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Modelling membrane curvature generation using mechanics and machine learning
The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effective...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490339/ https://www.ncbi.nlm.nih.gov/pubmed/36128706 http://dx.doi.org/10.1098/rsif.2022.0448 |
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author | Malingen, S. A. Rangamani, P. |
author_facet | Malingen, S. A. Rangamani, P. |
author_sort | Malingen, S. A. |
collection | PubMed |
description | The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine-learning models. These models produced promising results, accurately classifying model behaviour and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change. |
format | Online Article Text |
id | pubmed-9490339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-94903392022-11-14 Modelling membrane curvature generation using mechanics and machine learning Malingen, S. A. Rangamani, P. J R Soc Interface Life Sciences–Physics interface The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine-learning models. These models produced promising results, accurately classifying model behaviour and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change. The Royal Society 2022-09-21 /pmc/articles/PMC9490339/ /pubmed/36128706 http://dx.doi.org/10.1098/rsif.2022.0448 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Physics interface Malingen, S. A. Rangamani, P. Modelling membrane curvature generation using mechanics and machine learning |
title | Modelling membrane curvature generation using mechanics and machine learning |
title_full | Modelling membrane curvature generation using mechanics and machine learning |
title_fullStr | Modelling membrane curvature generation using mechanics and machine learning |
title_full_unstemmed | Modelling membrane curvature generation using mechanics and machine learning |
title_short | Modelling membrane curvature generation using mechanics and machine learning |
title_sort | modelling membrane curvature generation using mechanics and machine learning |
topic | Life Sciences–Physics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490339/ https://www.ncbi.nlm.nih.gov/pubmed/36128706 http://dx.doi.org/10.1098/rsif.2022.0448 |
work_keys_str_mv | AT malingensa modellingmembranecurvaturegenerationusingmechanicsandmachinelearning AT rangamanip modellingmembranecurvaturegenerationusingmechanicsandmachinelearning |