Project Topic A better understanding of interactions between physiological, environmental, and system-wide biomarkers can significantly speed up the discovery of new drug targets for multifactorial diseases. Childhood asthma is a multifactorial disease consisting of different clinical phenotypes. Several phenotypes have been identified based on symptom expression, inflammation type or pathological characteristics. A uniform, integrated systems medicine approach, however, is needed to disentangle the complexity of the disease, and to identify phenotypes that are predictive of therapy response. In this proposal we will make use of a consortium of existing large population-based paediatric asthma cohorts. Many (bio)markers are already available in these cohorts. However, importantly in a selection of 100 children out of these cohorts we will set-up a completely phenotyped patient cohort in which all biomarkers of interest (including state-of-the art markers in exhaled breath, faeces and saliva) will be determined. We will make use of sophisticated statistical, computational and mathematical approaches to construct a model for the prediction of therapy response. This model will be validated in a second separate validation cohort (n=100) also selected within the existing cohorts. The goals of our consortium are: I) to develop new systems medicine algorithms integrating a broad range of system-wide measurements for identification and validation of markers in response to asthma therapy, II) to improve the prediction of disease-related phenotypes, and III) to identify novel candidate drug targets. Our approach will build on high-quality well-phenotyped cohorts already available to our consortium, and on recent advancements in systems (bio-) medicine and high-dimensional statistical modelling. If proof of principle is shown this study can be the start of a larger and more refined systems medicine approach, that will lead to new durable targets for difficult-to-treat asthma.