16 ERA-CAPS - Genes2Shape

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.

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, important as reproductive structures and amongst the best characterised systems in plant developmental biology.
It is widely accepted that regulatory molecular networks interfere with 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.
Building on our complementary experience in interdisciplinary research on plant development, we will therefore develop a tool, called the Computable Flower (CF), that permits (i) integration of data on geometry, gene expression and biomechanics on a 3D dynamic template of the growing flower bud and (ii) the user to explore, interpret and generate hypotheses based on data supported by mechanistic modelling approaches. The CF will be populated with existing or novel quantitative datasets coming from experimental and computational techniques. 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.
The CF will be disseminated via an interactive graphical web interface, 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.

The project will produce data with important long-term implications for basic and applied research. While classical breeding techniques are reaching their limits and GMO technologies have failed so far to provide new ideotypes at an extensive scale, genomic selection has raised new hopes. This strategy to predict phenotypes from genotypic data uses models based on both massive phenotypic analysis in the field and genotypic information. However, the number of variables that can be measured in the field remains limited and the level of phenotypes macroscopic. In the long term, breeding programs will require a profound and integrated knowledge of how the plant functions at multiple scales. This is precisely what the partners have set out to do within this project. The framework we propose should provide in the long run a significant step towards a new generation of multi-scale models required to predict phenotypes of specific genotypes in different conditions and develop better targeted breeding programmes. By producing novel experiments and modelling tools that exploit and combine complex data sets at multiple scales that will be made available for visualisation and analysis within a computable flower for the wider community, we aim for improved knowledge of plant biology to increase crop yields, exploiting and sharing datasets to achieve their maximum potential.