The human body contains myriads of components that range from biomolecules to cells, tissues and organs. Modern technologies allow us to profile these components at molecular resolution, generating massive amounts of data in biological and medical research. Interpreting this data, however, poses a critical challenge in both research and clinical practice. Our limitations in interpreting these massive data, in turn, limits our ability to exploit them for practical medical applications. These limitations are not only technical, but rather conceptual, as we lack understanding of the fundamental principles governing human biology across molecular, cellular, organ, and whole-body levels. 

The overarching ambition of the LBI-NetMed is to leverage network theory, machine learning and artificial intelligence to formulate a holistic view of the intricate cross-scale nature of human biology and to translate the gained insights to concrete medical impact ranging from diagnosis to treatment.

To achieve this, we will systematically:

1. characterize the architecture of the respective networks within and across levels of biological organization
2. dissect the impact of disease associated and therapeutic perturbations on these levels, and
3. convert the results to clinical applications. 

We will pursue three major research lines along the hierarchical biological organization from molecules to tissues, organs, the whole body system, and finally the population level. In parallel, a next generation data exploration platform will be developed aiming to open up entirely new ways of integrating and interpreting complex biomedical data. 

Major research questions that we will address are: 

1. What is the architecture within and across molecular, cellular, tissue and organ networks allowing for the emergence of complex biological functions? A deeper understanding of this question will allow us, for example, to more accurately diagnose genetic diseases and their pathobiological consequences. 
2. What are the signatures of disease associated or therapeutic perturbations on this network architecture? Answering this question will help us, for example, in monitoring patients or stratifying heterogeneous patient populations into clinically actionable groups. 
3. How can we predict the combined impact of independent network perturbations or their dynamics over time? These fundamental questions are at the heart of, for example, combination therapy or effective management of disease comorbidities.
4. How can we integrate and interpret diverse biomedical data to leverage their full potential? 

We will closely collaborate with clinical partners to translate, validate and implement our results on these fundamental questions into medical practice.