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.
Keywords: Computational neural networks; Microfluidics; Optimization; Receptor-ligand binding.
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