  Single-cell gene expression measurements offer opportunities in deriving
mechanistic understanding of complex diseases, including cancer. However, due
to the complex regulatory machinery of the cell, gene regulatory network (GRN)
model inference based on such data still manifests significant uncertainty. The
goal of this paper is to develop optimal classification of single-cell
trajectories accounting for potential model uncertainty. Partially-observed
Boolean dynamical systems (POBDS) are used for modeling gene regulatory
networks observed through noisy gene-expression data. We derive the exact
optimal Bayesian classifier (OBC) for binary classification of single-cell
trajectories. The application of the OBC becomes impractical for large GRNs,
due to computational and memory requirements. To address this, we introduce a
particle-based single-cell classification method that is highly scalable for
large GRNs with much lower complexity than the optimal solution. The
performance of the proposed particle-based method is demonstrated through
numerical experiments using a POBDS model of the well-known T-cell large
granular lymphocyte (T-LGL) leukemia network with noisy time-series
gene-expression data.
