ArticlePloS one2015
A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- Modeling Floral Induction in the Narrow-Leafed LupinPlants (Basel, Switzerland) · 2024Article
- Modeling the Flowering Activation Motif during Vernalization in Legumes: A Case Study ofLife (Basel, Switzerland) · 2023Article
- Pseudanthia in angiosperms: a review.Annals of botany · 2023Review
- The flowering transition pathways converge into a complex gene regulatory network that underlies the phase changes of the shoot apical meristem inFrontiers in plant science · 2022Article
- Two alternative splicing variants of a wheat geneFrontiers in plant science · 2022Article
- Beyond the Genetic Pathways, Flowering Regulation Complexity inInternational journal of molecular sciences · 2021Review
- Genetic architecture and adaptation of flowering time among environments.The New phytologist · 2021Article
- Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering inFrontiers in genetics · 2021Article
- Article
- Effect of epistasis and environment on flowering time in barley reveals a novel flowering-delaying QTL allele.Journal of experimental botany · 2020Article
- Simulation-assisted machine learning.Bioinformatics (Oxford, England) · 2019Article
- Genetic inhibition of flowering differs between juvenile and adult Citrus trees.Annals of botany · 2019Article
- Deterministic and Stochastic Models of Arabidopsis thaliana Flowering.Bulletin of mathematical biology · 2019Article
- Reduction of the geomagnetic field delays Arabidopsis thaliana flowering time through downregulation of flowering-related genes.Bioelectromagnetics · 2018Article
- Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From theFrontiers in genetics · 2018Article
- WRKY23 is a component of the transcriptional network mediating auxin feedback on PIN polarity.PLoS genetics · 2018Article
- Inferring transcriptional logic from multiple dynamic experiments.Bioinformatics (Oxford, England) · 2017Article
- The logic of the floral transition: Reverse-engineering the switch controlling the identity of lateral organs.PLoS computational biology · 2017Article
- A plant-based chemical genomics screen for the identification of flowering inducers.Plant methods · 2017Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.
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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.