Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization
Abstract
Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
J. Byška, T. Trautner, S. M. Marques, J. Damborský, B. Kozlíková, and M. Waldner, "Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization," Computer Graphics Forum, vol. 38, iss. 3, pp. 441-453, 2019. doi:10.1111/cgf.13701
[BibTeX]
Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
@article{Byska-2019-LongMolecularDynamicsSimulations,
author = {Byška, J. and Trautner, T. and Marques, S.M. and Damborský, J. and Kozlíková, B. and Waldner, M.},
title = {Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization},
journal = {Computer Graphics Forum},
volume = {38},
number = {3},
pages = {441-453},
keywords = {CCS Concepts, Human-centered computing -- Scientific visualization; User centered design},
doi = {10.1111/cgf.13701},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13701},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13701},
abstract = {Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.},
year = {2019},
pdf = "pdfs/AnalysisOfLongMolecularDynamicsSimulationsUsingInteractiveFocusAndContextVisualization_Trautner.pdf",
images = "images/Byska-2019-LongMolecularDynamicsSimulations.png",
thumbnails = "images/Byska-2019-LongMolecularDynamicsSimulations.png"
}