A Quantitative and Dynamic Model of the Arabidopsis Flowering Time Gene Regulatory Network
| Autor: | Min C. Kim, Markus Schmid, Gerco C. Angenent, Richard G. H. Immink, Jaap Molenaar, Aalt D. J. van Dijk, Roeland C. H. J. van Ham, Simon van Mourik, Gabino F. Sanchez-Perez, David Posé, Marco Busscher, Felipe Leal Valentim |
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| Rok vydání: | 2015 |
| Předmět: |
0106 biological sciences
Arabidopsis Gene regulatory network lcsh:Medicine Farm Technology Wiskundige en Statistische Methoden - Biometris 01 natural sciences floral transition Gene Expression Regulation Plant feedback loops Arabidopsis thaliana Gene Regulatory Networks lcsh:Science induction Regulator gene Regulation of gene expression 0303 health sciences Multidisciplinary signals food and beverages Laboratory of Molecular Biology DNA microarray Research Article ft protein Bioinformatics MADS Domain Proteins Flowers Computational biology Biology BIOS Applied Bioinformatics 03 medical and health sciences soc1 Bioinformatica expression Botany Laboratorium voor Moleculaire Biologie thaliana BIOS Plant Development Systems Mathematical and Statistical Methods - Biometris Gene Leafy 030304 developmental biology Models Genetic Arabidopsis Proteins lcsh:R fungi biology.organism_classification transport Agrarische Bedrijfstechnologie lcsh:Q leafy Transcription Factors 010606 plant biology & botany |
| Zdroj: | PLoS ONE 10 (2015) 2 PLoS ONE, Vol 10, Iss 2, p e0116973 (2015) PLoS ONE, 10(2) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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