ArticleNPJ systems biology and applications2018
Dynamic modeling of transcriptional gene regulatory network uncovers distinct pathways during the onset of Arabidopsis leaf senescence.
Article in NPJ systems biology and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Transcriptome meta-analysis of abiotic stresses-responsive genes and identification of candidate transcription factors for broad stress tolerance in wheat.Protoplasma · 2023Pooled it
- Integrative systems biology framework discovers common gene regulatory signatures in mechanistically distinct inflammatory skin diseases.NPJ systems biology and applications · 2025Article
- Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators.Plants (Basel, Switzerland) · 2023Article
- Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators inInternational journal of molecular sciences · 2023Article
- Cytotrap: An Innovative Approach for Protein-Protein Interaction Studies for Cytoplasmic Proteins.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Preparation and Utilization of a Versatile GFP-Protein Trap-Like System for Protein Complex Immunoprecipitation in Plants.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Dynamic Enrichment for Evaluation of Protein Networks (DEEPN): A High Throughput Yeast Two-Hybrid (Y2H) Protocol to Evaluate Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Protein-Protein Interaction Network Exploration Using Cytoscape.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Protein-Protein Interaction Network Analysis Using NetworkX.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Building Protein-Protein Interaction Graph Database Using Neo4j.Methods in molecular biology (Clifton, N.J.) · 2023Article
- An enhanced transcription factor repressilator that buffers stochasticity and entrains to an erratic external circadian signal.Frontiers in systems biology · 2023Article
- A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions.STAR protocols · 2022Article
- A genome-wide comparative evolutionary analysis of zinc finger-BED transcription factor genes in land plants.Scientific reports · 2022Article
- Specificity of HCellular & molecular biology letters · 2022Review
- A rice protein interaction network reveals high centrality nodes and candidate pathogen effector targets.Computational and structural biotechnology journal · 2022Article
- Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity.Scientific reports · 2021Article
- Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Dark-Induced Senescence Causes Localized Changes in DNA Methylation.Plant physiology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.