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Application of a computational neural network to optimize the fluorescence signal from a receptor–ligand interaction on a microfluidic chip
Author(s) -
Ortega Maria,
Hanrahan Grady,
Arceo Marilyn,
Gomez Frank A.
Publication year - 2015
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201400288
Subject(s) - microfluidics , signal (programming language) , ligand (biochemistry) , microfluidic chip , fluorescence , artificial neural network , nanotechnology , computer science , biological system , chip , chemistry , computational biology , receptor , biophysics , biology , materials science , physics , artificial intelligence , biochemistry , telecommunications , quantum mechanics , programming language
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.