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Visualization of Protein Dynamics and Systems Biology

  • Molecular dynamics simulations play a major role in biologica, pharmaceutcal und medical research. In particular, the analysis of protein properties in different solvents is of interest for many processes. The simulation produces large, time-dependent data sets - so-called trajectories. The visualization of these trajectories enables the interactive investigation of the simulated properties. Furthermore, the automated extraction of freatures from the raw data and the fitting visualization of these features is important. Here, the abstract illustration of solvent properties plays a major role. In order to ensure an interactive analysis and high-quality visualizations, parallel algorithms are particularly developed. These algorithms take advantage of the features of current multi-core CPUs and programmable GPUs.
  • In systems biology, correlations play an important role and visualizations are a great way to make these visible. Hence, the focus of this work lies in the visualization and the interactive exploration of data from this environment. The data to be visualized will be generated by in silico simulations. Special emphasis is placed on the development of methods, which are carried out on graphic processing units (GPUs). The parallel architecture of GPUs is of interest, because it has the potential to allow high speed-ups of computations.

    The aim of this work is to develop a mesoscopic simulation of selected intra-cellular and extra-cellular processes and visualizations which are able to represent the simulation results in meaningful ways. In particular, cellular signal transduction processes will be studied.
Signal concentration of a virtual cell
Signal concentration of a virtual cell
Microscopic-like image for comparison with wet lab experiment
Microscopic-like image for comparison with wet lab experiment



@inproceedings {Vehlow_2012_BioVis,
    author = {Vehlow, Corinna and Hasenauer, Jan and Kramer, Andrei and Heinrich, Julian and Radde, Nicole
              and Allg\"{o}wer, Frank and Weiskopf, Daniel},
    title = {Uncertainty-Aware Visual Analysis of Biochemical Reaction Networks},
    year = {2012},
    editor = {IEEE},
    booktitle = {IEEE Symposium on Biological Data Visualization},
    volume = {2012},
    pages = {91--98},
    doi = {10.1109/BioVis.2012.6378598},
    url = {}