Abstract
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.
Highlights
Mathematical models are nowadays widely used to describe biochemical reaction networks
We present our visual analytics system iVUN. iVUN supports an in-depth study of biochemical reaction network (BRN) with uncertain properties
We developed the visual analytics system iVUN, a JAVA based tool facilitating the interactive visualization of uncertain BRNs. iVUN has been developed in a participatory design process together with two target users
Summary
Mathematical models are nowadays widely used to describe biochemical reaction networks. 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. Biomolecules, such as genes, RNAs and proteins, are the building blocks of cells. To ensure reliability and predictive power of the BRN models, the unknown parameters have to be estimated from the available measurement data. Due to the limited availability of data and the ubiquity of measurement noise, the parameter estimation does in general not yield a unambiguous result, i.e., the parameters remain uncertain. There are various tools available that help simulating and visualizing BRN models, hardly any tool exists that supports the visual analysis of uncertainties in BRN models
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