This project is aimed at understanding how molecular regulation integrates with mechanics to control overall plant shape, an unresolved problem with wide implications for both fundamental and applied biology. We will address this issue in the Arabidopsis flower, which, besides their obvious importance as reproductive structures, are amongst the best characterised systems in plant developmental biology. From a mechanistic point of view, it is widely accepted that regulatory molecular networks interfere with the properties of the structural cellular elements (cell wall, cytoskeleton) to induce particular growth patterns. How this occurs and how this is coordinated in space is not known. To obtain a mechanistic understanding of such a complex process, information from multiple scales, from molecular networks to physical properties and geometry have to be combined into a single picture. An integrated tool to do so is currently not available. Building on our complementary experience in interdisciplinary research on plant development, we will therefore develop a tool, called the Computable Flower that permits (i) integration of data on geometry, gene expression and biomechanics and (ii) the user to explore, interpret and generate hypotheses based on data supported by mechanistic modelling approaches. The tool therefore provides an integrated description in the form of a 3D dynamic template of the growing flower bud. The Computable Flower will be populated with existing or novel quantitative datasets coming from experimental and computational techniques concerning: (i) the spatial distribution of regulatory molecules such as transcription factors and hormones. (ii) the spatial expression patterns of genes involved in cell wall synthesis and remodelling which operate downstream from these regulatory networks. (iii) the spatial organisation and properties of structural elements, including cell wall stiffness, cytoskeleton and cellulose microfibril organisation. (iv) changes in geometry. In the process we will develop computational models to generate hypotheses regarding biochemical, physical and geometrical properties with simulation outcomes quantitatively compared with experimental data. Predictions coming from the modelling will guide experiments using domain-specific perturbation of genes that influence microtubule and wall status. These transgenic lines will then be subjected to detailed quantitative growth studies to test the validity of the model or to refine it. The above measured datasets and simulation outcomes will be disseminated via an interactive graphical web interface of the Computable Flower, transforming the way data is provided to the community by integrating multiple data types and allowing users to browse the data and build their experiments and models on the latest information and insights. Importantly, the tools generated to create the computable flower will be easily adaptable to a wide range of plant and animal systems.