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Implementation of a genetically tuned neural platform in optimizing fluorescence from receptor–ligand binding interactions on microchips
Author(s) -
Alvarado Judith,
Hanrahan Grady,
Nguyen Huong T. H.,
Gomez Frank A.
Publication year - 2012
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.201200103
Subject(s) - microchannel , artificial neural network , biological system , microfluidics , fluorescence , channel (broadcasting) , computer science , materials science , chemistry , nanotechnology , artificial intelligence , physics , biology , telecommunications , optics
This paper describes the use of a genetically tuned neural network platform to optimize the fluorescence realized upon binding 5‐carboxyfluorescein‐D‐Ala‐D‐Ala‐D‐Ala (5‐FAM‐(D‐Ala) 3 ) ( 1 ) to the antibiotic teicoplanin from Actinoplanes teichomyceticus electrostatically attached to a microfluidic channel originally modified with 3‐aminopropyltriethoxysilane. Here, three parameters: (i) the length of time teicoplanin was in the microchannel; (ii) the length of time 1 was in the microchannel, thereby, in equilibrium with teicoplanin, and; (iii) the amount of time buffer was flushed through the microchannel to wash out any unbound 1 remaining in the channel, are examined at a constant concentration of 1 , with neural network methodology applied to optimize fluorescence. Optimal neural structure provided a best fit model, both for the training set ( r 2 = 0.985) and testing set ( r 2 = 0.967) data. Simulated results were experimentally validated demonstrating efficiency of the neural network approach and proved superior to the use of multiple linear regression and neural networks using standard back propagation.