Biomaterials serve as temporary or permanent replacements for tissue structures that are irreversibly damaged or functionally impaired. Possible incompatibility of biomaterials used for total joint replacements and dressings for chronic wounds is triggered by inflammatory reactions in the host tissue, which are characterized by cell type-specific intracellular and extracellular signaling pathways. These can lead to chronic foreign-body reactions. In addition, degradation products from the biomaterials can further enhance the foreign body reactions.

In this regard, the research training group GRK 2901/1 SYLOBIO funded by the German Research Foundation (DFG), analyses the local as well as the systemic response to these biomaterials. The central research question is whether the systemic reaction to biomaterials is either triggered by biological factors from the biomaterial environment or whether systemic exposure to degradation products leads to inflammation in organs/tissues distant from the biomaterial. In order to identify the complexity of material-induced inflammation and the patient-specific diversity of tissue response, a multidisciplinary approach is required to analyze systemic and local mechanisms of action in detail.

At the SBI, we have developed the Atlas of Inflammation Resolution (AIR) as a web resource for the network-based analysis of inflammatory processes. The AIR provides an interactive web platform with standardized representations of molecular interactions involved in over 40 immunological processes at different biological levels. In total, the AIR network comprises over 20,000 interactions between more than 6,000 biomolecular entities. The AIR has been extended with functions that enable network-based integration, linking, and simulation of data at multiple levels. In this project, we will extend the AIR with systems biology models of the tissues, cells, and processes studied in SYLOBIO. Using the provided functions of the AIR, we will integrate the data obtained in individual projects to enable the visualization of the experimental data in molecular networks. Finally, predictions from in silico simulations based on the network-integrated data will provide new insights into the molecular mechanisms and systematic effects of biomaterial interactions.