Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Details

Serval ID
serval:BIB_37B3BDC1EC23
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.
Journal
Analytical Chemistry
Author(s)
Busam S., McNabb M., Wackwitz A., Senevirathna W., Beggah S., Meer J.R., Wells M., Breuer U., Harms H.
ISSN
0003-2700[print], 0003-2700[linking]
Publication state
Published
Issued date
2007
Volume
79
Number
23
Pages
9107-9114
Language
english
Abstract
Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.
Keywords
Algorithms, Bacteria/genetics, Flow Cytometry, Fluorescence, Neural Networks (Computer)
Pubmed
Web of science
Create date
01/02/2008 13:03
Last modification date
20/08/2019 13:26
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