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An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries

Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represen...

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Autores principales: Lee, Christopher T., Laughlin, Justin G., Moody, John B., Amaro, Rommie E., McCammon, J. Andrew, Holst, Michael, Rangamani, Padmini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Biophysical Society 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063475/
https://www.ncbi.nlm.nih.gov/pubmed/32032503
http://dx.doi.org/10.1016/j.bpj.2019.11.3400
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author Lee, Christopher T.
Laughlin, Justin G.
Moody, John B.
Amaro, Rommie E.
McCammon, J. Andrew
Holst, Michael
Rangamani, Padmini
author_facet Lee, Christopher T.
Laughlin, Justin G.
Moody, John B.
Amaro, Rommie E.
McCammon, J. Andrew
Holst, Michael
Rangamani, Padmini
author_sort Lee, Christopher T.
collection PubMed
description Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.
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spelling pubmed-70634752020-10-10 An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries Lee, Christopher T. Laughlin, Justin G. Moody, John B. Amaro, Rommie E. McCammon, J. Andrew Holst, Michael Rangamani, Padmini Biophys J Computational Tool Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data. The Biophysical Society 2020-03-10 2020-01-22 /pmc/articles/PMC7063475/ /pubmed/32032503 http://dx.doi.org/10.1016/j.bpj.2019.11.3400 Text en © 2020 Biophysical Society. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Computational Tool
Lee, Christopher T.
Laughlin, Justin G.
Moody, John B.
Amaro, Rommie E.
McCammon, J. Andrew
Holst, Michael
Rangamani, Padmini
An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title_full An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title_fullStr An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title_full_unstemmed An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title_short An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
title_sort open-source mesh generation platform for biophysical modeling using realistic cellular geometries
topic Computational Tool
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063475/
https://www.ncbi.nlm.nih.gov/pubmed/32032503
http://dx.doi.org/10.1016/j.bpj.2019.11.3400
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