Unsupervised In-Silico Modeling of Complex Biological Systems

The advent of high-throughput technologies and the resultant generation of data has increased the demand for data-driven analytics. However, a comprehensive and computationally efficient method for analyzing, understanding and managing the emergent behavior of complex biological systems using time-series data remains elusive. In this paper, we introduce a new computational framework and modeling formalism designed for unsupervised learning and model construction in high-throughput biological data applications. This framework uses an underlying Bayesian nonparametric model which effectively infers long-range temporal dependencies from heterogeneous data streams to produce grammatical rules used for real-time in-silico modeling, behavior recognition and prediction. We present initial results of unsupervised learning tasks using unlabeled live-cell imaging data from experiments performed on the Large Scale Digital Cell Analysis System (LSDCAS), namely cellular event classification and large-scale spatio-temporal behavior recognition.