Graph Models for Biological Pathway Visualization: State of the Art and Future Challenges
This work addresses visualization challenges for researchers in computational biology, but it is incremental as it builds on existing multilayer network concepts.
The paper tackles the gap between data modeling and visual representation in biological pathway visualization by proposing multilayer networks as a unified data structure, resulting in guidelines and challenges for future research.
The concept of multilayer networks has become recently integrated into complex systems modeling since it encapsulates a very general concept of complex relationships. Biological pathways are an example of complex real-world networks, where vertices represent biological entities, and edges indicate the underlying connectivity. For this reason, using multilayer networks to model biological knowledge allows us to formally cover essential properties and theories in the field, which also raises challenges in visualization. This is because, in the early days of pathway visualization research, only restricted types of graphs, such as simple graphs, clustered graphs, and others were adopted. In this paper, we revisit a heterogeneous definition of biological networks and aim to provide an overview to see the gaps between data modeling and visual representation. The contribution will, therefore, lie in providing guidelines and challenges of using multilayer networks as a unified data structure for the biological pathway visualization.