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Electrophoresis. 2012 Sep;33(17):2711-7. doi: 10.1002/elps.201200103.

Implementation of a genetically tuned neural platform in optimizing fluorescence from receptor-ligand binding interactions on microchips.

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Department of Chemistry and Biochemistry, California State University, Los Angeles, CA, USA.


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.

[Indexed for MEDLINE]

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