https://github.com/smrfeld/physics-based-ml-reaction-networks
Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
https://github.com/smrfeld/physics-based-ml-reaction-networks
chemistry machine-learning physics-based physics-based-ml physics-informed physics-informed-ml reaction-networks reactions
Last synced: about 1 month ago
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Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
- Host: GitHub
- URL: https://github.com/smrfeld/physics-based-ml-reaction-networks
- Owner: smrfeld
- License: mit
- Created: 2021-05-23T23:01:21.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-29T18:56:40.000Z (almost 4 years ago)
- Last Synced: 2025-04-13T06:12:36.501Z (about 1 month ago)
- Topics: chemistry, machine-learning, physics-based, physics-based-ml, physics-informed, physics-informed-ml, reaction-networks, reactions
- Language: Mathematica
- Homepage:
- Size: 15.9 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Physics-based machine learning for modeling IP3 induced calcium oscillations
[](https://doi.org/10.5281/zenodo.4839127)
This repo. contains the code for the paper `Physics-based machine learning for modeling IP3 induced calcium oscillations`.
## Contents
* [learn_ip3](learn_ip3) - Models extrapolating in `IP3` for a single volume and number of `IP3Rs` (`IP3` receptors).
* [learn_ip3r](learn_ip3r) - Models extrapolating in the number of `IP3Rs` for a single volume, across all `IP3` values.
* [stochastic_simulations](stochastic_simulations) - Stochastic simulations for generating training data for both models.
The code is a mix of `Mathematica` notebooks and `C++`.
## Data
All data is stored using [DVC (data version control)](https://dvc.org). You need to install DVC, then you should be able to just run from this directory:
```
dvc pull
```
to get all the data.
**BE AWARE** that this can be a lot to download, particularly because of the [stochastic_simulations](stochastic_simulations) - otherwise you can look for the `.dvc` files in the various directory and load those individually.
## Figures
