Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1021/ac0713508
Title (Primary) Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry
Author Busam, S.; McNabb, M.; Wackwitz, A.; Senevirathna, W.; Beggah, S.; van der Meer, J.; Wells, M.; Breuer, U.; Harms, H.
Source Titel Analytical Chemistry
Year 2007
Department UMB
Volume 79
Issue 23
Page From 9107
Page To 9114
Language englisch
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 M, 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.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=1692
Busam, S., McNabb, M., Wackwitz, A., Senevirathna, W., Beggah, S., van der Meer, J., Wells, M., Breuer, U., Harms, H. (2007):
Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry
Anal. Chem. 79 (23), 9107 - 9114 10.1021/ac0713508