Pavol Ulbrich, Manuela Waldner, Katarína Furmanová, Sergio M. Margues, David Bednář, Barbora Kozlikova, Jan Byska
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
IEEE Transactions on Visualization and Computer Graphics:1-10, October 2022. [arxiv]

Information

  • Publication Type: Journal Paper with Conference Talk
  • Workgroup(s)/Project(s): not specified
  • Date: October 2022
  • Date (from): October 2022
  • Date (to): October 2022
  • DOI: 10.1109/TVCG.2022.3209411
  • Event: IEEE VIS 2022
  • Journal: IEEE Transactions on Visualization and Computer Graphics
  • Lecturer: Pavol Ulbrich
  • Pages (from): 1
  • Pages (to): 10
  • Pages: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • Keywords: Molecular Dynamics, structure, node-based visualization, progressive analytics, proteins, Analytical models, Biological system modeling, Three-dimensional displays, Computational modeling, Task analysis, Animation

Abstract

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

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BibTeX

@article{sMolBoxes_2022,
  title =      "sMolBoxes: Dataflow Model for Molecular Dynamics Exploration",
  author =     "Pavol Ulbrich and Manuela Waldner and Katar\'{i}na 
               Furmanov\'{a} and Sergio M. Margues and David Bedn\'{a}\v{r}
               and Barbora Kozlikova and Jan Byska",
  year =       "2022",
  abstract =   "We present sMolBoxes, a dataflow representation for the
               exploration and analysis of long molecular dynamics (MD)
               simulations. When MD simulations reach millions of
               snapshots, a frame-by-frame observation is not feasible
               anymore. Thus, biochemists rely to a large extent only on
               quantitative analysis of geometric and physico-chemical
               properties. However, the usage of abstract methods to study
               inherently spatial data hinders the exploration and poses a
               considerable workload. sMolBoxes link quantitative analysis
               of a user-defined set of properties with interactive 3D
               visualizations. They enable visual explanations of molecular
               behaviors, which lead to an efficient discovery of
               biochemically significant parts of the MD simulation.
               sMolBoxes follow a node-based model for flexible definition,
               combination, and immediate evaluation of properties to be
               investigated. Progressive analytics enable fluid switching
               between multiple properties, which facilitates hypothesis
               generation. Each sMolBox provides quick insight to an
               observed property or function, available in more detail in
               the bigBox View. The case studies illustrate that even with
               relatively few sMolBoxes, it is possible to express complex
               analytical tasks, and their use in exploratory analysis is
               perceived as more efficient than traditional scripting-based
               methods.",
  month =      oct,
  doi =        "10.1109/TVCG.2022.3209411",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  pages =      "10",
  publisher =  "Institute of Electrical and Electronics Engineers (IEEE)",
  pages =      "1--10",
  keywords =   "Molecular Dynamics, structure, node-based visualization,
               progressive analytics, proteins, Analytical models,
               Biological system modeling, Three-dimensional displays,
               Computational modeling, Task analysis, Animation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/",
}