Artificial Neural Network for Temporal Impedance Recognition of Neurotoxins

The design, development and in-vitro evaluation of an impedimetric neurotoxicity cell-based biosensor that is designed for real time monitoring of changes in electrophysiological behavior under the influence of neurotoxins is described. The electrical cell impedance sensing (ECIS) system [ECIS 8W1E element array of gold electrodes] is used as a substrate for the culture of rat pheochromocytoma (PC 12) cells. The neurotoxicity biosensor is a microfabricated solid state device that mimics the natural environment of PC 12 cells that are responsive to neurotoxins. The PC 12 neurotoxicity biosensors are complemented by artificial neural networks (ANNs) to recognize the impedance profiles of the cells under the influence of a neurotoxin. The neurotoxins were rotenone (Rot), okadaic acid (OA) and peroxynitrite (Per), which are all known to induce cell death in PC 12 cells. Three multilayer feedforward artificial neural network models were developed using a back-propagation algorithm for pattern recognition of neurotoxins. The neurotoxin network (NTN) and the neurotoxin concentration network (NTCN), were trained with data from all the neurotoxins and the cascade network (NTN_NTCN) was developed by combining both the NTN and NTCN. The cascade network was developed to screen against false positives. The neurotoxicity biosensor coupled with these networks allowed for the action of unknown agents (neurotoxins) to be deduced by the measured cellular response. Using back-propagation ANNs to distinguish neurotoxins under the cascade network, the highest success recognition rate for concentration identification were 96% for peroxynitrite, 88% for rotenone, and 96% for okadaic acid. The recognition rate for neurotoxin identification was 98%. The ANN models required less than ten minutes to train and demonstrated that back-propagation ANNs can be handled by commercially-available computers to train and assimilate neurotoxin impedance information, permitting high success rates in the neurotoxin recognition problems.