This repo contains a Python and C++ implementation for following papers:

• Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation
• Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

by Xiaoyu Wang, Adrianne L. Jenner, Robert Salomone and Christopher Drovandi

The code to simulate the VCBM originates from Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model

Agent-based models are a class of models that can describe complicated phenomena by analysing the interactions between each agent. In cellular dynamics, treating each cell as an agent is a popular strategy. Prior research has sometimes ignored the geometries of agents and only considered their interactions. In the Voronoi cell-based model, the agent is modelled with Voronoi tesselation so that its shape is as accurate as reality.

In this work, we use sequential Monte Carlo - approximate Bayeisan computation (SMC-ABC) and sequential neural likelihood method to quantify the uncertainty of the model paramters.

The VCBM is compiling under the MacOS, and it works for MacOS and Linux users. If you are a Windows user, you need to re-compile the Model.cpp to a .so file and replace Model.so in VCBM folder to your .so file. The simulation for SMC-ABC requires huge amount of memory so parallel computing in personal device is not recommend. If you require to use parallel computing, you need to use HPC or similar to run the code.

If you find the code useful for your research, please consider citing

• For VCBM

    @article{jenner2022examining,
    title={Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model},
    author={Jenner, Adrianne and Kelly, Wayne and Dallaston, Michael and Araujo, Robyn and Parfitt, Isobelle and Steinitz, Dominic and Pooladvand, Pantea and Kim, Peter S and Wade, Samantha J and Vine, Kara L},
    journal={bioRxiv},
    year={2022},
    publisher={Cold Spring Harbor Laboratory}
    }

This work is an extension of

  @article{wang2022calibration,
  title={Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation},
  author={Wang, Xiaoyu and Jenner, Adrianne L and Salomone, Robert and Drovandi, Chris},
  journal={bioRxiv},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
  }

The SNL is powered by

  @article{tejero2020sbi,
  title={SBI--A toolkit for simulation-based inference},
  author={Tejero-Cantero, Alvaro and Boelts, Jan and Deistler, Michael and Lueckmann, Jan-Matthis and Durkan, Conor and Goncalves, Pedro J and Greenberg, David S and Macke, Jakob H},
  journal={arXiv preprint arXiv:2007.09114},
  year={2020}
  }