Open Access

Lea Gütebier

• A lot of research data has become available since the outbreak of the COVID-19 pandemic in 2019. Connecting this data is essential for the understanding of the SARS-CoV-2 virus and the fight against the pandemic. Amongst biological and biomedical research data, computational models targeting COVID-19 have been emerging and their number is growing constantly. They are a central part of the field of Systems Biology, which aims to understand the mechanisms and behaviour of biological systems. Model predictions help to understand the mechanisms of the novel coronavirus and the life-threatening disease it is causing. Both biomedical research data and modelling data regarding COVID-19 have previously been stored in separated domain-specific graph databases. MaSyMoS, short for Management System for Models and Simulations, is a graph database for storing simulation studies of biological and biochemical systems. The CovidGraph project integrates research data regarding COVID-19 and the coronavirus family from various data resources in a knowledge graph. In this thesis, we integrate simulation models from MaSyMoS, including models targeting COVID-19, into the CovidGraph. Therefore, we present a concept for the integration of simulation studies and the linkage through ontology terms and reference publications in the CovidGraph. Ultimately, we connect data from the field of systems biology and biomedical research data in a graph database.