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. Because of its importance, the floral transition has been 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 near future.