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.

年龄依赖性衰老是植物生命中多方面且高度协调的发育阶段，表现为遗传、生化和表型连续体。因此，阐明分子事件的动态网络建模和模拟，特别是衰老开始期间的基因调控网络至关重要。在这里，我们构建了一个计算管道，将衰老相关的共表达网络与转录因子 (TF)-启动子关系和 microRNA (miR)-靶标相互作用相结合。网络结构和功能分析揭示了这些共表达网络每个模块内的重要节点。随后，我们使用时程基因表达数据集推断出叶片衰老中重要的动态转录调控模型。动态模拟和预测网络扰动分析以及实验数据集说明了 TF 及其下游目标之间的动力学关系。总之，我们的网络科学框架发现了 TF 群及其在叶子衰老中具有先前未被识别的作用的路径，并提供了动态转录电路的全面景观。