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

Abstract

Age-dependent senescence is a multifaceted and highly coordinated developmental phase in the life of plants that is manifested with genetic, biochemical and phenotypic continuum. Thus, elucidating the dynamic network modeling and simulation of molecular events, in particular gene regulatory network during the onset of senescence is essential. Here, we constructed a computational pipeline that integrates senescence-related co-expression networks with transcription factor (TF)-promoter relationships and microRNA (miR)-target interactions. Network structural and functional analyses revealed important nodes within each module of these co-expression networks. Subsequently, we inferred significant dynamic transcriptional regulatory models in leaf senescence using time-course gene expression datasets. Dynamic simulations and predictive network perturbation analyses followed by experimental dataset illustrated the kinetic relationships among TFs and their downstream targets. In conclusion, our network science framework discovers cohorts of TFs and their paths with previously unrecognized roles in leaf senescence and provides a comprehensive landscape of dynamic transcriptional circuitry.