Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations

Marwan Abdellah, Alessandro Foni, Eleftherios Zisis, Nadir Román Guerrero, Samuel Lapere, Jay S Coggan, Daniel Keller, Henry Markram, Felix Schürmann

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Abstract Motivation Astrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood–brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations. .
Original languageEnglish (US)
Pages (from-to)i426-i433
Number of pages1
JournalBioinformatics
Volume37
Issue numberSupplement_1
DOIs
StatePublished - Jul 12 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-08-10
Acknowledged KAUST grant number(s): OSR-2017-CRG6-3438
Acknowledgements: This work was supported by funding to the Blue Brain Project, a research center of the E´ cole polytechnique fe´de´rale de Lausanne (EPFL), from the Swiss Government’s ETH Board of the Swiss Federal Institutes of Technology and also supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2017-CRG6-3438.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications

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