Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.
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.
Busam
Sirisha
S
Department of Chemistry, Tennessee Technological University, Cookeville, Tennessee 38505, USA.
McNabb
Maia
M
Wackwitz
Anke
A
Senevirathna
Wasana
W
Beggah
Siham
S
Meer
Jan Roelof van der
JR
Wells
Mona
M
Breuer
Uta
U
Harms
Hauke
H
D000465:Algorithms; D001419:Bacteria / Q000235:genetics*; D005434:Flow Cytometry; D005453:Fluorescence; D016571:Neural Networks, Computer