Record link: 
http://hdl.handle.net/20.500.12708/81180
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Title: 
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
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Citation: 
Ulbrich, P., Waldner, M., Furmanova, K., Marques, S. M., Bednar, D., Kozlikova, B., & Byska, J. (2022). sMolBoxes: Dataflow Model for Molecular Dynamics Exploration. IEEE Transactions on Visualization and Computer Graphics, PP, 1–10. https://doi.org/10.1109/TVCG.2022.3209411
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Publisher DOI: 
10.1109/TVCG.2022.3209411
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Ulbrich, Pavol 
Waldner, Manuela 
Furmanova, Katarina 
Marques, Sérgio M. 
Bednar, David 
Kozlikova, Barbora 
Byska, Jan 
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Organisational Unit: 
E193-02 - Forschungsbereich Computer Graphics 
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Journal: 
IEEE Transactions on Visualization and Computer Graphics 
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ISSN: 
1077-2626
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Date (published): 
26-Sep-2022
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Number of Pages: 
10
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Publisher: 
Institute of Electrical and Electronics Engineers (IEEE)
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Peer reviewed: 
Yes
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Keywords: 
Molecular Dynamics; structure; node-based visualization; progressive analytics; proteins; Analytical models; Biological system modeling; Three-dimensional displays; Computational modeling; Task analysis; Animation
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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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Research Areas: 
Computer Science Foundations: 50%
Modeling and Simulation: 50%
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Science Branch: 
1020 - Informatik: 80%
3013 - Medizinische Biochemie, Humangenetik: 20%
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