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Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry
Biomembranes adopt varying morphologies that are vital to cellular functions. Many studies use computational modeling to understand how various mechanochemical factors contribute to membrane shape transformations. Compared with approximation-based methods (e.g., finite element method [FEM]), the cla...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495267/ https://www.ncbi.nlm.nih.gov/pubmed/36157269 http://dx.doi.org/10.1016/j.bpr.2022.100062 |
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author | Zhu, Cuncheng Lee, Christopher T. Rangamani, Padmini |
author_facet | Zhu, Cuncheng Lee, Christopher T. Rangamani, Padmini |
author_sort | Zhu, Cuncheng |
collection | PubMed |
description | Biomembranes adopt varying morphologies that are vital to cellular functions. Many studies use computational modeling to understand how various mechanochemical factors contribute to membrane shape transformations. Compared with approximation-based methods (e.g., finite element method [FEM]), the class of discrete mesh models offers greater flexibility to simulate complex physics and shapes in three dimensions; its formulation produces an efficient algorithm while maintaining coordinate-free geometric descriptions. However, ambiguities in geometric definitions in the discrete context have led to a lack of consensus on which discrete mesh model is theoretically and numerically optimal; a bijective relationship between the terms contributing to both the energy and forces from the discrete and smooth geometric theories remains to be established. We address this and present an extensible framework, Mem3DG, for modeling 3D mechanochemical dynamics of membranes based on discrete differential geometry (DDG) on triangulated meshes. The formalism of DDG resolves the inconsistency and provides a unifying perspective on how to relate the smooth and discrete energy and forces. To demonstrate, Mem3DG is used to model a sequence of examples with increasing mechanochemical complexity: recovering classical shape transformations such as 1) biconcave disk, dumbbell, and unduloid; and 2) spherical bud on spherical, flat-patch membrane; investigating how the coupling of membrane mechanics with protein mobility jointly affects phase and shape transformation. As high-resolution 3D imaging of membrane ultrastructure becomes more readily available, we envision Mem3DG to be applied as an end-to-end tool to simulate realistic cell geometry under user-specified mechanochemical conditions. |
format | Online Article Text |
id | pubmed-9495267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-94952672022-09-22 Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry Zhu, Cuncheng Lee, Christopher T. Rangamani, Padmini Biophys Rep (N Y) Article Biomembranes adopt varying morphologies that are vital to cellular functions. Many studies use computational modeling to understand how various mechanochemical factors contribute to membrane shape transformations. Compared with approximation-based methods (e.g., finite element method [FEM]), the class of discrete mesh models offers greater flexibility to simulate complex physics and shapes in three dimensions; its formulation produces an efficient algorithm while maintaining coordinate-free geometric descriptions. However, ambiguities in geometric definitions in the discrete context have led to a lack of consensus on which discrete mesh model is theoretically and numerically optimal; a bijective relationship between the terms contributing to both the energy and forces from the discrete and smooth geometric theories remains to be established. We address this and present an extensible framework, Mem3DG, for modeling 3D mechanochemical dynamics of membranes based on discrete differential geometry (DDG) on triangulated meshes. The formalism of DDG resolves the inconsistency and provides a unifying perspective on how to relate the smooth and discrete energy and forces. To demonstrate, Mem3DG is used to model a sequence of examples with increasing mechanochemical complexity: recovering classical shape transformations such as 1) biconcave disk, dumbbell, and unduloid; and 2) spherical bud on spherical, flat-patch membrane; investigating how the coupling of membrane mechanics with protein mobility jointly affects phase and shape transformation. As high-resolution 3D imaging of membrane ultrastructure becomes more readily available, we envision Mem3DG to be applied as an end-to-end tool to simulate realistic cell geometry under user-specified mechanochemical conditions. Elsevier 2022-06-15 /pmc/articles/PMC9495267/ /pubmed/36157269 http://dx.doi.org/10.1016/j.bpr.2022.100062 Text en © 2022 The Author(s) https://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 | Article Zhu, Cuncheng Lee, Christopher T. Rangamani, Padmini Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title | Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title_full | Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title_fullStr | Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title_full_unstemmed | Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title_short | Mem3DG: Modeling membrane mechanochemical dynamics in 3D using discrete differential geometry |
title_sort | mem3dg: modeling membrane mechanochemical dynamics in 3d using discrete differential geometry |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495267/ https://www.ncbi.nlm.nih.gov/pubmed/36157269 http://dx.doi.org/10.1016/j.bpr.2022.100062 |
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