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Automembrane

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Estimation of forces induced on the membrane by ActuAtor

This package contains a tool automembrane which employs automatic differentiation to compute the energy and forces of the membrane given a 2D discrete shape profile. We apply the tool to a dataset where ActuAtor is used to deform the nuclear membrane. A preprint of this work is available on the bioRxiv.

Local directory structure

ActuAtorForceEstimation
├── actuator_example    ->  Code for application to ActuAtor
│   ├── coordinates     ->  Initial segmentations
│   ├── crop_images     ->  Cropped images
│   └── raw_images      ->  Raw EM images
├── automembrane        ->  Source for automembrane tool
│   └── tests
├── devtools
│   └── conda-envs
└── examples

Installing automembrane and running the example

After cloning this repository, automembrane can be installed by running pip install . from the root folder.

Examples can be found in the actuator_example folder. The raw/cropped EM images and segmentations are in the corresponding subfolders. There are several python files which perform the analysis and work.

actuator_constants.py   -> Contains mappings for file locations and EM image metadata
make_movie.py           -> Helper for making movies

main_sweep.py       -> Main driver function which runs relaxation, parameter variation and plotting

The following should be run in order:
variation_relax.py  -> Code for relaxation of initial segmentented geometries
variation_run.py    -> Runs parameter variation on the relaxed geometries
variation_view.py   -> Generated plots and movies

If you are short on time, you can simply run main_sweep.py which will perform geometric relaxation, parameter variation, and plotting for you automatically. It calls functions from the three variations_*.py files which do the actual work.

For a guided approach, there are also two jupyter notebooks which discuss the theory behind the work. You can also execute these to get a feel for what is happening in the automatic scripts.

Copyright

Copyright (c) 2022-2023, Eleanor Jung, Cuncheng Zhu, Christopher T. Lee, and Padmini Rangamani

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.6.