Dynamic network visualization illustrating complexity underlying human health & disease

To make our kickoff celebration in July 2014 even more fun, engaging and beautiful, we designed a network visualization to run in the background. This resulted in a dynamic, ever-changing, 3D network visualization symbolizing the importance of taking into account connections in a system at all levels. See the video to enjoy an outtake of the visualization sequence! Read on below the video for more on the idea and technology behind the visualization.

The underlying idea was to create a way to show the complexity behind human health and disease and at the same time hint to the underlying principle of our work: we should (and through smart analyses and visualization can!) embrace this complexity, uncover organization of complex systems and steer them to our benefit. For example, a disease is not simply explained by a list of associated genes or proteins. It is driven by networks that reflect how these proteins influence each other, how they link to other molecules, to other aspects of health, other levels of biological regulation, together defining a complex system. We help our clients to get a grip on this complexity, to find patterns in seemingly disconnected data and information, to extract meaningful out of complex networks, and eventually contribute to curing disease and improving health.

The visualization iterates over a list of diseases and for each disease slowly builds a network of known molecular mechanisms underlying that disease, based on a combined knowledgebase of molecular interactions and functional associations (see Data Sources). Firstly, proteins that are known to be associated directly to the disease, such as genetic associations or biomarkers, are added. Secondly, the network is extended by querying the knowledgebase for other proteins, miRNAs and diseases that link to multiple of the disease associated proteins. Looking at the different networks, you may notice that many diseases share molecules and mechanisms with other diseases, i.e. an organism is one big interconnected system. Yet, every network has a unique structure and if you look closely, you’ll see that this visualization already helps you to spot specific patterns such as key regulators (looking like many-legged spiders sitting on the network) or protein complexes (looking like tumbleweed)!

Data Sources

We integrated several public resources on disease and molecular interaction information to create this visualization:

Open Source Software

Several open source tools were combined to create this visualization:

  • Neo4j as graph database backend and to query shortest paths
  • UbiGraph for 3D dynamic graph visualization
  • netlib for network conversion, integration and storage, developed by Georg Summer, a PhD student supervised by us.