Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models

Ngan Nguyen, Ondrej Strnad, Tobias Klein, Ruwayda Alharbi, Peter Wonka, Martina Maritan, Peter Mindek, Ludovic Autin, David Goodsell, Ivan Viola
Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models
IEEE Transactions on Visualization and Computer Graphics, 2020.

Information

  • Publication Type: Journal Paper with Conference Talk
  • Workgroup(s)/Project(s):
  • Date: 2020
  • Date (from): 2020
  • Date (to): 2020 (to appear)
  • Event: IEEE VIS 2020
  • Journal: IEEE Transactions on Visualization and Computer Graphics

Abstract

We present a new technique for rapid modeling and construction of scientifically accurate mesoscale biological models. Resulting 3D models are based on few 2D microscopy scans and the latest knowledge about the biological entity represented as a set of geometric relationships. Our new technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we learn statistical properties of various structural aspects, such as the outer membrane shape, spatial properties and distribution characteristics of the macromolecular elements on the membrane. This information is utilized in 3D model construction. Once all imaging evidence is incorporated in the model, additional information can be incorporated by interactively defining rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, their distances and orientations to other structures. These rules are defined through an intuitive 3D interactive visualization and modeling feedback loop. We demonstrate the utility of our approach on a use case of the modeling procedure of the SARS-CoV-2 virus particle ultrastructure. Its first complete atomistic model, which we present here, can steer biological research to new promising directions in fighting spread of the virus.

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BibTeX

@article{nguyen_2020-covid,
  title =      "Modeling in the Time of COVID-19: Statistical and Rule-based
               Mesoscale Models",
  author =     "Ngan Nguyen and Ondrej Strnad and Tobias Klein and Ruwayda
               Alharbi and Peter Wonka and Martina Maritan and Peter Mindek
               and Ludovic Autin and David Goodsell and Ivan Viola",
  year =       "2020",
  abstract =   "We present a new technique for rapid modeling and
               construction of scientifically accurate mesoscale biological
               models. Resulting 3D models are based on few 2D microscopy
               scans and the latest knowledge about the biological entity
               represented as a set of geometric relationships. Our new
               technique is based on statistical and rule-based modeling
               approaches that are rapid to author, fast to construct, and
               easy to revise. From a few 2D microscopy scans, we learn
               statistical properties of various structural aspects, such
               as the outer membrane shape, spatial properties and
               distribution characteristics of the macromolecular elements
               on the membrane. This information is utilized in 3D model
               construction. Once all imaging evidence is incorporated in
               the model, additional information can be incorporated by
               interactively defining rules that spatially characterize the
               rest of the biological entity, such as mutual interactions
               among macromolecules, their distances and orientations to
               other structures. These rules are defined through an
               intuitive 3D interactive visualization and modeling feedback
               loop. We demonstrate the utility of our approach on a use
               case of the modeling procedure of the SARS-CoV-2 virus
               particle ultrastructure. Its first complete atomistic model,
               which we present here, can steer biological research to new
               promising directions in fighting spread of the virus.",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  URL =        "/research/publications/2020/nguyen_2020-covid/",
}