Life science researchers have been adopting graph databases, such as Neo4j, instead of traditional relational or triple stores. A recent workshop in Berlin brought together researchers and practitioners to share their experiences with graph technology. The presentations covered various applications, including genome-, proteome-, pathway- and systems-biology model databases and interactions, drug development efforts, and plans for improving healthcare. Researchers discussed the benefits of using graph databases for managing systems biology models, comparing multiple metaproteomics samples, and exploring protein association databases. They also highlighted challenges in performing graph-similarity measures on XML-encoded simulation models. The workshop demonstrated how to query public linked data endpoints and integrate results into a Neo4j property graph. Graph databases are being used to normalize protein structures, improve healthcare outcomes, and support medical decision-making systems. Researchers emphasized the need for user-driven methods for graph exploration, intuitive graph explanation, and interactivity in graph exploration. The workshop also covered data modeling for systems medicine with Neo4j and integrating linked life science data sources into a graph model in Neo4j.