Training in Systems Biology Applied to Flowering

    Project: EU research project

    Project Details


    We will educate and train young scientists to apply an interdisciplinary systems-based approach to complex biological questions using plant reproduction as a model system. Systems approaches are not routinely taught, have been identified as an area requiring urgent PhD-level training in some European countries and require network-scale working practices. We have assembled a team with International research reputations in two key areas for the success of this project: half are experimental and half are computational/mathematical scientists. We will place 9 PhD students under the supervision of this team, focussed on this single biological problem. Students assigned to experimental groups will use advanced techniques to generate data for the computational groups. The computational students will inform, analyse, interpret and model the data and their models will be validated by the experimental groups. A series of laboratory placements will ensure a wide range of subject-specific training and exchanges between the experimental and computational groups will lead to a greater understanding at the interface of these disciplines. The integration of a team of PhD students into this project, so that they each see their contributions as essential and integral parts of the success of the strategy, combined with the supervison and co-ordinated discipline-specific and generic training schedule will produce a cohort of young scientists trained in systems biology. The core skills and approaches that will be instilled into the young scientists and the integration of industry into the project, will equip them to take a systems approach to any biological question and prepare them for a career in an academic or industrial environment. Scientific outcomes will include the use of advanced techniques to provide the quantity and quality of data required to model floral regulation, the use of computational approaches to generate predictive models and their experimental validation.
    Effective start/end date1/12/0930/11/13