Large-scale dynamic gene regulatory networks analysis for time course DNA microarray data from C. elegans, preliminary results and findings

This paper presents preliminary results and findings of a dynamic gene regulatory networks analysis obtained from Caenorhabditis elegans (C. elegans) time course DNA microarray data using a maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. High dimensionality and non-stationarity of the time course microarray data are two major challenges of time-varying GRNs analysis. The proposed method employs the L1-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. To process dataset which may contain extremely large amount of genes, the MAP-TVAR is extended to a distributed framework based on the concept similar to the spirit of Split Bregman method. Well-known interactions such as the eEF-1A.1 and RPL-12, can be identified by the MAP-TVAR approach. These interactions and their corresponding genes are found to be related in the embryo development process of C. elegans. These suggest that the MAP-TVAR approach may serve as a wonderful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.