TY - JOUR T1 - An inference approach combines spatial and temporal gene expression data to predict gene regulatory networks in Arabidopsis stem cells JF - bioRxiv DO - 10.1101/140269 SP - 140269 AU - Maria Angels de Luis Balaguer AU - Adam P. Fisher AU - Natalie M. Clark AU - Maria Guadalupe Fernandez-Espinosa AU - Barbara K. Möller AU - Dolf Weijers AU - Jan U. Lohmann AU - Cranos Williams AU - Oscar Lorenzo AU - Rosangela Sozzani Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/19/140269.abstract N2 - Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is key for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. For this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network (GRN) inference algorithm that combines clustering with Dynamic Bayesian Network (DBN) inference. We leveraged the topology of our networks to infer potential key regulators. The results presented in this work show that our combination of molecular biology approaches, computational biology and mathematical modeling was key to identify candidate factors that function in the stem cells. Specifically, through experimental validation and mathematical modeling, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center (QC) function. ER -