Skip to main content

Cellular Automaton Model for Tumor Growth

Project description

TCAMpy

This is a single python module for a cellular automaton, modeling tumor growth. The user can set the parameters, create unique initial states, view and save statistics, save data and also use a streamlit dashboard as a graphical interface. Growth plots, histograms and animation is available for visualization in an easy to use way. (Online version available here, without writing a single line a code: Online dashboard.)

The theoretical background for this model is based on the work of Carlos A Valentim, José A Rabi and Sergio A David. I expanded this model by simulating an immune response during the growth of the tumor cells, as well as random mutations, influencing tumor survival. Other ideas, like nutrition may also be implemented in the future. There are also basic Machine Learning functions for dataset generation, model training, and predicting tumor size or confluence based on a new set of parameters.

Valentim CA, Rabi JA, David SA. Cellular-automaton model for tumor growth dynamics: Virtualization of different scenarios. Comput Biol Med. 2023 Feb;153:106481. doi: 10.1016/j.compbiomed.2022.106481. Epub 2022 Dec 28. PMID: 36587567. (url: https://pubmed.ncbi.nlm.nih.gov/36587567/)

This documentation provides detaild description on how to use the module, with example codes and links to example files. Detailed description is available in the uploaded paper: [to be uploaded]

Example Results

Visualization from running a single model. If enabled, the user recieves an image of the growth, a map for mutations, a line graph of cell numbers over time and a histogram of proliferation potentials.

Plot example

Animation is available to turn on when running the model as well. Visualizing a dataframe containing results from multiple model executions is also possible. A cell number line graph of the average numbers and a histogram of the average proliferation potentials can be plotted with standard deviations.

Averages plot example


Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tcampy-0.1.0.tar.gz (29.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tcampy-0.1.0-py3-none-any.whl (28.8 kB view details)

Uploaded Python 3

File details

Details for the file tcampy-0.1.0.tar.gz.

File metadata

  • Download URL: tcampy-0.1.0.tar.gz
  • Upload date:
  • Size: 29.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for tcampy-0.1.0.tar.gz
Algorithm Hash digest
SHA256 779d73fe2a1ee1a431ac126c843ec2e247e053ce4114d8e9c7c4506a2c0f06c1
MD5 fb0711d8b75a2e7ba2d4538b49e92c59
BLAKE2b-256 0d5851a138cfc3847adcb6fde12948bc216a5077a36654a5b2fec0138596e24e

See more details on using hashes here.

File details

Details for the file tcampy-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: tcampy-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 28.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for tcampy-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 eedc596a98b8c8092587bfe1d8ccfac46ebc1ec1a77b66c833fbae06fcc8a856
MD5 2c8135af7eb0cf090cca388d74024121
BLAKE2b-256 6ed462a921f6c3d58f29be201bbd91f4250b726ef685666825f85d864f87f1b5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page