Methods for short time series analysis of cell-based biosensor data

This paper describes two approaches for sensing changes in spiking cells wh en only a limited amount of spike data is available, i.e., dynamically cons tructed local expansion rates and spike area distributions. The two methods were tested on time series from cultured neuron cells that exhibit spiking both autonomously and in the presence of periodic stimulation. Our tested hypothesis was that minute concentrations of toxins could affect the local statistics of the dynamics. Short data sets having relatively few spikes we re generated from experiments on cells before and after being treated with a small concentration of channel blocker. In spontaneous spiking cells, loc al expansion rates show a sensitivity that correlates with channel concentr ation level, while stimulated cells show no such correlation. Spike area di stributions on the other hand showed measurable differences between control and treated conditions for both types of spiking, and a much higher degree of sensitivity. Because these methods are based on analysis of short time series analysis, they might provide novel means for cell drug and toxin det ection. Published by Elsevier Science B.V.