immobilization of teicoplanin on the platform, and (iii) the length of time 1 is allowed to equilibrate with teicoplanin in the microfluidic channel. Optimal neural structure provided a best fit model, both for the training set (r(2) = 0.961) and test set (r(2) = 0.934) data. Model simulated results were experimentally validated with excellent agreement (% difference) between experimental and predicted fluorescence shown, thus demonstrating efficiency of the neural network approach.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 051607174

Download citation: RISBibTeXText

PMID: 25100638

DOI: 10.1002/elps.201400288

Related references

Structuring a multi-nodal neural network in vitro within a novel design microfluidic chip. Biomedical Microdevices 20(1): 9, 2018

Nanoscale diameter control of sensory polydiacetylene nanoparticles on microfluidic chip for enhanced fluorescence signal. Sensors and Actuators B: Chemical 230: 623-629, 2016

Squeeze-chip: a finger-controlled microfluidic flow network device and its application to biochemical assays. Lab on a Chip 12(9): 1587-1590, 2012

Studying protein-drug interaction by microfluidic chip affinity capillary electrophoresis with indirect laser-induced fluorescence detection. Electrophoresis 27(15): 3125-3128, 2006

Surface biopassivation of replicated poly(dimethylsiloxane) microfluidic channels and application to heterogeneous immunoreaction with on-chip fluorescence detection. Analytical Chemistry 73(17): 4181-4189, 2001

Two-way communication between ex vivo tissues on a microfluidic chip: application to tumor-lymph node interaction. Lab on a Chip 19(6): 1013-1026, 2019

Application of the Doehlert design to optimize the signal obtained in photochemically induced fluorescence for the determination of eight phenylureas. Journal of Fluorescence 18(2): 365-373, 2008

A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction dataNeural network for modelling spatial interaction data. Annals of Regional Science 32(3): 437-458, 1998

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics 35(7): 1098-1107, 2019

Interaction of the sigma(2) receptor ligand PB28 with the human nucleosome: computational and experimental probes of interaction with the H2A/H2B dimer. Chemmedchem 5(2): 268-273, 2010

Application of the ANNA neural network chip to high-speed character recognition. IEEE Transactions on Neural Networks 3(3): 498-505, 1992

Development of microfluidic chips for heterogeneous receptor-ligand interaction studies. Analytical Chemistry 81(12): 5095-5098, 2009

The artificial neural network approach based on uniform design to optimize the fed-batch fermentation condition: application to the production of iturin A. Microbial Cell Factories 13(1): 54, 2014

Fish inspection system using a parallel neural network chip and the image knowledge builder application. Ai Magazine 29(1): 21-28, 2008

Artificial neural network - Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones. Food Chemistry 227: 264-270, 2017