Artificial Neural Network Study of Whole-Cell Bacterial Bioreporter Response Determined Using Fluorescence Flow Cytometry

详细信息    查看全文

• 作者：Sirisha Busam ; Maia McNabb ; Anke Wackwitz ; Wasana Senevirathna ; Siham Beggah ; Jan Roelof van der Meer ; Mona Wells ; Uta Breuer ; Hauke Harms
• 刊名：Analytical Chemistry
• 出版年：2007
• 出版时间：December 1, 2007
• 年：2007
• 卷：79
• 期：23
• 页码：9107 - 9114
• 全文大小：204K
• 年卷期：v.79,no.23(December 1, 2007)
• ISSN：1520-6882

文摘

Genetically engineered bioreporters are an excellentcomplement to traditional methods of chemical analysis.The application of fluorescence flow cytometry to detectionof bioreporter response enables rapid and efficient characterization of bacterial bioreporter population responseon a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flowcytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptiveclustering approach (a single-layer perceptron model).Results for this approach are far superior to othermethods 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, thushaving potential for future development of high-throughput screening applications.