Supporting the Integrated Visual Analysis of Input Parameters and Simulation Trajectories

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides of the same coin: Since the input parameters influence the simulation outcome, it is desirable to visualize and explore both in a combined manner. The main challenge posed by such an integrated visualization is the combinatorial explosion of possible parameter combinations. It leads to insurmountably high simulation runtimes and screen space requirements for their visualization. The Visual Analytics approach presented in this paper targets this issue by providing a visualization of a coarsely sampled subspace of the parameter space and its corresponding simulation outcome. In this visual representation, the analyst can identify regions for further drill-down and thus finer subsampling. We aid this identification by providing visual cues based on heterogeneity metrics. These indicate in which regions of the parameter space deviating behavior occurs at a more fine-grained scale and thus warrants further investigation and possible re-computation. We demonstrate our approach in the domain of systems biology by a visual analysis of a rule-based model of the canonical Wnt signaling pathway that plays a major role in embryonic development. In this case, the aim of the domain experts was to systematically explore the parameter space to determine those parameter configurations that match experimental data sufficiently well

Illustrative Informationsvisualisierung

Mit wachsender Größe von Daten wird es zunehmend schwerer, die in der Informationsvisualisierung erzeugte visuelle Repräsentation zu interpretieren und die relevanten Informationen adäquat darzustellen. Die Illustration beschäftigt sich seit längerem mit der Kommunikation wichtiger Bildinformationen. Das Ziel dieser Dissertation ist es deshalb, illustrative Verfahren in Techniken der Informationsvisualisierung zu integrieren - sowohl konzeptuell als auch in praktischer Anwendung. Als Ergebnis unterstützen die neu entwickelten Lösungsansätze die Kommunikation dargestellter Informationen

Heterogeneity-based Guidance for Exploring Multiscale Data in Systems Biology

In systems biology, analyzing simulation trajectories at multiple scales is a common approach when subtle, detailed behavior and fundamental, overall behavior of a modeled system are to be investigated at the same time. A variety of multiscale visualization techniques provide solutions to handle and depict data at different scales. Yet the mere existence of multiple scales does not necessarily imply the existence of additional information on each of them: Data on a more fine-grained scale may not always yield new details, but instead reflect the already known data from more coarse-grained scales – just at a higher resolution. Nevertheless, to be sure of this, all scales have to be explored. We address this issue by guiding the exploration of simulation trajectories according to information about the deviation of the data between subsequent scales. For this purpose, we apply different dissimilarity measures to the simulation data at subsequent scales to automatically discern heterogeneous regions that exhibit deviating behavior on more fine-grained scales. We mark these regions and display them alongside the actual data in a multiscale visualization. By doing so, our approach provides valuable visual cues on whether it is worthwhile to drill-down further into the multiscale data and if so, where additional information can be expected. Our approach is demonstrated by an exploratory walk-through of stochastic simulation results of a biochemical reaction network