This repository collects selected code from my time working on a cross-disciplinary project at the Peter Doherty Institute and the University of Melbourne. The goal of this project was to determine a stochastic model for the motion of a particular population of T-cells residing in the liver. This project was supervised by Dr Lynette Beattie (Dept. Microbiology and Immunology), Professor Jonathan Manton (Dept. Electrical and Electronic Engineering) and Professor William Heath (Dept. Microbiology and Immunology), all at The University of Melbourne. Some data (not included in this repository) and advice was also provided by Professor Stefan Hoehme at Leipzig University. All code in this repository was authored by me.
Not all of the code written as part of this project has been included in this repository, and no data has been included.
sinusoid_mapping: this module is concerned with approximating the sinusoids using a connected network of curves, and extracting the displacements of cells moving along these curves (i.e. looking at the distance along the curve between two points rather than the straight line distance). It takes microscope imaging data after it has been analysed by Imaris (a program that works directly with the output of the microscope). Components can be used individually, but the main entry point issinusoid_mapping.processtrack_analysis: consists of code used to analyse the output of thesinusoid_mappingmodule. A major goal is to understand the distribution of the displacements of a cell moving along the sinusoids. This module contains many attempts to estimate the distribution of these displacements, as well as code for visualising this data.simulation_c: can be compiled into a simulation of cells moving through a liver. This requires a graphical (i.e. nodes and edges) representation of a liver which is not provided.simulation_py: achieves the same goal assimulation_c, but it is written in python. It is less performant but easier to change and is mostly used for prototyping.