BLOOM-NET

The transition from vegetative to generative development is of fundamental agricultural importance, because flowers are essential for breeders and the ornamental industry and are prerequisites for fruit and seed formation, which form the major source of human and animal food. This importance has resulted in the floral transition being extensively studied, and most key regulators have been identified. The current challenge is to understand how these factors act together to integrate the various external and internal signals into one apparently simple output: the formation of flowers at the optimal time. The overall aim of this project is to define the complex regulatory network that acts in the shoot apical meristem (SAM) to regulate the time of the transition to flowering, and to model quantitative and dynamic changes in this network during the floral transition. For this purpose, we will focus on an important set of key regulators that belong to the MADS-box transcription factor family, and that function either as repressors or inducers of the floral transition. Sophisticated genomic tools, such as genome-wide Chromatin Immunoprecipitation (ChIP), high-throughput Illumina/Solexa sequencing, and Surface Plasmon Resonance (SPR), will be applied to generate comprehensive and quantitative data sets, concerning MADS-box protein expression levels, phenotypic output, in vivo target gene promoter occupancy and their protein-protein interaction affinities. Furthermore, the SAM transcriptome will be investigated during the very early stages of floral induction and correlated with the ChIP data. The generation of quantitative data will allow us to parameterize a mathematical model for floral timing, which will lead to novel hypotheses that subsequently can be tested and experimentally validated and refined. To this end, we have compiled a consortium comprising of four different wet-lab research groups and one bioinformatics group, who will collaborate closely to meet the objectives of the programme. The groups are either experts in flowering research, or pioneers in one of the proposed technologies. The incorporation of quantitative data into a regulatory network model will give an insight into general transcriptional control and in particular into the complex regulatory network behind floral timing. Furthermore, we foresee that the outcome will be of general interest and offer applications for breeders and plant growers in the future.

The overall aim of this project is to define the regulatory network that acts in the shoot apical meristem to regulate the timing of transition to flowering, and to model quantitative and dynamic changes in this network during the floral transition. Most proteins with major functions in this process have been elucidated by genetic analyses. Nevertheless, the precise interplay and temporal behaviour, as well as the characteristics of the components that underpin the robustness of this regulatory system, have not been defined. This information is essential to predict the effects of mutations or environmental changes on flowering time. We will focus on a sub-set of MADS box transcription factors shown to be the key regulators of the floral transition in Arabidopsis. The exact timing of flowering is regulated by a balance between these transcriptional activators and suppressors. Iterative cycles of lab experiments and modeling will give comprehensive and quantitative data sets. Quantitative information will be integrated in a mathematical model describing the regulatory steps controlling flowering time in the meristem. The following specific objectives are postulated: 1. Determine the concentrations of key-regulators (MADS proteins) in the SAM over time and link this to phenotypic output under standard and perturbed conditions. 2. Obtain comprehensive binding site data-sets (i.e. target genes) of the various MADS-factors using a novel ChIP-SEQ approach. 3. Obtain the temporal transcriptome from the meristem of wild-type (WT) and mutant plants at time points before and after the transition to reproductive development, and compare these data with the direct target gene data. 4. Determine the dimerization affinities of the MADS box TF molecules. 5. Predict target sites at the genome level and model target site binding affinity and MADS-factor dimerization affinities. 6. Integrate the quantitative and qualitative aspects into a mathematical model.