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

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About: Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models
Creator	Nguyen, Ngan Goodsell, David Alharbi, Ruwayda Autin, Ludovic Klein, Tobias »more»
source	ArXiv
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.
has issue date	2020-05-01(xsd:dateTime)
has license	arxiv
sha1sum (hex)	7bf2d8b9801b612da3ab73bae942dcedd7d27e7d
resource representing a document's title	Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models
resource representing a document's body	covid:7bf2d8b9801b612da3ab73bae942dcedd7d27e7d#body_text
is schema:about of	named entity 'RULE-BASED' named entity 'SARS-CoV-2' named entity 'membrane proteins' named entity 'randomly selected' named entity 'coronavirus' »more»

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