BackgroundMathematical models are nowadays widely used to describe biochemical reaction
networks. One of the main reasons for this is that models facilitate the
integration of a multitude of different data and data types using parameter
estimation. Thereby, models allow for a holistic understanding of biological
processes. However, due to measurement noise and the limited amount of data,
uncertainties in the model parameters should be considered when conclusions are
drawn from estimated model attributes, such as reaction fluxes or transient
dynamics of biological species.Methods and resultsWe developed the visual analytics system iVUN that supports
uncertainty-aware analysis of static and dynamic attributes of biochemical
reaction networks modeled by ordinary differential equations. The multivariate
graph of the network is visualized as a node-link diagram, and statistics of the
attributes are mapped to the color of nodes and links of the graph. In addition,
the graph view is linked with several views, such as line plots, scatter plots,
and correlation matrices, to support locating uncertainties and the analysis of
their time dependencies. As demonstration, we use iVUN to quantitatively
analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling.ConclusionOur case study showed that iVUN can be used to perform an in-depth study
of biochemical reaction networks, including attribute uncertainties, correlations
between these attributes and their uncertainties as well as the attribute
dynamics. In particular, the linking of different visualization options turned out
to be highly beneficial for the complex analysis tasks that come with the
biological systems as presented here.