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Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

 : Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolut...

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Detalles Bibliográficos
Autores principales: Abdellah, Marwan, Cantero, Juan José García, Guerrero, Nadir Román, Foni, Alessandro, Coggan, Jay S, Calì, Corrado, Agus, Marco, Zisis, Eleftherios, Keller, Daniel, Hadwiger, Markus, Magistretti, Pierre J, Markram, Henry, Schürmann, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851302/
https://www.ncbi.nlm.nih.gov/pubmed/36434788
http://dx.doi.org/10.1093/bib/bbac491
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author Abdellah, Marwan
Cantero, Juan José García
Guerrero, Nadir Román
Foni, Alessandro
Coggan, Jay S
Calì, Corrado
Agus, Marco
Zisis, Eleftherios
Keller, Daniel
Hadwiger, Markus
Magistretti, Pierre J
Markram, Henry
Schürmann, Felix
author_facet Abdellah, Marwan
Cantero, Juan José García
Guerrero, Nadir Román
Foni, Alessandro
Coggan, Jay S
Calì, Corrado
Agus, Marco
Zisis, Eleftherios
Keller, Daniel
Hadwiger, Markus
Magistretti, Pierre J
Markram, Henry
Schürmann, Felix
author_sort Abdellah, Marwan
collection PubMed
description  : Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure–function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE: There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.
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spelling pubmed-98513022023-01-20 Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience Abdellah, Marwan Cantero, Juan José García Guerrero, Nadir Román Foni, Alessandro Coggan, Jay S Calì, Corrado Agus, Marco Zisis, Eleftherios Keller, Daniel Hadwiger, Markus Magistretti, Pierre J Markram, Henry Schürmann, Felix Brief Bioinform Problem Solving Protocol  : Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure–function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE: There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations. Oxford University Press 2022-11-26 /pmc/articles/PMC9851302/ /pubmed/36434788 http://dx.doi.org/10.1093/bib/bbac491 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Problem Solving Protocol
Abdellah, Marwan
Cantero, Juan José García
Guerrero, Nadir Román
Foni, Alessandro
Coggan, Jay S
Calì, Corrado
Agus, Marco
Zisis, Eleftherios
Keller, Daniel
Hadwiger, Markus
Magistretti, Pierre J
Markram, Henry
Schürmann, Felix
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title_full Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title_fullStr Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title_full_unstemmed Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title_short Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
title_sort ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3d models for in silico neuroscience
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851302/
https://www.ncbi.nlm.nih.gov/pubmed/36434788
http://dx.doi.org/10.1093/bib/bbac491
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