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3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries.

Authors :
Christopher T Lee
Justin G Laughlin
Nils Angliviel de La Beaumelle
Rommie E Amaro
J Andrew McCammon
Ravi Ramamoorthi
Michael Holst
Padmini Rangamani
Source :
PLoS Computational Biology, Vol 16, Iss 4, p e1007756 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
16
Issue :
4
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.517c7c181edc4062a0f9f0441ddc7cdb
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1007756