image_based_particle_model

This repo is an image-based particle model Monte Carlo simulation for the article by Zhu and Loo $\textit{et. al.}$:

Receptor binding and tissue architecture explains the morphogen local-to-global diffusion coefficient transition

Shiwen Zhu¹, Yi Ting Loo ^2,3, Sapthaswaran Veerapathiran¹, Tricia Y. J. Loo^3,5, Bich Ngoc Tran¹, Cathleen Teh¹, Jun Zhong^1,5, Paul T. Matsudaira¹, Timothy E Saunders^1,3, and Thorsten Wohland^1,4,7.

¹NUS Centre for Bio-Imaging Science, Department of Biological Sciences, National University of Singapore, Singapore 117558 ²Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom ³Warwick Medical School, University of Warwick, Coventry CV4 7AL, United Kingdom ⁴Department of Chemistry, National University of Singapore, Singapore 117543 ⁵Mechanobiology Institute, National University of Singapore, Singapore 117411 ⁶Institute of Molecular and Cell Biology, A*STAR, Singapore 138673 ⁷Institute of Digital Molecular Analytics and Science, National University of Singapore, Singapore 636921

Recommended set-up for project

Create a virtual environment using:

conda create -n ImageDiff python=3.9.12
conda activate ImageDiff

Add conda-forge channel required for some libraries:

conda config --env --add channels conda-forge

Install package versions listed in requirements.txt:

conda install --file requirements.txt

Clone the repo:

git clone https://github.com/TimSaundersLab/image_based_particle_model.git

Folders description

binary_images contains the realistic images of extracellular spaces in the zebrafish brain tissue architecture. toy_data/toy_OT is a smaller stack of binary images to test the simulations. particle_models contains the main functions for FRAP and FCS image-based particle modelling. scripts contains python scripts with example usages of the functions.