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PLoS One. 2015 Feb 26;10(2):e0116973. doi: 10.1371/journal.pone.0116973. eCollection 2015.

A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network.

Author information

1
Bioscience, Plant Research International, Wageningen UR, Wageningen, The Netherlands.
2
Biometris, Wageningen UR, Wageningen, The Netherlands; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.
3
Max Planck Institute for Developmental Biology, Molecular Biology, Tübingen, Germany.
4
Bioscience, Plant Research International, Wageningen UR, Wageningen, The Netherlands; Chair group Bioinformatics, Wageningen University, Wageningen, The Netherlands.
5
Biometris, Wageningen UR, Wageningen, The Netherlands.
6
Bioscience, Plant Research International, Wageningen UR, Wageningen, The Netherlands; Laboratory of Molecular Biology, Wageningen University, Wageningen, The Netherlands.
7
Bioscience, Plant Research International, Wageningen UR, Wageningen, The Netherlands; Biometris, Wageningen UR, Wageningen, The Netherlands; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.

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.

PMID:
25719734
PMCID:
PMC4342252
DOI:
10.1371/journal.pone.0116973
[Indexed for MEDLINE]
Free PMC Article

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