We present a prototype of the flood early warning system (EWS) developed within the UrbanFlood FP7 project. The system monitors sensor networks installed in flood defenses (dikes, dams, embankments, etc.), detects sensor signal abnormalities, calculates dike failure probability, and simulates possible scenarios of dike breaching and flood propagation. All the relevant information and simulation results are fed into an interactive decision support system that helps dike managers and city authorities to make informed decisions in case of emergency and in routine dike quality assessment. In addition to that, a Virtual Dike computational module has been developed for advanced research into dike stability and failure mechanisms, and for training the artificial intelligence module on signal parameters induced by dike instabilities. This paper describes the UrbanFlood EWS generic design and functionality, the computational workflow, the individual modules, their integration via the Common Information Space middleware, and the first results of EWS monitoring and performance benchmarks.

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PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

An application pull has occurred in biomedicine with the move to in silico studies, which augment in vivo and in vitro studies by simulating more details of biomedical processes. Using these simulated processes helps medical doctors make decisions by exploring different scenarios. Preoperative simulation and visualization of vascular surgery 3 and expert systems for drug ranking 4 are two examples of such processes. At the same time, a technology push is occurring in computing resources and data availability. 5 In the field of high-performance computing, as computing advanced from sequential to parallel to distributed, killer applications moved from mathematics to physics, chemistry, biology, and now to medicine. In addition, advances in Internet technology and grid computing 6 have made huge amounts of data available from sensors, experiments, and simulations.Still, significant computational, integration, collaboration, and interaction gaps exist between the observed application pull and the technology push. Bridging the gapsClosing the computational gap in systems biology requires constructing, integrating, and managing a plethora of models. A bottom-up, data-driven approachComputer science provides the language needed to study and understand complex multiscale, multiscience systems.ViroLab, a grid-based decision-support system, demonstrates how researchers can now study diseases from the DNA level all the way up to medical responses to treatment.

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening of patient virus data have led to sustained viral suppression and higher life expectancy in patients who have been infected with HIV. The sheer complexity of the disease stems from the multiscale and highly dynamic nature of the system under study. The complete cascade from genome, proteome, metabolome and physiome to health forms a multidimensional system that crosses many orders of magnitude in temporal and spatial scales. Understanding, quantifying and handling this complexity is one of the biggest challenges of our time, which requires a highly multidisciplinary approach. In order to supply researchers with an interactive framework and to provide the medical professional with appropriate tools and information for making a balanced and reliable clinical decision, we have developed 'ViroLab', a collaborative decision-support system (http://www.virolab.org/). ViroLab contains computational models that cover various spatial and temporal scales from atomic-level interactions in nanoseconds up to sociological interactions on the epidemiological level, spanning years of disease progression. ViroLab allows for personalized drug ranking. It is on trial in six hospitals and various virology and epidemiology laboratories across Europe.

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