Developing new visual analysis methods to be integrated into simulation processes, focusing on the exploration of cell biological systems in space and time

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The analysis of large stochastic simulation data sets is a challenging task, especially when spatial and temporal dependencies have to be investigated at different levels of granularity, as is the case in analyzing cell biological systems. We approach this problem by means of Visual Analytics. Our main focus will be intertwining interactive visualization methods and discrete event simulation processes to allow for a sustained visual feedback and user control. Therefore, innovative strategies will be developed in both fields.Visualization: This includes the multi-level visualization of multi-variate data in space and time in combination with their provenance and quality.Simulation: This refers to model partitioning strategies and the combination and (re-) configuration of simulators induced automatically and by user interaction.These new strategies will help analyzing simulation data of cell biological systems in space and time, but also directly steering the simulation process, and thus the data generating process. Thereby, currently available approaches of Visual Analytics will be enriched by two important aspects:• Use of cell biological data originating from a discrete-event simulated system (drylab) in contrast to data produced by a real system (wet-lab)• Exploiting simulation methods as a new source for analytical reasoning