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Inferring membrane properties during clathrin-mediated endocytosis using machine learning

Zhiwei Lin, Zhiping Mao, View ORCID ProfileRui Ma
doi: https://doi.org/10.1101/2023.01.11.523591
Zhiwei Lin
1Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen, China
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Zhiping Mao
2School of Mathematical Sciences, Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing, Xiamen University, Xiamen, China
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  • For correspondence: zpmao@xmu.edu.cn
Rui Ma
1Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen, China
3Fujian Provincial Key Laboratory for Soft Functional Materials Research, Research Institute for Biomimetics and Soft Matter, Xiamen University, Xiamen, China
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  • For correspondence: ruima86@gmail.com
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Abstract

Endocytosis is a fundamental cellular process for eukaryotic cells to transport molecules into the cell. To understand the molecular mechanisms behind the process, researchers have obtained abundant biochemical information about the protein dynamics involved in endocytosis via fluorescence microscopy and geometric information about membrane shapes via electron tomography. However, measuring the biophysical information, such as the osmotic pressure and the membrane tension, remains a problem due to the small dimension of the endocytic invagination. In this work, we combine Machine Learning and Helfrich model of the membrane, as well as the dataset of membrane shapes extracted from the electron tomography to infer biophysical information about endocytosis. Our results show that Machine Learning is able to find solutions that both match the experimental profile and fulfill the membrane shape equations. Furthermore, we show that at the early stage of endocytosis, the inferred membrane tension is negative, which implies strong compressive forces acting at the boundary of the endocytic invagination. This method provides a generic framework to extract membrane information from the super-resolution imaging.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 13, 2023.
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Inferring membrane properties during clathrin-mediated endocytosis using machine learning
Zhiwei Lin, Zhiping Mao, Rui Ma
bioRxiv 2023.01.11.523591; doi: https://doi.org/10.1101/2023.01.11.523591
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Inferring membrane properties during clathrin-mediated endocytosis using machine learning
Zhiwei Lin, Zhiping Mao, Rui Ma
bioRxiv 2023.01.11.523591; doi: https://doi.org/10.1101/2023.01.11.523591

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