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

voxelization Ultraliser in silico mesh reconstruction molecular simulations optical imaging simulations reaction–diffusion simulations ultrastructure watertight

Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
19 01 2023
Historique:
received: 02 08 2022
revised: 27 09 2022
accepted: 14 10 2022
pubmed: 27 11 2022
medline: 24 1 2023
entrez: 26 11 2022
Statut: ppublish

Résumé

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). 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.

Identifiants

pubmed: 36434788
pii: 6847753
doi: 10.1093/bib/bbac491
pmc: PMC9851302
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : King Abdullah University of Science and Technology
ID : OSR-2017-CRG6-3438
Organisme : Swiss Federal Institute of Technology Lausanne

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

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Auteurs

Marwan Abdellah (M)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Juan José García Cantero (JJG)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Nadir Román Guerrero (NR)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Alessandro Foni (A)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Jay S Coggan (JS)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Corrado Calì (C)

Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia.
Neuroscience Institute Cavalieri Ottolenghi (NICO) Orbassano, Italy.
Department of Neuroscience, University of Torino Torino, Italy.

Marco Agus (M)

Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia.
College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar.

Eleftherios Zisis (E)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Daniel Keller (D)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Markus Hadwiger (M)

Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia.

Pierre J Magistretti (PJ)

Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia.

Henry Markram (H)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

Felix Schürmann (F)

Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland.

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