A Quantitative and Dynamic Model of the Arabidopsis Flowering Time Gene Regulatory Network

Felipe Leal Valentim,Simon Van Mourik,David Posé,Marco Busscher,Aalt D J Van Dijk,Richard G H Immink,Min C Kim,Roeland C H J Van Ham,Markus Schmid,Gerco C Angenent,Gabino F Sanchez-Perez,Jaap Molenaar,Hector Candela

doi:10.1371/journal.pone.0116973

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.

Highlights

Flowering at the right moment is crucial for the reproductive success of flowering plants
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
We aimed to obtain a mechanistic understanding of the Arabidopsis thaliana flowering time integration network, by investigating a core gene regulatory network composed of eight genes (Fig. 1): SHORT VEGETATIVE PHASE (SVP), FLOWERING LOCUS C (FLC), AGAMOUSLIKE 24 (AGL24), SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1), Fig 1

Summary

Introduction

Flowering at the right moment is crucial for the reproductive success of flowering plants. Quantitative aspects of flowering time changes upon perturbations of input signals cannot be understood by merely assessing qualitatively which interactions are present To this end, a quantitative model describing how different genes in the network regulate each other is needed. Other complex plant developmental processes have been subject to extensive modeling efforts [3] This includes processes such as the circadian clock [4,5,6,7], auxin signalling [8,9,10,11], photoperiod regulation of flowering time genes [12,13] and the development of floral organs [14,15,16,17], which all have been investigated in detail by computational models. These models enable to formalize biological knowledge and hypotheses, and, importantly, to investigate how various types of inputs are combined to give a quantitative readout

Objectives

Methods

Results

Conclusion