This article is part of the supplement: Highlights from the 2nd IEEE Symposium on Biological Data Visualization

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iVUN: interactive Visualization of Uncertain biochemical reaction Networks

Corinna Vehlow1*, Jan Hasenauer23*, Andrei Kramer4, Andreas Raue25, Sabine Hug23, Jens Timmer567, Nicole Radde4, Fabian J Theis23 and Daniel Weiskopf1

Author Affiliations

1 Visualization Research Center (VISUS), University of Stuttgart, Allmandring 19, 70569 Stuttgart, Germany

2 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

3 Department of Mathematics, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany

4 Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70550 Stuttgart, Germany

5 Institute for Physics, University of Freiburg, Hermann-Herder Straße 3, 79104 Freiburg, Germany

6 Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstraße 19, 79104 Freiburg, Germany

7 BIOSS Centre for Biological Signalling Studies, University of Freiburg, Schänzlestraße 18, 79104 Freiburg, Germany

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BMC Bioinformatics 2013, 14(Suppl 19):S2  doi:10.1186/1471-2105-14-S19-S2

Published: 12 November 2013



Mathematical 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 results

We 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.


Our 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.