Epidemiology & Systems Biology

The local processes in the tissue surrounding endoprosthetic implants that then trigger systemic inflammation still need to be investigated. Wear and degradation products, reaching other organs via the bloodstream, are presumed to initiate local inflammation but also systemic effects. The resulting chronic low-grade inflammation is believed to play a crucial role in the pathogenesis of various systemic diseases. There are proven links between cell-mediated inflammation and the pathogenesis of atherosclerosis, cardiovascular events, chronic kidney disease, and sarcopenia. However, there is currently a lack of evidence-based epidemiological studies regarding the possible systemic effects of endoprosthetic implants. Large-scale cohort studies are needed to shed light on these processes from an epidemiological perspective. On the other hand, patients with common underlying immunomodulatory conditions such as periodontitis, which is considered a systemic disease rather than localized inflammation, might be more susceptible to the impact of local inflammatory processes in the periphery of joint endoprostheses. Apart from the large-scale cohort studies, omics analyses in combination with deep learning methods will be used to provide information about these processes and to identify possible pathways of periodontitis.

Immunological processes are characterized by numerous mediators with spatially and temporally dependent effects that form complex communication networks. With the help of computer models for molecular interactions, systems biology network models provide a platform for molecular interference analyses. They enable a better understanding of the underlying mechanisms and support the validation of hypotheses. At the SBI, we have developed the Atlas of Inflammation Resolution (AIR) as a web resource for the network-based analysis of inflammatory processes (https://air.bio.informatik.uni-rostock.de/). 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 furnish the AIR with systems biology models of the tissues, cells, and processes studied in SYLOBIO. Via 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.