Dynamic modeling of transcriptional gene regulatory network uncovers distinct pathways during the onset of Arabidopsis leaf senescence

Dynamic modeling of transcriptional gene regulatory network and regulated pathways during Arabidopsis leaf senescence. a Computational pipeline for dynamic modeling of transcriptional gene regulatory network (GRN) and regulated pathways during the onset of Arabidopsis leaf senescence. Major components of this platform including expression data acquisition, co-expression analyses, transcription factor (TF)-targets and microRNA (miR)-target identification, dynamic transcriptional modeling, and network simulations are demonstrated in sequential steps. b A weighted co-expression network with weight？≥？0.75, which was constructed by weighted co-expression network analysis (WGCNA) containing 9,014 co-expressed (nodes) and 993,699 interactions (edges), representing the Senescence CoExpression NeTwork (SCENT) was visualized in cytoscape. Nodes in different colors represent diverse modules in SCENT. 1,473 nodes with？≥？500 connections (Hub500) are spread throughout different modules. c The information centrality (IC) distributions, path length structural centrality measure, of seven modules and SCENT revealed that darkolivegreen, greenyellow and violet modules have highest IC values. The Student's t-test (p-value？<？2.2e？16) was applied to test the significance of ICs of all modules against SCENT. d Reconstructed responsive dynamic regulatory events for senescence network were modeled by SMARTS (Scalable Models for the Analysis of Regulation from Time Series). Modeled TF-targets and miR-target gene interaction, senescence gene expression and miRs expression values were utilized for event mining. The colored lines represent clustered genes based on their expression patterns. The green nodes denote splits between sets of genes that are regulated mutually until a particular interval. Forty-two different paths modeled by 18 different groups of TFs and several miRs are demonstrated as bifurcations. The dynamic activated pathways regulated by TFs and miRs were generated by TAIR Gene Ontology function, represented by a particular color with path numbers. The significance threshold was set to 0.032 for TFs and 0.10 for miRNAs regulatio