Inference of genetic regulatory network for stem cell using single cells expression data

Single cell experimental studies provide an unprecedented opportunity to examine the heterogeneity of molecular processes in different cells. However, the reconstruction of a sequence of changes in molecular processes and development of regulatory networks using single cell data are still challenging problems in bioinformatics and systems biology. In this work we propose an integrated framework to infer genetic regulatory networks using single cell experimental data. We first use the Wanderlust algorithm to construct the pseudo-trajectory of gene expression activities. Due to noise in the expression data, a Gauss process regression method is employed to produce a smoothly trajectory. Our integrated approach includes both a top-down approach (i.e. the GENIE3 algorithm) to infer the network structure and a bottom-up approach (i.e. differential equation model) to reverse-engineering the regulatory network. Using the gene network of hematopoietic development in the mouse embryo as the test problem, we developed a dynamic model for a network of nine genes. Our results suggest that the proposed integrated framework is an effective approach to reconstruct regulatory networks from single cell data.