FSM-based model for spatio-temporal event recognition for HCS
Extraction of quantitative information about spatio-temporal events happening in cells is the key to understanding biological processes. In this paper we present a finite state machine (FSM)-based model for specification and identification of spatio-temporal events at the single-cell level. Cells are modeled as objects with specific attributes such as color, size, shape, etc., and events are modeled in terms of the specific values of attributes of participating objects along with the spatial relationships between these objects. Results for a time-lapse apoptosis screen are presented where the extra information provided by per cell-based analysis is used to compensate for experimental artifacts. The model is general and is applicable to other cell-based studies also.
HCS, spatio-temporal modeling, quantitative imaging
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